country capacity building needs

GCP/RAS/162/JPN

The status of the forest resources assessment in the south-asian sub-region and the country capacity building needs

 

Proceedings of the

GCP/RAS/162/JPN Regional Workshop

 held in Dehradun, 8-12 June 1998

 

Table of Contents

Abbreviations

1. Introduction

2. Overview of workshop activities

3. Thematic deliberations

References

Appendix 1: Resource persons and participants

Appendix 2: Daywise session profiles of the workshop

Appendix 3: An outline for country report on FRA 2000

Appendix 4: Information for electronic networking

APPENDIX 5: Technical papers


 

Forests are crucial for the well-being of humanity. They provide foundations for life on earth through ecological functions, by regulating the climate and water resources, and by serving as habitats for plants and animals. Forests also furnish a wide range of essential goods such as wood, food, fodder and medicines, in addition to opportunities for recreation, spiritual renewal and other services.

Today, forests are under pressure from expanding human populations, which frequently leads to the conversion or degradation of forests into unsustainable forms of land use. When forests are lost or severely degraded, their capacity to function as regulators of the environment is also lost, increasing flood and erosion hazards, reducing soil fertility, and contributing to the loss of plant and animal life. As a result, the sustainable provision of goods and services from forests is jeopardized.

FAO, at the request of the member nations and the world community, regularly monitors the world's forests through the Forest Resources Assessment Programme. The next report, the Global Forest Resources Assessment 2000 (FRA 2000), will review the forest situation by the end of the millennium. FRA 2000 will include country-level information based on existing forest inventory data, regional investigations of land-cover change processes, and a number of global studies focusing on the interaction between people and forests. The FRA 2000 report will be made public and distributed on the world wide web in the year 2000.

The Forest Resources Assessment Programme is organized under the Forest Resources Division (FOR) at FAO headquarters in Rome. Contact persons are:

Robert Davis FRA Programme Coordinator robert.davis@fao.org

Peter Holmgren FRA Project Director peter.holmgren@fao.org

or use the e-mail address: fra@fao.org

Paper drafted by Kailash Govil & edited by Patrizia Pugliese

Abbreviations

BEF Biomass Expansion Factor
BV Biomass of inventoried volume
CATIE Centro Agronómico Tropical de Investigación y Ensemanza
Cirad Centre de coopération internationale en recherche agronomique pour le développement
EDC Eros Data Centre
FAO Food and Agricultural Organization of the United Nations
FORIS Forest Resources Information System
FRA Forest Resources Assessment
GIS Geographic Information System
SNU Sub National Unit(s)
UN-ECE United Nations Economic Commission for Europe
VOB Volume Over Bark
WD Wood Density
WCMC World Conservation Monitoring Centre

1. Introduction

1.1Purpose

FAO has established a regional project office (GCP/RAS/162/JPN) at Dehradun, UP, India in April 1998, to build institutional capacities of the South Asian countries in the field of Forest Resource Assessment and Planning. The first regional activity under this project was a workshop "The status of Forest Resource Assessment in the South Asian Sub-region and Country Capacity Building needs", June 8 to 12, 1998 at Forest Survey of India, Dehradun, UP, India.

This document provides not only the proceeding of the workshop but also the copies of the material exchanged during the deliberations. This document provides the core information of FRA, 1990, about six South Asian countries and Myanmar and recapitulates the relevant definitions of the core items to present complete picture and to serve as a reference document. The document also presents any new information relating to FRA 2000 provided by the participants.

1.2 Resource Organization

FAO, Forest Survey of India, Indira Gandhi National Forest Academy, Indian Institute of Remote Sensing and French Institute of Pondicherry provided the technical resource persons and material.

Country Capacity Building "CCB" project (GCP/RAS/162/JPN) provided total financial support for the workshop. The project provided money to respective FAOR to meet travel and other expenses of the participants. The project also provided $9,000 to FSI-Dehradun to cover the local transportation, lodging and boarding, resource material, and remuneration to resource persons.

1.3 Regional Representation

Six countries (Bangladesh, Bhutan, India, Nepal, Sri Lanka and Myanmar) participated in this regional workshop. Each country sent two delegates to participate in this workshop. Delegates from Pakistan could not attend it due to some political constraints. The participants from Myanmar desired that, in future, Myanmar should be included in this group of South Asian countries for deliberations on forestry issues. This group of twelve participants included foresters and ministry officials dealing with forestry subjects in their countries.

1.4The Proceeding

The proceeding is organized in three sections. First section has the main report. The second section consists of annexes. The third section reproduces the technical papers or transparencies presented and provided by the participants during the workshop.

This main section of the proceeding is divided in two parts. The first part provides an overview and the second part concentrates on the thematic sessions at the workshop.

2. Overview of workshop activities

The workshop was organized in eleven sessions comprising one each of inaugural, foundation, and concluding session and eight working sessions on core items of FRA.

The participants deliberated on technical issues in the eight working sessions over the period of 5 days. Each session focussed on selected items of the "core information." The workshop was participative and consultative in nature and provided excellent opportunities to develop the capacities and enhance co-operation in field of forest resource assessment amongst the countries of the region.

2.1 Inaugural session

Dr. B. N. Gupta, Director General, ICFRE, the Chief Guest, formally inaugurated the five day workshop. While welcoming the delegates in his keynote address, he explained the unique socio-economic importance of forest resources at micro levels in the South Asian Region. He stressed on the need for capacity building of institutions dealing with forest resources and planning. He specially emphasized the need for enhancing the capacities of local communities for increasing their efficiency in participation for planning, protection and management of local forest resources.

Mr. A. Marzoli, FAO welcomed the participants and deliberated on the FAO's thinking on GFRA 2000 and the need for enhancing the capacities of the countries in order to decentralize collection, storage, retrieval and utilization of information about forest resources. He outlined the objectives of the workshop; defining strategies for CCB, establishing mechanism for sharing information about FRA 2000 and providing scenarios of status of forest resources in respective countries.

Mr. P. C. Tyagi, Joint Director-FSI and the course director informed about the agenda, schedule and organization of the workshop. Dr. D. Pandey, Director FSI introduced the audience to the activities of FSI, genesis of the workshop and the need for supplying quality information for the GFRA 2000. Dr. K. C. Govil, Regional Project Coordinator-FAO, explained the purpose of the workshop and made a wish for very productive deliberations. Mrs. Ranjana Gupta, FSI, proposed the vote of thanks for the inaugural session. 2.2 Foundation session

Revelation of the expectation from the workshop organized by the FAO and the views of the participants, coupled with two baseline presentations by FAO staff, laid a well-structured foundation for the workshop.

2.2.1 Expectations

The FAO representatives expected that during the workshop, the data needs of FRA 2000 will become more clear to the participants and that FAO will have a better appreciation of the problems faced by different countries with respect to the collection, compilation and sharing of information for FRA 2000. They also expected that the workshop would help in developing strategic activities under CCB to address the aforesaid problems through training, studies, workshop and equipment.

The participants from Bangladesh, Bhutan and Nepal expected that after the workshop they will have a better understanding of FRA 2000 needs, objectives, methodology and management and will be in a better position to contribute to the development of FRA 2000.

The Indian representatives expected the emergence of a clear perspective about the data requirements for planning and assessment at the ground level and the national level and the standardization in the format of collection and storage of the data.

The Sri Lankan representatives expressed their desire to understand their role and to learn more about the experience of different countries in developing proper strategies to support this project.

The Myanmar delegates expressed their hope that this workshop will improve exchange of information, technology, and support from FAO and other institutions like ICIMOD.

2.2.2 Forest assessment activities

Following sub-section provides a brief description on the forest assessment activities presented by the participants of the six countries.

Bangladesh

In 1980, the forest department with UNDP/FAO assistance conducted a detailed inventory of forest areas including a countrywide inventory of village forests. Recently (1995-97), the forest department completed a massive inventory program in hill and coastal forest areas under a loan agreement with International Development Agency. This is a unique inventory in the sense that a socio-economic survey has also been conducted along with the forest inventory to understand the behavioral pattern of the users. The forest inventory design is based on systematic sampling with grid intervals of 40x40 feet in combination with smaller grids 20 or 10 feet, to cover the required number of cluster plots. Forest statistics are generated with continuous resource change assessment. A Resource Information Management System (RIMS) is in place which uses SPOT data for generating signatures of different kinds of forest vegetation.

Bhutan

The PISFR-India designed and conducted first inventory for Bhutan in 1976-1981. Bhutan has 72% forest cover (with 8% scrub forest) amounting to 2.9 million hectares. About 39% of total "Reserve Forest" is considered operable. Forests are managed through 39 Forest management units. Social forestry and Agro forestry divisions raise and monitor the plantations. Currently about 27% of land is under "Protected Forest area" category.

India

Forest Survey of India is doing assessment utilizing satellite imageries, aerial photographs and ground inventories. Vegetation maps on the scale of 1:250000 are prepared for the whole country on a 2-year cycle. Thematic maps using aerial photographs on 1:50000 scale are being produced on a 10-year cycle. Approximately, 70% of India has been covered on thematic maps. Similarly ground inventory has covered about 80% of the area. State of Forest Report is published by FSI on a two-year cycle. Forest cover assessment is done partially (28%) in digital mode and rest (72%) by visual interpretation. FSI has adopted a mandate to digitally assess the vegetation cover of the whole of India in the near future.

Myanmar

A FAO/UNDP project has helped Myanmar to assess forest resources through use of satellite data from LANDSAT in 1970. The National Forest Survey and Inventory Project (1981-91), funded by UNDP/FAO, has provided reliable information on forest resources and aerial photographs for about 90% of the area. During this period, information has been collected at three levels; pre-investment, reconnaissance and management covering 12 million ha of forest land. Since 1991, Myanmar has been conducting forest inventory every year, covering 2 million ha using remote sensing and GIS technology. The available information is used to generate maps of 1:25000 and 1:50000 scale. Forest cover assessment of 1980 used Landsat imagery (scale 1:1000000). The 1989 forest cover estimation utilized Landsat TM data (scale 1:500000). Interpretation of the collected information is done visually.

Nepal

Nepal has established Forest resource Information System Program (FRISP) and is currently carrying out hierarchical grid inventory at the regional and the district level. Inventory data on forest type, crown diameter type and density class at both the levels is being collected. Recent maps based on aerial photographs were produced in 1996. Nepal faces an uphill task in forest resource assessment and inventory due to difficult terrain, non- availability and high cost of aerial photographs, problems in adopting new technologies, and untrained manpower.

Sri Lanka

First National level Inventory was carried out in 1956 with the Swiss and the Canadian help. Next inventory was done in 1983 with the help of a FAO/UNDP project. The total forest cover with closed canopy is 23.9%, sparse forest is 7% and forest plantation is 2%. Their resource base consists of natural forests, forest plantations, and non-forest tree resources from home garden and the rubber and the coconut plantations. Forest Cover Assessment Maps using LANDSAT imagery were produced in 1991-92. Indicatory inventories of non-forest land and detailed periodic inventories of plantation are carried out for assessing resources and their potential of supply. Forest Resource Assessment is done using the aerial photographs of scale 1:20000 for natural forest and 1:10000 and 1:20000 for plantations. Ground survey is used for management planning. Problems are faced in the acquisition of data, manpower and training.

2.2.3 Foundation papers

Two presentations by FAO staff members, Mr. A. Marzoli, on "Global Forest Resources Assessment: Introduction to the project Objectives, Activities and Definitions", and M. Lorenzini, on "FAO strategy for Country Capacity Building" laid down an excellent foundation for workshop deliberations.

Mr. Marzoli explained the definitions, intent and responsibilities regarding the data for FRA 2000, as decided in Expert Consultation at Kotka III in Finland and about the FAO responsibility for collecting, compiling and processing of information of the developing countries.

Mr. Lorenzini presented the concept of Country Capacity Building "CCB". The CCB expects the countries to provide information to FAO on the status of their forest resources, identification of problems, if any, related to the development of baseline statistical and spatial geo-referenced databases, and utility of the data for strategic integrated planning for forest resources. At the same time, countries can expect that FAO, through CCB, will support countries in solving their problems in the acquisition, homogenization and integration of data, and strategic planning, including access to the data bases available with the FAO.

2.3 Working sessions

The following table provides an overview of the coverage of "core items" during the eight working sessions.

Table 1. Coverage of the Core items of FRA 2000 at June 1998 Workshop

Item of Core Information Topic of presentation Date and Working Session
1. Area of forest and other wooded land Presentations on the overall status of Forest Resources Assessment at National level by representatives of each country

Sri Lanka: Mr. P.M.A de Silva

India: Mr. Davendra Pandey

Bangladesh: Mr. Ishtiaq Uddin Ahmed

Bhutan: Mr. Pasang Wangchen Norbu

Myanmar: Mr. U Shwe kyaw

Nepal: Mr.Sharad Rai

June 8th, Afternoon
  Problem related to the assessment of forest area: the FAO perspective: Mr. A Marzoli, FAO June 9th, Forenoon
  Problems related to the assessment of Plantation Areas:

Dr. D Pandey, FSI

June 9th, Forenoon
  Estimation of Forest Cover for Standard reference year:

Mr. A Marzoli, FAO

June 9th, Afternoon
  Assessment of Forest Area by natural categories:

Mr. P C Tyagi, FSI

June 10th, Forenoon
2. Protection status Status of Protected Areas in India: Mr. P C Tyagi, FSI June 10th, Forenoon
3. Forest ownership State of Forest Ownership in India:

Mr. R K Upadhyay, IGNFA

June 9th, Forenoon
4. Forest by ecological zone Status of Forest Resources of Hindu Kush Himalayas:

Dr. A K Myint

June 10th, Afternoon
5. Wood supply potential Forest availability for Wood supply: Naturalness and Accessibility: Mr. M Lorenzini, FAO June 10th, Afternoon
6. Changes over time Forest Cover Change Assessment: FSI methodology:

Mr. S Tripathi and Mr. P K Pathak, FSI

June 11th, Forenoon
  Area Production Model: example of a case study for one district of Andhra Pradesh, India: Mr. A Marzoli, FAO June 11th, Forenoon
  Monitoring of Forest Resources at District Level:

Mr. A Marzoli, FAO

June 11th, Afternoon
  The country perspective-India's Contributions:

Grazing: Mrs. Ranjana Gupta, FSI

June 9th, Forenoon
7. Volume and biomass Growing Stock Estimates: the country perspective-FSI Contribution: Mr. S K Chakrabarti, FSI June 9th, Afternoon
  Estimating actual Forest Volume using Forest Inventory and Climatological data: Mr. M Lorenzini, FAO June 9th, Afternoon
  Productivity Estimation Models: Dr. D Pandey, FSI June11th, Afternoon
8. Felling and removals Not deliberated  
9. Forest fires The country perspective-India's Contributions:

Forest Fire: Mrs. Ranjana Gupta, FSI

June 9th, Forenoon
10. Non Wood Goods and services The country perspective-India's Contributions:

Non-wood forest products: Mrs. Ranjana Gupta, FSI

June 9th, Forenoon

The workshop was enriched by the deliberations on items, in addition to the "core", required by FRA 2000. The additional issues included Shifting Cultivation, Biodiversity, Networking and MIS. The following table provides an overview of such deliberations.

Table 2: Coverage of Additional Items during June 1998 workshop.

Item of Core Information Topic of presentation Date and Session
Additional parameters    
Shifting Cultivation Problems related to the assessment of Forest Area: The country perspective on Shifting Cultivation: Dr. V N Pandey, FSI June 9th, Forenoon
Biodiversity Biodiversity Assessment: the FAO perspective

Mr. Lorenzini, FAO

June 10th, Forenoon
  Biodiversity Characterization at Landscape Level using satellite data and GIS: Dr. P S Roy, IIRS June 10th, Forenoon
  Biodiversity Assessment: the regional perspective

Mrs. C Elouard, French Institute of Pondicherry

June 11th, Forenoon
Additional items    
Networking CCB and Networking: Dr. K.C.Govil, FAO June 10th, Afternoon
Land Use Planning An Integrated Approach to planning and Management of Land Resources as promoted by FAO: Mrs. Andrea Kutter June 11th, Afternoon
MIS User perspectives: Mr. J S Grewal June 11th, Afternoon

2.3 Concluding session

This session reviewed the deliberations of the last four days and wrapped up the workshop. Each delegation was requested to provide a feed back on the utility of the workshop and the activities that, according to them, are necessary to enhance the capacities of forestry institutions in their countries. All delegates valued the workshop and requested for immediate provision of a website, internet, email facilities to them in addition to financial and technical help for FAO to develop the material for FRA 2000. All the members were requested to expedite the submission of information for FRA 2000 as explained in the workshop.

Bangladesh delegates considered this workshop very useful for sharing knowledge and experience amongst the South Asian countries. They emphasized that the strength and the weakness of each country are different, therefore, each country will need different support from FAO under this project. They wanted financial and technical help for internet and access to web-site to prepare material for FRA 2000 and forest planning.

Bhutan representatives considered the workshop a great source of education and exposure. They also felt the need for technical and financial help from the FAO to provide information for FRA 2000.

The Indian delegates supported the workshop for improving the efficiency of the efforts in FRA 2000 and wanted that a web-site be established and internet facilities extended to them to share information and problems. They wanted quicker switching over to the digital mode of analysis and continuous field inventory methodology. The Indian delegates wanted that software like Area Production Model should be made available to all the institutions.

The Myanmar delegates were interested in training to enhance their capacities and in internet and email facilities to share information. They requested the FAO to provide financial and technical help. While appreciating the utility of the workshop, the Nepalese delegates informed that facilities, funds, manpower, and infrastructural facilities were not sufficient in Nepal to serve FRA 2000 and needed FAO support.

The representatives from Sri-Lanka highlighted the utility of the workshop and emphasized on more access to relevant and useful data with forestry-related national, regional and global institutions. They requested for an early establishment of a web-site and extension of Internet facilities among all the member countries.

3. Thematic deliberations

This part provides description of the technical deliberations on "core" and "additional" items of FRA 2000 in various working sessions. Annex "A" contains copies of papers, transparencies or other material provided by the participants in the workshop.

Forest Resource Assessment (Kotka III) identifies ten "core items" on which information is required for the assessment; Area of forest, Protection Status, Forest Ownership, Forest by Ecological zones, Wood Supply Potential, Changes over time, Volume and Biomass, Felling and Removals, Forest Fires, and Non-wood Goods and Services. The "additional" items of FRA 2000, deliberated at the workshop include shifting cultivation, biodiversity, networking and MIS.

This part also contains new information, if any provided by the participants over and above the information included in FRA 1990. Such new information was only provided by India (SFR, 1997), Sri-Lanka (OSFSMP,1995), Bangladesh (Ahmad, 1998) and Myanmar (SFAR, 1998). To complete the picture on each of the core item, this section provides past information contained in FRA 1990 and current definition of related terms for FRA 2000. This helps to identify gaps in the information for FRA 2000.

3.1 Area of Forest and Other Wooded Land

The delegates shared information on the present status of forest resource assessment in their countries. India (SFR, 1997), Sri-Lanka (OSFSMP, 1995), Bangladesh (Ahmad, 1998) and Myanmar (SFAR, 1998), and some of them provided new information over and above what is contained in the FRA 1990. India presented the recent information on forest cover and growing stock assessment contained in State of Forest Resources 1997, that is based on 1993 to 1995 satellite data. Myanmar provided the information on the classification of forest cover types according to the 1995 assessment based on TM digital data from LANDSAT. Similarly, Sri Lanka provided new information on the area of forests based on the 1992 assessment.

Mr. A Marzoli, in his baseline presentations titled "Problem related to the assessment of forest area" and "Estimating Forest Cover for Standard Reference Years", explained in detail about the establishment of a computerized database and development of an adjustment function to compute forest cover for the standard reference years. The data collected on forest cover from different countries, have different reference years and therefore, need to be brought up to the standard years, for example, to the year 1990 and year 2000 for GFRA 2000, to facilitate aggregation, analysis and reporting. The forest area adjustment function or model, which is used to accomplish this difficult task, is based on the correlation between the forest cover and ancillary variables, like, population density over time.

Three main aims of this model-based approach are:

(i) to adjust the sub-national units inventory data to standard reference years,

(ii) to provide a methodology to correctly expand, to all the units, the forest area change data, and

(iii) to develop an understanding of the causes and the dynamics of change in forest area.

This function or a "General" model, that is applicable to a wide range of localities, is computed through a regression analysis using forest change database, together with population time series and ecological zone maps. This analysis during FRA 1990 has provided a good understanding of the relationship between explanatory variable and the forest cover. The analysis indicates a logarithmic relationship along a "S" shaped curve between the population density and the forest cover. The ecological conditions mediate this relationship across their boundaries.

The latest available forest inventory is used as a baseline for each unit, to estimate the forest cover and the changes in forest cover during the standard reference years (for example 1980 and 1990 for FRA 1990), under any one of the following conditions:

(i) Reliable multi-date inventories are available,

(ii) Reliable single-date inventory is available, or

(iii) No reliable inventory is available

If reliable multi-date inventory is available then the model utility is limited to an adjustment function. With single-date inventory, the model is used in its general form. When no reliable inventory is available, then the baseline forest cover areas are extracted from the calibrated vegetation maps existing in the GIS database of the project. The estimated values at sub-national level are aggregated at national, regional and global levels.

Dr. D Pandey in his paper "Assessment of Forest Plantation Areas", discussed about the problems faced in the assessment of plantation areas. One of the main problems is in assessment of the net area of the plantation. Dr. Pandey emphasized on the need of developing a methodology for inventory of plantations, which also provides the necessary information to develop reduction factors for estimating the net areas from gross areas of forest plantations. He also presented values of such reduction factors at global, regional and country levels. These factors vary from 0.47 9(Laos) to 0.92 (Nicaragua).

Dr. Pandey listed eight main problems in assessment of the plantation area in India; ambiguous definition, non-deletion of failed or felled areas, cumulative reporting, non maintenance of proper plantation records, over reporting, corrupt practices, lack of proper monitoring, and difficulty in the assessment of the area through remote sensing. The last problem is technological in nature because linear features such as roadside plantations of size less than the resolution scale could neither be recorded nor delineated.

Mr. P C Tyagi in his presentation on "Forest by Naturalness", discussed about different levels of classification under FRA 2000 guidelines. He also apprised the participants about various difficulties in collecting the information at these levels for FRA 2000. Most of the forests in India have been worked for timber and only few forest tracts may be in their natural state and these are located in remote and difficult areas. High cost of exploitation may have checked their commercial use, but local population or tribals living close to the forests may have used them for meeting their domestic needs. When requested by GOI in 1981, very few states could identify patches of such forests. Therefore, considering the difficulties in identifying natural forest tracts in India, Mr. Tyagi suggested use of special studies to assess the forest and other wooded land according to naturalness categories.

3.1.1 Recapitulation of Terms and Definitions

The deliberation on "Area" was more focussed around the definition, methodologies and difficulties in providing information for FRA 2000. The following lines briefly recapitulate the relevant definitions:

Total area: Total area of country, including inland water bodies but excluding offshore territorial waters.

Inland water: Area occupied by major rivers, lakes and reservoirs.

Land Area: Total area excluding inland water. It may consist of Forest or Non-Forest lands.

Forest: Land with tree cover of more than 10%, area of more than 0.5 hectares and minimum height of 5 meters at

maturity but excludes land predominantly under agriculture. The forest area is divided into three categories:

Undisturbed by man: These are classified into "Closed forest" and "Open forest."

Closed forest: It consists of areas with high proportion of area covered by trees and which do not have continuous dense grass layer. The "closed forest" category is further classified into three categories; Broad-leaved, Coniferous, Bamboo/Palm.

Open forest: These are forest tracts with tree cover of at least 10 percent and generally have continuous grass layer. The forests under belong to two main groups;

Broad-leaved and Coniferous.

Plantations: Forest stands established by planting or /and seeding in the process of afforestation and reforestation.

Semi-Natural: Forest or other wooded land, which is neither "forest/other wooded land undisturbed by man" nor "plantation"

Non-Forest: It includes "other wooded land" and the rest "other land" defined below,

Other wooded land: Land with any of the following three attributes for crown cover and height,

Crown cover Height at maturity
More than 10% Trees with less than 5 meters height
More than 10% Shrub or bush cover
Between 5 to 10% Tree with height reaching 5 m

This category contains shrubs and fallow lands. The shrub is defined as the woody vegetation dominated by shrubs, with height between 0.5 m to 5 m and without a definite crown. The forest fallow is defined as complexes of woody vegetation deriving from the clearing of natural forest for shifting agriculture.

Other land: Land that is not classified as "forest" or as "other wooded land."

3.1.2 Information on "Forest Area" with FAO and that provided by the participants

Bangladesh

Bangladesh is in the process of publishing new information of 1997 therefore participants shared following FRA 1990 information only on the area of forest and other wooded land.

Table 3. Area of Forest and Other wooded land in Bangladesh (FRA, 1990)

Country Land Area

1000 ha

Forest & Other wooded land

1000 ha

Forest Other Wooded Land

1000 ha

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
Bangladesh 13017 1472 1004 8 769 235 468

Source: FAO, 1995.

Bhutan

Bhutan has completed its new assessment but participants could share information contained in FRA 1990 only.

Table 4. Area of Forest and Other wooded land in Bhutan (FRA, 1990)

Country Land Area

1000 ha

Forest & Other wooded land

1000 ha

Forest Other Wooded Land

1000 ha

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
Bhutan 4700 3168 2813 60 2809 4 355

Source: FAO, 1995.

India

FSI provided a copy of the "Status of Forest Resources "SFR", 1997" to each of the participants which contains new information over and above the FRA 1990 report. Following provides details of information of "Area" available in FRA 1990 and SFR 1997.

Table 5: Area of Forest and Other wooded land in India (FRA, 1990)

Country Land Area

1000 ha

Forest & Other wooded land

1000 ha

Forest Other Wooded Land

1000 ha

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
India 297319 82648 64959 22 51729 13230 17689

Source: FAO, 1995.

Table 6: New information on Forest and Non Forest Cover in India (SFR, 1997)

Country Total Geographic area

1000 ha

Forest Non Forest

1000 ha

Scrub

1000 ha

    Total

1000 ha

% of Geographical area Dense Forest

1000 ha

Open Forest

1000 ha

Mangrove

1000 ha

   
India 328726.3 63339.7 19.27 36726 26131 482.7 259665.5 5721.1

Source: SFR, 1997. (FSI)

Table 6. Extent of Forest area undisturbed by man in India, 1981

Type of Forest Area (sq. km)
Forest undisturbed by man 19948.28

From 1991-92, FSI has also taken up inventory of trees growing outside conventional forest area in the country. Such plantations have been classified according to their locational characteristics like Farm forestry, Village woodlot, Block Plantation, Roadside, Pond, Railside and Canalside Plantations, etc. An inventory for trees planted outside forest areas has been completed by FSI for one of the states (Haryana) in India (SFR 1997).

Myanmar

Table 7. Area of Forest and Other wooded land in Myanmar (FRA, 1990)

Country Land Area

1000 ha

Forest & Other wooded land (1000 ha) Forest Other Wooded Land

(1000 ha)

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
Myanmar 65797 49774 29091 44 28856 235 20683

Source: FAO, 1995.

Nepal

Table 8. Area of Forest and Other wooded land in Nepal (FRA 1990)

Country Land Area

1000 ha

Forest & Other wooded land

1000 ha

Forest Other Wooded Land

1000 ha

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
Nepal 13680 5751 5079 37 5023 56 672

Source: FAO, 1995.

New information is under process. The forest cover of Nepal, on the basis of 1944 information from NOAA satellite data, reveals that the mixed and coniferous forests exist over 41.6% of the area of the country, excluding 14.5% of degraded forests. (Myint, 1998)

Sri Lanka

Sri-Lanka provided 1992 information over and above FRA 1990 for the benefit of the participants.

Table 9. Area of Forest and Other wooded land in Sri-Lanka (FRA, 1990)

Country Land Area

1000 ha

Forest & Other wooded land

1000 ha

Forest Other Wooded Land

1000 ha

      Total

1000 ha

% of land area Natural

1000 ha

Plantation

1000 ha

 
Sri Lanka 6463 3998 1885 29 1746 139 2113

Source: FAO, 1995.

Based on the new information, the total land area including all inland water bodies is 6,616,628 ha. The area of natural closed canopy forest is 1,582,756 ha and the sparse and open forest occupies a total of 463,842 ha. The total area under mature and well-established forest plantations is 72,340 ha (Legg and Jewell, 1998).

Table 10. Areas of Natural Forest in Sri- Lanka in 1992

Country Land Area Forest Other Wooded Land
  1000 ha Total

1000 ha

% of land area Natural Forests Plantation

1000 ha

1000 ha
        Closed-canopy in 1000 ha Sparse and Open forests 1000 ha    
Sri Lanka 6616.628 2118.938 32.04 1,582.756 463.842 72.340  

Source: Legg and Jewell, 1998.

Table 11. Areas of Forest Plantations in Sri-Lanka, 1992

Forest Plantation Area (ha)
Conifers 16,816
Eucalyptus 16,201
Teak 35,268
Mahogany enriched forest 4,055

Source: Legg and Jewell, 1998.

In 1992, Home gardens covered 860,000 ha and rubber and coconut plantations together covered about 500,000 ha. (OSFSMP, 1995).

3.2 Protection Status

Mr. P C Tyagi motivated the participants to deliberate on the issue of "Protection status" in their countries, through his paper on "Protected Areas in India". He explained the important biological and practical parameters necessary in planning protected areas, available information on the status and statistics of the existing protected areas. He also dealt with the bio-geographical classification, developed by the Wildlife Institute of India, for conservation planning in India. During the workshop, only India and Sri-Lanka provided some new information on this item of FRA 2000.

3.2.1 Recapitulation of Terms and Definitions

Information is required for protection status of the "Forest" and "Other wooded lands" by following six IUCN categories under two groups; (a) Strictly Protected Areas (Categories I and II), and (b) Protected Areas with integrated management (Categories III to VI).

(A) Strictly Protected Areas

I. Strict Nature Reserve/Wilderness Area: Protected area managed for science or wilderness protection.

II. National Parks: Protected area managed mainly for ecosystem protection and recreation.

(B) Protected Areas with integrated managements

III. Natural Monument: Protected area managed mainly for conservation of specific natural features.

IV. Habitat / Species Management Area: Protected area managed mainly for conservation through management intervention.

V. Protected Landscape/Seascape: Protected area managed mainly for landscape /seascape conservation and recreation.

VI. Managed Resource Production Area: Protection area managed mainly for sustainable use of natural ecosystem.

3.2.2 Information on "Protection Status" with FAO and that provided by the participants.

Bangladesh

Information not available

Bhutan

Information not available

India

Analysis of protected areas is done within the boundaries of the bio-geographic zones and provinces. Information on biome within each protected area or within each bio-geographic unit is not readily available. As an alternative, presence or absence data was used for analysis of biome coverage. The data analysis shows that the PA's are not distributed uniformly across the states or across the bio-geographic Zones or provinces of the country.

In India, the conservation status of forests is directly linked to the legal category of protection under Forest and Wildlife Acts.

The Wildlife Protection Act, 1972, recognizes four categories of protected areas; National parks, Sanctuaries, Game Reserves, and Closed Areas. The IUCN categories I and II are almost equivalent to the National parks and categories IV and VI of IUCN cover the Sanctuaries.

Accordingly, India has a network of 85 National parks covering 36,619 Sq. Km of area conforming to the group comprising of category I and II of IUCN. Further, 450 Wildlife sanctuaries in India are spread over 113,169 Sq. Km and come under the group consisting of categories IV and V of IUCN. National Parks and Sanctuaries, as a whole, cover some 149,788 sq. km or 4.70% of the countries land surface. (Tyagi, 1998).

Table 12. Protected Areas in India by IUCN categories

Categories I and II

(Sq.km)

Categories IV and V

(Sq.km)

Total

(Sq.km)

36,619 113,169 149,788

Source: Tyagi, 1998.

Myanmar

At present, 18 Wild life Sanctuaries and 3 National parks totaling 7,731 Sq.km or 1.14% of the total area of the country have been established.

Nepal

Information not available

Sri Lanka

In Sri-Lanka, the total protected area is about 9,300 Sq. km., which is about 14% of the total land area. (OSFSMP, 1995).

Table 13. Protected areas in Sri-Lanka

Control and Type of Areas Number and Area

No. Area (Sq.km)

A. Area with Forest department    
Total of all conservation areas Not available 1,111
SubTotal with Forest Dept.   1,111
B. Area with Dept. of Wildlife Conservation    
National Park 12 4,624
National Reserve 3 334
Sanctuary 52 2,841
Strict Natural Reserve 3 316
SubTotal with DWLC 70 8,115
Grand Total   9,226

Source: FAO- APFSOS/WP/16, 1997

3.3 Forest Ownership

Mr. R K Upadhyay initiated the discussion on "Ownership", through presenting his paper titled "Forest Ownership in India". He focused on statutory and legal nature of forest ownership in India. All property in India is vested in the Constitution of India, which respects all types of ownership for forests, ranging from private to community, to corporate and to the government. He provided information, which may help to classify ownership of forests on the basis of rights, concession and possession (legal transfer or encroachment). The Paper provides details on statewise forest cover in India, which has been notified as forest, forest owned by departments other than forests, corporate bodies, community, and private individuals. The Paper also deals with the system of annual reconciliation of ownership and possession of forestland in one of the states (Tamil Nadu) of India.

3.3.1 Recapitulation of Terms and Definitions

The following lines provide the agreed terms and their definitions (Kotka III) for the above core information item, upon which the information is needed for GFRA 2000.

Public Ownership: Belonging to State or Other public bodies. It is categorized in following three groups:

State Ownership: Owned by National, State and Regional governments or by Government owned

Corporations; Crown forests

Owned by other public institutions: Belonging to cities, municipalities, villages and communities. It includes all publicly owned forest and other wooded lands that are not under "State ownership"

Owned by indigenous and tribal people: Owned by indigenous and tribal councils in independent countries.

Private ownership: It is, also, categorized in following three types of ownership:

Individual ownership: By individuals and families

Owned by forest industries: By private forest or wood processing industries

Owned by other private institutions: By private corporations, co-operatives or institutions.

3.3.2 Information on "Forest Ownership" with FAO and that provided by the participants.

Bangladesh

No information is available.

Bhutan

No information is available.

India

According to 1988 National Forest Policy, forest lands or lands with tree cover are treated as a national asset and the Constitution Of India safeguards them for providing sustained benefits to the entire community. The level of protection provided to them varies with their legal status or differentiation. First major differentiation is on the basis of control by the government forest department and others. The forests with forest departments are further legally differentiated into reserved, protected and unclassed forests, which define the degree of their access and use, by local communities (Table 14).

Table 14. Forests with Government Forest Departments

Reserved forest

(Sq.km)

Protected forest

(Sq.km)

Unclassed forest

(Sq.km)

416,516 223,309 125,385

Source: Upadhyay, 1997.

The forests, other than those with government forest department, are further classified on the basis of ownership and control (other government departments, public corporate bodies and communities, and private individuals), as summarized below (Table 15).

Table 15. Forest not with Government Forest Departments

Revenue Depts.

(Sq.km)

Corporate / Community ownership (Sq.km) Private ownership

(Sq.km)

15187.00 8811.73 3031.88

Source: Upadhyay, 1998.

Myanmar

Information was not available.

Nepal

Information was not available.

Sri Lanka

Information was not available.

3.4 Forest By Ecological Zone

Mr. A K Myint laid down the basis for deliberations on "Ecological zoning" of forests through his paper titled "Forest Resources of Hindu Kush Himalayas (HKH)" covering regional view of the part of the forests of Pakistan, India, Nepal, Myanmar, China, Bhutan, Bangladesh, and Afghanistan. Following Kawosa (1988), he divides the Himalayan forests of India, Bhutan and Bangladesh into two zones; Eastern and Western. Major forest types of the Eastern Himalayas zone are further differentiated as Tropical Evergreen forests, Sub-Tropical forests, Eastern Temperate forests, Sub-Alpine forests, Alpine vegetation, Stony deserts. The Western Himalayas zone is similarly described as Tropical Deciduous forests, Sub-Tropical Pine forests, Himalayan Moist Temperate forests, Himalayan Dry Temperate forests, Sub-Alpine forests, or Alpine Pastures and Scrub.

The forests in HKH region roughly cover about 24% of the land. Forest cover estimates are available individually for countries as a whole in the HKH region from many sources like FAO, WCMC, and WRI. However, it is difficult to get a precise picture of forest cover in this region due to non-availability of even district level estimates for the areas falling in the HKH region. International co-operation can help in this field by providing necessary information for such study.

3.4.1 Recapitulation of Terms and Definitions

Kotka III deals with ecological zoning through following terms and classification. Forests are divided in two main domains:

(i) Tropical and Sub-tropical, and

(ii) Temperate and Boreal.

The first domain "Tropical and Sub-tropical" consists of following seven ecological zones:

Tropical wet and very moist zone,

Tropical moist zone,

Tropical dry zone,

Tropical very dry zone,

Desert (Hot and Cold) zone,

Tropical and sub-tropical hill and montane zone, hot and moist, and

Tropical and sub-tropical hill and montane zone dry.

The second domain "Temperate and Boreal" comprises of following five ecological zones:

Dry (Mediterranean type) temperate zone,

Temperate zone,

Temperate boreal hill and montane zone,

Boreal zone, and

Tundra zone.

3.4.2 Information on "Ecological Zoning" with FAO and that provided by the participants.

Bangladesh

Table 16. Land area and Forest cover by Ecological zone in Bangladesh (FRA 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
4606 12 8410 2 0 0 0 0 0 0 0 0

Source: FAO, 1993

Bhutan

Table 17. Land area and Forest cover by Ecological zone in Bhutan (FRA, 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
352 50 0 0 0 0 0 0 1299 31 3049 73

Source: FAO, 1993

India

Table 18. Land area and Forest cover by Ecological zone in India (FRA 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
18425 45 40474 17 178002 15 18519 0 24317 5 17603 51

Source: FAO, 1993

Myanmar

Table 19. Land area and Forest cover by Ecological zone in Myanmar (FRA 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
22130 55 22087 47 5969 6 0 0 248 17 15363 39

Source: FAO, 1993

The Japanese Forestry Technical Association (JAFTA) using Landsat TM digital data in 1995, has identified following forest cover types:

Table 20. Forest Cover Types in Myanmar

Sl.No Forest Cover Types
1 Evergreen
2 Evergreen Forest/open
3 Moist Upper Mixed Deciduous Forest
4 Moist Upper Mixed Deciduous Forest/open
5 Dry Upper Mixed Deciduous Forest
6 Deciduous Dipterocarp Forest
7 Pine Forests
8 Hill Forests
9 Dry Forests
10 Bamboo Forests
11 Mangrove Forests
12 Open Mangrove Forests
13 Scrubland
14 Grass/ Agricultural land
15 Orchards/ other trees
16 Bare land
17 Shifting cultivation
18 Water surfaces

Source: SFAR, 1998.

Myint (1998) recognizes the following 10 standard forest types in Myanmar:

Tidal, beach and dune and swamp forests,

Tropical evergreen forests,

Tropical semi-evergreen of Riverain-evergreen forests,

Mixed deciduous forests,

Lower mixed deciduous,

Moist upper mixed deciduous forests,

Dry upper mixed deciduous forest,

Deciduous Dipterocarp forests,

Dry and thorny forests, and

Sub-tropical hill and alpine forests.

Nepal

Table 21. Land area and Forest cover by Ecological zone in Nepal (FRA 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
1847 33 3010 43 165 22 0 0 3811 19 4847 49

Source: FAO, 1993

The 1997 national forest inventory (FRSC/FINNIDA 1997) differentiates the forests of Nepal by dominant species into following 10 forest types.

Table 22. Forest Types in Nepal

Sal - Shorea robusta forests
KS/SK - Acacia catch and Dalbergia sisso forests
TMH - Tropical mixed hardwoods forests
UMH - Upper mixed hardwoods forests
LMH - Lower mixed hardwoods forests
Q - (Oak) Quercus forests
B/Bu - (Birch) Betula utilis forests
A/F - (Fir) Abies spectabilis and Abies pindrow forests
Td/H - (Hemlock) Tsuga dumosa forests
Pr/CP - (Chir pine) Pinus roxburghii forests
Pw/BP - (Blue pine) Pinus wallichiana forests

Source: Myint, 1998.

Sri Lanka

Table 23. Land area and Forest cover by Ecological zone in Sri-Lanka (FRA 1990)

Ecological Zone
Wet zone Moist zone Dry zone Very Dry zone Desert zone Hill and Montane zone
Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover Land area Forest cover
000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage 000

ha

Percentage
1810 14 2331 26 2088 40 0 0 0 0 234 24

Source: FAO, 1993

Forest categories interpreted from Satellite Imagery and derived by GIS techniques in Sri-Lanka are as under

Table 24. Forest types in Sri-Lanka

Image Categories GIS Subdivisions GIS Criteria
Closed Canopy Forest Lowland Rainforest >2500 mm rainfall, <1000 m elevation
  Moist Monsoon Forest 1800-2500 mm rain, <1000 m elevation
  Dry Monsoon Forest <1800 mm rainfall
  Sub-Montane Forest >1800 mm rainfall, >1000 m elevation
Mangroves    
Riverine Dry Forest    
Sparse and Open Forest    
Conifer    
Eucalyptus    
Teak    
Mahogany    
     

Source: Legg and Jewell, 1998.

3.5 Wood Supply Potential

Mr. M Lorenzini led a discussion on the basis of his paper on "Deforestation and Accessibility" which evaluates use of GIS information as a potential tool to improve the knowledge of human-induced "deforestation" or forest "access" process. Mr. Lorenzini explained it with the help of a study in the Central Africa region, which includes five countries (Cameroon, Central African Republic, Congo, Equatorial Guinea, Gabon and Zaire). This study attempts to use new global 30 ARC seconds (approx. 1 km resolution) DEM GTOPO30 data sets with other global digital data sets like the Digital Chart of the World and the Global Land Cover characterization data set. Besides these, this study uses a number of selected multi-temporal observations (full LANDSAT scenes) for the region, to develop the maps for accessibility.

The paper hypothesizes that, people convert forests into other land-use due to two main factors; (a) easy accessibility, and (b) income generating potential of tree and forest land. The model is based on a cost distance algorithm available within ARC/INFO GIS package. Two surfaces were generated independently from two data sets i.e. urban settlements and roads. These were used in the final cost map as a weighing factor, so that total cost increases when people move far away from the towns/villages and roads.

3.5.1 Recapitulation of Terms and Definitions

The expert consultation held at Kotka, Finland in 1996 (Kotka III) desires that the forests should be divided into available/not available for wood supply, where availability for wood supply is defined according to legal, economic, technical or environmental restrictions.

3.5.2 Information currently available with FAO and that provided by the participants is given below.

Bangladesh

Table 25A. Forest Available for Wood Supply

  Total Forest (1000 ha) Undisturbed Forest (1000 ha) Disturbed Forest (1000 ha)
Broad leaved 0 0 0
Moist Deciduous 0 0 0
Mangroove 0 0 0
Total 0 0 0

Source: GFSS Estimate

Table 25B. Forest Not Available for Wood Supply

                   
  Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest

(1000 ha)

Inaccessible Area

I II III

Legally Protected Area (1000 ha)
Broad leaved 70 300 370 0 0 328 42
Moist Deciduous 0 50 50 0 0 37 13
Mangroove 80 200 280 0 0 248 32
Total 150 550 700 0 0 613 87

Source: GFSS Estimate

Bhutan

Table 26A. Forest Available for Wood Supply

  Total Forest

(1000 ha)

Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Broad leaved 762 212 550
Open 0 0 0
Coniferous 480 230 250
Total 1242 442 800

Source: GFSS Estimate

Table 26B. Forest Not Available for Wood Supply

  Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest

(1000 ha)

Inaccessible Area

I II III

Legally Protected Area (1000 ha)
Broad leaved 316 50 366 300 0 0 66
Open 250 500 750 0 0 650 100
Coniferous 290 100 390 200 0 0 190
Total 856 650 1506 500 0 650 356

Source: GFSS Estimate

India

Table 27A. Forest Available for Wood Supply

  Total Forest

(1000 ha)

Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Dense Broad leaved 19232 0 19232
Open Broad leaved 0 0 0
Mangroove 303 73 230
Coniferous 1200 0 1200
Bamboo 1200 100 1100
Total 21935 173 21762

Source: GFSS Estimate

Table 27B. Forest Not Available for Wood Supply

  Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest

(1000 ha)

Inaccessible Area

I II III

Legally Protected Area (1000 ha)
Dense Broad leaved 3132 4068 7200 2700 0 0 4500
Open Broad leaved 1200 16800 18000 1800 0 13200 3000
Mangroove 130 20 150 0 0 0 150
Coniferous 1000 2000 3000 200 0 1800 1000
Bamboo 0 100 100 50 0 0 50
Total 5462 22988 28450 4750 0 15000 8700

Source: GFSS Estimate

Myanmar

No information is available.

Nepal

Table 28A. Forest Available for Wood Supply

  Total Forest

(1000 ha)

Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Hard wood 1870 440 1430
Mixed 546 100 446
Coniferous 390 30 360
Total 2806 570 2236

Source: GFSS Estimate

Table 28B. Forest Not Available for Wood Supply

  Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest

(1000 ha)

Inaccessible Area

I II III

Legally Protected Area (1000 ha)
Hard wood 710 170 880 50 400 250 180
Mixed 500 80 580 180 250 50 100
Coniferous 460 40 500 150 250 30 70
Total 1670 290 1960 380 900 330 350

Source: GFSS Estimate

Sri Lanka

Table 29A. Forest Available for Wood Supply

  Total Forest

(1000 ha)

Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest 0 0 0

Source: GFSS Estimate

Table 29B. Forest Not Available for Wood Supply

  Undisturbed Forest

(1000 ha)

Disturbed Forest

(1000 ha)

Total Forest

(1000 ha)

Inaccessible Area

I II III

Legally Protected Area (1000 ha)
Total Forest 157 1500 1657 0 0 1092 565

Source: GFSS Estimate

3.6 Changes Over Time

Mr. S Tripathi, FSI & Mr. P K Pathak, FSI presented a paper on a methodology of FSI to assess the changes in forest cover. FSI assesses forest cover periodically on a 2-year cycle. FSI uses IRS-1B Linear Imaging Self-Scanning (LISS-II) Sensor satellite data for the assessment. National Remote Sensing Agency (NRSA) supplies this data in the form of False Color Composites (FCC). FSI staff interpret most (72 percent) of the information visually and the rest (28 percent) digitally. Ground verification helps in post interpretational validation. More than 2,000 spots are checked during the ground verification in each assessment. Manual dot-grid method provides estimates of an area. FSI is slowly adopting the Computer Aided Cartography (CAC) and digital mode of forest cover assessment in a phased manner. The paper elaborates the four steps of Digital Image Processing, i.e. Image preprocessing, Classification, Area calculation, and Ground truthing.

Mr. Marzoli, FAO presented Mr. Drigo's paper the "Monitoring of Forest Resources at district level using Multi-Date Satellite Data" to explain FAO methodology to deal with monitoring of forest resources at district level in order to introduce reliable information on the processes of change into the forestry planning system by applying interdependent interpretation of multi-date satellite data methodology.

Mrs. Ranjana Gupta (FSI) presented a paper on "Forest Grazing". She provided information on the estimated and projected livestock population, based on cumulative growth rates. She also presented the projected annual requirement of green fodder and dry fodder based on NCA (1976) norms. FSI assesses the "incidence of grazing" at the time of ground inventory or validation of forest cover. The basis of assessment is ocular estimation.

FSI classifies forest into five categories, based on the extent of grazing incidence. The paper provides such assessment of grazing incidence by the FSI. Indian forests bear heavy grazing in 43587 Sq.km. (18.85 %), medium grazing in 54638 Sq.km. (23.63 %) light grazing in 64406 Sq.km (27.85%), no grazing in 64810 Sq.km. (28.03 %) and unrecorded incidence of grazing in 3796 Sq.km. (1.64 %) area.

3.6.1 Recapitulation of Terms and Definitions

According to the expert consultation on FRA 2000 held at Kotka, Finland in 1996 (Kotka III), following are agreed terms and their definitions in respect of "Forest Change."

FRA 2000 requires three types of information on forest change- Deforestation, Forest degradation and Forest plantations. The deforestation is defined as change of land use where tree crown cover has depleted to less than 10 percent. The Forest degradation is assessed through reduction in biomass changes in species composition and soil degradation. Data on area and species composition of Forest plantations established either through afforestation or through reforestation is required to assess the changes.

3.6.2 Information on "Change over time" with FAO and that provided by the participants.

Bangladesh

Table 30. Annual changes in Forest and Other wooded land, 1980-90

Country Forest and other land (1000 ha) Natural forest Plantations
    Status

(1000 ha)

Annual change

(1000 ha)

Status

1000 ha

Annual change

1000 ha

Bangladesh 1472 769 -38 235 12.3

Source: FAO, 1995.

Bhutan

Table 31. Annual changes in Forest and Other wooded land, 1980-90

           
Country Forest and other land (1000 ha) Natural forest Plantations
    Status

1000 ha

Annual Change

1000 ha

Status

1000 ha

Annual Change

1000 ha

Bhutan 3168 2809 -16 4 0.2

Source: FAO, 1995.

India

Table 32. Annual changes in Forest and Other wooded land, 1980-90

           
Country Forest and other land (1000 ha) Natural forest Plantations
    Status

1000 ha

Annual Change

1000 ha

Status

1000 ha

Annual Change

1000 ha

India 82648 51729 -339 13230 1009.0

Source: FAO, 1995.

FSI provides following additional information regarding change in forest cover over time.

Table 33. Change in forest cover

Year Forest cover Area (1000 ha)
1987 640,81.90
1989 638,80.40
1991 639,36.40
1993 639,38.60
1995 638,87.90
1997 633,39.70

Source: SFR 1997.

Myanmar

Table 34. Annual changes in Forest and Other wooded land, 1980-90

           
Country Forest and other land (1000 ha) Natural forest Plantations
    Status

1000 ha

Annual Change

1000 ha

Status

1000 ha

Annual Change

1000 ha

Myanmar 49774 28856 -401 235 19.6

Source: FAO, 1995.

Nepal

Table 35. Annual changes in Forest and Other wooded land, 1980-90

           
Country Forest and other land (1000 ha) Natural forest Plantations
    Status

1000 ha

Annual change

1000 ha

Status

1000 ha

Annual Change

1000 ha

Nepal 5751 5023 -54 56 4.3

Source: FAO, 1995.

Sri Lanka

Table 36. Annual changes in Forest and Other wooded land, 1980-90

           
Country Forest and other land (1000 ha) Natural forest Plantations
    Status

1000 ha

Annual Change

1000 ha

Status

1000 ha

Annual Change

1000 ha

Sri Lanka 3998 1746 -27 139 6.0

Source: FAO, 1995.

Rate of Deforestation in Sri-Lanka averages around 20,000 ha per year. Legg and Jewell (1998) provide additional information about change in forest cover.

Table 37. Changes in Forest Cover, 1992

Closed forest 1992 Closed forest 1983
1,582,756 1,757,995

Source: Legg and Jewell, 1998.

3.7 Volume and Biomass

Mr. S K Chakrabarti (FSI) presented the methodology of FSI for assessing volume of the forest growing stock in India. This information is collected at the time of ground validation of the forest cover. The methodology, therefore, consists of combining the maps of forest density and forest types into a forest "density-type" map. This "density-type" map at 1:50,000 scale of all the States and Union Territories is divided into grids of 2.5' x 2.5' (latitude x longitude). Each grid is allocated one of the four density categories; Very dense forest, dense forest, open forest, no forest. The grids are stratified on the basis of the species associations (forest type) and density categories. For example, in 1995, the grids were stratified into 21 strata of unique forest composition. For each of these grids, the secondary data on "volume per hectare" is drawn from standard national forest inventory on the basis of two sample plots of 0.1 ha in each forested grid. The volume of growing stock in a forested grid is obtained by multiplying the average volume per hectare information from the concerned sample plots with the area of the grid (1800 ha). The grid volumes are added to get an estimate of growing stock for each stratum. The volumes of each strata are added at the state or national level to estimate the volume of the forest growing stock.

Mr. M Lorenzini presented FAO methodology of estimating actual forest volume using forest inventory and climatological data. This methodology is based on GIS and helps to improve the knowledge of spatial distribution of forest volume. FAO develops a model to produce an "Actual Volume" map considering that available data from forest inventories were insufficient to extrapolate volume estimates across the region.

The data used in his paper were derived from the forest inventory reports of the Tropical South Asia: India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka. The vegetation maps and administrative unit maps were combined in an overlay and linked to the forest inventory database to generate Forest Inventory Plot map. The population data of the year 1980 were considered and a Population Density map was generated. The Vegetation map was drawn by using the map proposed by the International Institute for Vegetation Mapping, France. The Forest map was derived from a reclassification of the vegetation map on the basis of statistical analysis.

Weck's Climatic Index -WPIB (calculated with Brown's formula) and an Accessibility map is prepared, based on the assumption that the current distribution of the forest biomass is defined by the potential amount that a landscape can support under the prevailing climatic, edaphic and topographic condition and the cumulative impact from human activities.

Different data layers like Inventory Plot Map, Accessibility Map, Population Density Map and Productivity Index Map(WPIB) help to generate the model that provides the Volume Density Map. The Volume Density map is finally clipped with the Forest Map to obtain Forest Volume Map.

3.7.1 Recapitulation of Terms and Definitions

The expert consultation on FRA 2000 held at Kotka, Finland in 1996 (Kotka III), desires information on forest volume in terms of "Volume" and "Biomass" as defined below.

The growing stock is defined as Gross "Volume Over Bark" of free bole (from stump or buttress to crown point or first main branch) of all living species more than 10-cm dbh.

The "Biomass" is defined as the totals above ground organic matter in trees, expressed in oven dry tons per unit area.

3.7.2 Information on "Volume and Biomass" with FAO and that provided by the participants.

Bangladesh

Table 38. Forest area, Volume and Biomass, 1990

           
Country Area (1000 ha) Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
Bangladesh 769 77 59.2 136 104.2

Source: FAO, 1995.

Bhutan

Table 39. Forest area, Volume and Biomass, 1990

           
Country Area

1000 ha

Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
Bhutan 2809 150 421.4 181 508.1

Source: FAO, 1995.

India

Table 40. Forest area, Volume and Biomass, 1990

           
Country Area

1000 ha

Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
India 51729 47 2431.3 93 4805.7

Source: FAO, 1995.

An attempt to estimate the growing stock of forest resources was made by the FSI in 1995 under a UNDP project. The growing stock of the country was assessed using information available in vegetation maps, thematic maps and ground inventories of forests carried out from time to time. The growing stock of the country is estimated to be 4740 million cu.m with an average volume of 74.42 cu.m per ha.

Table 41. Forest Growing Stock in India by Broad Forest Strata, 1995

         
Conifers

(million m3)

Broad Leaved

(million m3)

Bamboo

(million m3)

Total Volume
      (million m3) m3/ha
688.302 4021.272 31.284 4740.858 74.42

Source: Chakrabarti,1998.

Myanmar

Table 42. Forest area, Volume and Biomass, 1990

           
Country Area

1000 ha

Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
Myanmar 28856 145 4184.1 217 6258.9

Source: FAO, 1995.

Nepal

Table 43. Forest area, Volume and Biomass, 1990

Country Area

1000 ha

Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
Nepal 5023 55 276.3 109 548.7

Source: FAO, 1995.

Sri Lanka

Table 44. Forest area, Volume and Biomass, 1990

Country Area

1000 ha

Volume Biomass
    m3/ha Total (million m3) Tons/ha Total (million tons)
Sri Lanka 1746 45 78.6 113 197.7

Source: FAO, 1995.

3.8 Felling and Removal

The workshop did not deliberate on this core item of FRA 2000. According to the expert consultation held at Kotka, Finland in 1996 (Kotka III), "felling" and "removals" indicate the intensity of wood utilization in a given period (e.g., annual) as described below.

"Felling" indicates the average over bark volume of all trees upto 10 cm (dbh), living or dead that are felled, whether removed or not.

"Removals" means the annual under bark volume actually cut and removed that generate revenue for the owner of the trees. It includes wood used for domestic and industrial purposes but does not include "fuelwood."

3.9 Forest Fires

Mrs. Ranjana Gupta, FSI, India presented a paper on FSI methodology for assessing forest fire. She explained the sampling design and the methodology for assessing the extent of forest fires. The information on fire incidence is collected at the time of ground validation and forest inventory, which is done on 1:50,000 scale topographic sheet divided into 36 grids of 2 ½' x 2 ½' of latitudes and longitudes. In each of such grids, two sample points are marked. The fire incidence information is collected for the sampled area from secondary sources as well as from visual examination of about 2 ha area around the sample square plot of .1 ha. Qualitative data such as land use, crop composition and its density, fire and grazing incidence etc. are recorded in the Plot Description Form (PDF).

FSI uses six categories for reporting forest fire incidence in the country. Five of these categories relate to the forest area for which recorded information on forest fires is available and the sixth category represents rest of the forest area for which no such information exists. The five categories are forest areas with very heavy, heavy, frequent, occasional, and no fire.

3.9.1 Recapitulation of Terms and Definitions

According to the expert consultation on FRA 2000 held at Kotka, Finland in 1996 (Kotka III), information is needed on area (size), damage and time trend of forest "fire". The expert consultation describes the forest "fire" as given below,

The "fire" includes fire that breaks out and spreads on or to "forest" and "other wooded land" but does not include "prescribed" or "controlled" burning of "forest" or "other wooded land."

3.9.2 Information on "Forest Fires" with FAO and that provided by the participants.

Bangladesh

No information is available.

Bhutan

No information is available.

India

Forest fires may be natural or man made. The number of fires that arise due to natural causes are not more than 5 percentage of the total number of fires in one year. Men cause about 95 percent of the fires. Table 44 demonstrates assessment of the extent of forest fire under different categories.

Table 45. Extent of forest fire under different categories

Country Forest Area

(Sq.km)

Extent of Fire Incidence (Sq.km) Total

(Sq.km)

    Very Heavy Heavy Frequent Occasional No Fire Not Recorded  
India 209003.5 1817.1 289.2 10793.4 89998.5 101951.2 4154.1 209003.5

Source: Gupta, 1998.

Myanmar

No information is available.

Nepal

No information is available.

Sri Lanka

No information is available.

3.10 Non Wood Goods and Services

Mrs. Ranjana Gupta, FSI, India presented a paper on FSI conception of Non Wood Timber Products. She dealt with classification, commercial significance, importance, and constraints in the collection and marketing of NTFP. Statistics on statewise contribution to the forest revenue, potential and production, and quantity and value of export of NTFP are presented in the paper. The paper also deals with the proposed methods for the assessment of NTFP's.

3.10.1 Recapitulation of Terms and Definitions

The expert consultation on FRA 2000 held at Kotka, Finland in 1996 (Kotka III), classified non-wood goods and services into six major groups. The goods are divided in three categories; (a) Products for human consumption, (b) Fodder and Forage and (c) Other non-wood goods. Similarly, the services have been grouped into three classes; (a) Providing protection values (b) Providing Social and Economic values and (c) Providing Aesthetic, Cultural, Historical, Spiritual and Scientific values. The non-wood goods include all goods of biological origin derived from forest or any land under similar use, and exclude wood in all its forms.

FRA 2000 needs the following information on each "Non Wood Good" or "Service":

· description,

· indication of relative and absolute importance,

· changes in supply and demand, and

· quantity and value supplied.

3.10.2 Information on "Non Wood Goods and Services" with FAO and that provided by the participants.

Bangladesh

No information is available.

Bhutan

No information is available.

India

In India, Non Timber Forest Products (NTFP) of commercial importance have been divided into the following (Table 46) nine classes:

Table 46. Categories of NTFP in India

Sl. No. Category
1 Fibers and flosses
2 Grasses (other than oil producing grasses) bamboo's and canes
3 Essential oils (including oil yielding grasses)
4 Oil seeds
5 Tans and dyes
6 Gums, resins and oleo-resins
7 Drugs, spices, poisons and insecticides
8 Edible products
9 Animal, mineral and miscellaneous products

Myanmar

No information is available.

Nepal

No information is available.

Sri Lanka

No information is available.

3.11 Additional Parameters

Biodiversity

Mrs. C Elouard, FIP, provided exposure on a field method to assess biodiversity through her paper "Tools for Assessment of Forest Biological Diversity". The data used in this paper was collected during the field work from three forest types, e.g., evergreen, moist deciduous, dry deciduous forests in Karnataka state, India. The structural analysis in the paper uses the frequency data to understand the distribution of species and individuals, and the application of the relationship between height and girth, to understand the structural stratification of the forest. The paper also explains the diversity assessment through computation of various indices like Chao, Simpson, Shannon -Wiener, and Evenness.

Mr. M Lorenzini explained the FAO methodology to assess biodiversity at ecosystem level by presenting his paper "Biodiversity Assessment: FAO perspective". The combination of Eco-floristic layers and Vegetation type layers lead to a number of unique combinations of actual vs. potential land-cover conditions. Dominant or characteristics woody species of the flora and their successional patterns within major ecological regions define eco-floristic zones. The vegetation cover/condition layers are expressions of vegetational characteristics such as density, continuity of plant cover, height, etc. and human impact on the vegetation. The vegetation map together with eco-floristic zones may be used to capture global biodiversity. Relating biodiversity with human pressure indicators provides capacity to predict or fill gaps to assess biodiversity at any spatial or temporal location.

3.11.1 Recapitulation of Terms and Definitions

FRA 2000 utilizes the following definition of biodiversity provided by UNCED 1992:

"Biological diversity refers to the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are a part; this includes diversity within species, between species and of ecosystems"

FRA 2000 assesses biodiversity at ecosystem level and defines it as "the different forest or vegetation types or species associations in an area unit" (Singh, 197)

Shifting Cultivation

Dr. V. N. Pandey, FSI, introduced the subject of assessment of forestland, under shifting cultivation, through his paper on "Shifting Cultivation." The shifting cultivation is one of the major factors, that define forest change, not only in India but other countries. In India, it extends over Andhra Pradesh, Bihar, Madhya Pradesh, Orrissa, Tamil Nadu and North-Eastern states. Currently, FSI assesses these areas, as a part of the regular forest cover assessment, and classifies such areas as non-forest areas.

The shifting cultivation is the single most important factor, that explains not only the forest change in North-Eastern states but also the social and economic conditions of the dependent communities. Recognizing the unique significance of shifting cultivation in forest assessment, the FSI has initiated a special study on this subject in Nagaland. The paper provides a brief review of past assessments of shifting cultivation areas and a summary of the proposed study.

3.12 Other Items discussed at the workshop

3.12.1 Electronic Networking

Dr. Kailash Govil, FAO presented a paper on electronic networking including webpage. The paper deals with country, regional and global node for electronic networking. The Country Capacity Building Regional Node "CCBRN" aims to build country capacities for better assessments and effective strategic planning for participative sustainable management of forests through exchange and sharing of information and knowledge with the help of electronic networking. The CCBRN will serve as a repository of regional data and publisher of useful information. In addition, CCBRN will have an Answer to Question "AQ" service. This network will encourage the development of joint activities between countries, institutions and individuals in the region. In each country, network activities will be coordinated by one of the member institutes or organization, which will act as national node CCBNN. These national nodes will maintain contacts with all the regional nodes CCBRN. The regional node, in turn, will maintain similar relations with global node CCBGN maintained by Director CCB at FAO HQs. All the participants were excited about it and wanted "Internet" and "Web" facilities as early as possible.

3.12.2 Planning

Andrea Kutter, FAO deliberated on her paper "An integrated approach to planning and management of Landscape resources as promoted by FAO", and explained the framework for land evaluation (1976) and its major problems including the new approach for integrated land-use planning.

Mr. A Marzoli, FAO presented a very useful and potential paper on "Area Production Model (APM)" which is a simulation model for long-term land use changes and prediction of yields from Agriculture and Forestry. It aims to offer the possibility of harmonizing the use of sectoral inputs and balancing the supply and the demand through the simulation of consequences under various conditions. The APM needs data on land use, biomass/energy and forestry to develop alternative development scenarios.

3.12.3 Forest Inventory Management System

Mr. Grewal presented his paper "Global Forest Resource Assessment 2000 - a user's perspective", to initiate discussion on Forest Inventory Management System (FIMS) in India. He pointed out that, almost all the state forest departments in India are regularly carrying out very intensive micro level forest inventories, for working plan and other purposes. This inventory data is generally kept, by the forest departments, in hard copies, and therefore, is very voluminous and cannot be easily harmonized and used. The Forest Department of Maharashtra State has taken a lead in this matter by computerizing such forest inventory data for its easy and quick use in planning, management and monitoring of forest resources. Mr. Grewal presented a live demonstration of such a FIMS, which is being used for online inventory data processing in Maharashtra State for the last one year. This system is very user friendly with pull down menu for easy data entry and browsing. The potential utility of this FIMS has been increased through its integration with a Geographical Information System.

References

Ahmad, Ishtiaq Uddin, 1998. Forest Resource Assessment and Introduction of Geographical Information System (GIS) for Resource Information Management in Bangladesh. Paper presented at FAO organized workshop on "The Status of Forest Resources Assessment in the South Asia Sub-region and Country Capacity Building Needs" at Dehradun, India. June 8 to 12,1998.

Chakrabarti, S. K, 1998. Growing Stock Estimates: The Country Perspective - FSI contribution. Paper presented at FAO organized workshop on "The Status of Forest Resources Assessment in the South Asia Sub-region and Country Capacity Building Needs" at Dehradun, India. June 8 to 12, 1998.

FAO, 1993. Forest Resources Assessment 1990 -- Tropical Countries. FAO Forestry Paper 130. Food and Agriculture Organization of the United Nations, Rome.

FAO, 1995. Forest Resources Assessment 1990 - Global synthesis. FAO Forestry Paper 124. Food and Agriculture Organization of the United Nations, Rome.

GFSS, 1998. Draft of Global Fibre Supply Study, Forest Products Division, Forestry Department, Food and Agriculture Organization of the United Nations, Rome.

Gupta, Ranjana, 1998. Forest Fire. Paper presented at FAO organized workshop on "The Status of Forest Resources Assessment in the South Asia Sub-region and Country Capacity Building Needs" at Dehradun, India. June 8 to 12, 1998.

Myint, A. K, 1998. Forest Resources of Hindu Kush - Himalaya. Paper presented at FAO organized workshop on "The Status of Forest Resources Assessment in the South Asia Sub-region and Country Capacity Building Needs" at Dehradun, India. June 8 to 12, 1998.

OSFSMP, 1995. An Overview of the Sri Lanka Forestry Sector Master Plan,

Forestry Planning Unit, Ministry of Agriculture, Lands and Forestry, Battarmulla, Sri Lanka.

Pandey, D, 1998. Assessment of Forest Plantation areas. Paper presented at FAO organized workshop on "The Status of Forest Resources Assessment in the South Asia Sub-region and Country Capacity Building Needs" at Dehradun, India. June 8 to 12, 1998.

SFAR, 1998. State of Forest Assessment Report, Forest Department, Ministry of Forestry, Myanmar.

SFR, 1997. State of Forest Report, Forest Survey of India, Ministry of Environment and Forests, Dehradun, India.

Appendix 1: Resource persons and participants

The resource persons

FAO

Mr. A. Marzoli, Forestry Biometrician/Ecologist, FAO

Dr. M. Lorenzini, GIS Expert, FAO

Dr. Kailash C Govil, Regional Project Coordinator

French Institute of Pondicherry

Miss. C. Elouard, French Institute of Pondicherry

Forest Survey of India

Dr. D Pandey, Director, FSI

Mr. P C Tyagi, Joint Director, FSI

Mr. V N Pandey, Joint Director, FSI

Mr. S K Chakrabarti, Deputy Director, FSI

Mrs. Ranjana Gupta, Deputy Director, FSI

The participants

Bangladesh

Mr. Anwarul Huq, Deputy Secretary, Ministry of Environment and Forest

Mr. Ishtiaq Uddin Ahmed, Deputy Conservator of Forest

Bhutan

Mr. G K Pradhan, Joint Director, Forestry Survey Division, Ministry of Agriculture

Mr. Pasang Wangchen Norbu, DFO, Forest Resources Development, Ministry of Agriculture

India

Mr. J S Grewal, Conservator of Forests, Working Plans, Nagpur

Mr. Ashok Ramteke, AIGF, Ministry of Environment and Forests

Myanmar

Mr. U Shwe kyaw, Deputy Director General, Forest Department

Mr. U Tint Lwin, Staff Officer, Forest Department

Nepal

Mr. Sharad Rai, Divisional Forest Officer, Department of Forest

Mr. Surya Prasad Adhikari, Legal Officer, Department of Forest

Sri Lanka

Mrs. P A Kumaradasa, deputy Director, Ministry of Forestry & Environment

Mr. P M A de Silva, Dy. Conservator of Forests, Department of Forests

Appendix 2: Daywise session profiles of the workshop

Date Time Deliberations
June 8th 9.30-10.30 Inaugural Session

Welcome Speeches

Dr. D Pandey, Director, FSI

Dr. K C Govil, Regional Project Coordinator, CCB, FAO

Dr. M Lorenzini, GIS Expert, FAO

Mr. P C Tyagi, Joint Director, FSI

Key Note Address

Dr. B. N Gupta, Director General, ICFRE

Words of Thanks

Mrs. Ranjana Gupta, FSI

June 8th 10.30-11.15 Coffee Break
June 8th 11.15-11.30 Introductions of participants and their expectations from the Workshop
June 8th 11.30-12.30 Global Forest Resources Assessment. Introduction to the project Objectives, activities and definitions

Mr. A Marzoli, FAO

June 8th 12.30-13.00 FAO strategy for Country Capacity Building

Mr. M Lorenzini, FAO

June 8th 13.00-14.00 Lunch
June 8th 14.00-17.30 Presentations on the Present Status of Forest Resources Assessment at National level by representatives of each country

Bangladesh: Mr. Ishtiaq Uddin Ahmed

Bhutan: Mr. Pasang Wangchen Norbu

Myanmar: Mr. U Shwe kyaw

Nepal: Mr.Sharad Rai

Sri Lanka: Mr. P M A de Silva

India: Mr. Davendra Pandey

June 9th 9.00-10.30 Problems related to assessment of Forest area: the FAO perspective

Mr. A Marzoli, FAO

June 9th 10.30-10.45 Tea Break
June 9th 10.45-13.00 Problems related to the assessment of forest area: the country perspective-India contributions:

Shifting Cultivation:

Dr. V N Pandey, FSI

Assessment of Plantation areas:

Dr. D Pandey, FSI

Forest Ownership:

Mr. R K Upadhyay, IGNFA

Forest Fire, Grazing and NWFP:

Mrs. Ranjana Gupta, FSI

June 9th 13.00-14.00 Lunch
June 9th 14.00-17.30 Estimation of Forest Cover for Standard reference year

Mr. A Marzoli, FAO

Growing stock estimates: the country perspective-FSI contribution

Mr. S K Chakrabarti, FSI

Forest Inventory and Climatological data

Mr. M Lorenzini, FAO

June 10th 9.00-9.30 Biodiversity Assessment: the FAO perspective

Mr. Lorenzini, FAO

June 10th 9.30-10.30 Protected areas in India and Forest area by naturalness categories

Mr. P C Tyagi, FSI

June 10th 10.30-10.45 Tea Break
June 10th 10.45-11.30 Biodiversity characterization at landscape level using satellite data and GIS

Dr. P S Roy, IIRS

June 10th 11.30-13.00 Round table discussion on country specific problems
June 10th 13.00-14.00 Lunch
June 10th 14.00-15.00 CCB and Networking

Dr. K C Govil, FAO

June 10th 15.00-16.00 Forest availability for Wood supply: naturalness/accessibility

Mr. M Lorenzini, FAO

June 10th 16.00-16.15 Tea Break
June 10th 16.15-17.00 Forest Resources of Hindu Kush Himalayas

Dr. A K Myint

June 11th 9.00-10.30 Area Production Model: example of a case study for one district of Andhra Pradesh, India

Mr. A Marzoli, FAO

June 11th 10.30-10.45 Tea Break
June 11th 10.45-13.00 Forest Cover Change Assessment: FSI methodology

Mr. S Tripathi and Mr. P K Pathak, FSI

Biodiversity Assessment: the regional perspective

Miss. C Elouard, French Institute of Pondicherry

June 11th 13.00-14.00 Lunch
June 11th 14.00-15.00 An Integrated Approach to planning and management of land resources as promoted by FAO

Mrs. Andrea Kutter

June 11th 15.00-15.45 Productivity Estimation Models

Dr. D Pandey, FSI

June 11th 15.45-16.45 User perspectives

Mr. J S Grewal

June 11th 16.45-17.30 Monitoring of Forest Resources at district level

Mr. A Marzoli, FAO

June 12th 9.00-9.30 Group Photograph
June 12th 9.30-10.30 Filling up of proforma and questionnaire by participants
June 12th 10.30-10.45 Tea Break
June 12th 10.45-12.00 Presentation of country evaluation and future expectations

Bangladesh: Mr. I U Ahmed

Bhutan: Mr. G K Pradhan

India: J S Grewal

Myanmar: U Shwe kyaw

Nepal: Sharad Rai

Sri Lanka: M A Kumardasa

June 12th 12.00-13.00 Concluding remarks and thanks

FAO:

Dr. A Marzoli

Mr. M Lorenzini

Dr. K Govil

FSI:

Dr. D Pandey

Mr. P C Tyagi

Participant representative:

Mr. U Shwe Kyaw, Myanmar

Appendix 3: An outline for country report on FRA 2000

I. Identification of policy problems and information needs.

Satisfying present and future needs of fuelwood and fodder

Satisfying present and future needs of watershed protection and rehabilitation of degraded land

Satisfying present and future needs of industrial wood

Sustainable forest management

Conservation of biological diversity

Joint forest management

II. Information

II.A. Ecological setting

Country land area and administrative units

Ecological zones

Physiography

Rainfall

Productivity index

II.B. Socio-economic setting

Demography

Land use

Agriculture (area/productivity)

Livestock

Energy requirements

GNP

Occupation

Ownership

II.C. Forest Resources

Description of the natural vegetation types

Present forest area by administrative unit and ecological zone.

Potential versus actual forest resources

Volume and Biomass

Forest available for wood supply

Forest protection and Conservation

Forest plantations

Forest management

Increment

Minor forest produces

II.D. Forest production / consumption studies

Wood production and consumption (roundwood, fuelwood) balance

Fodder production/consumption studies

III. Trends and prospectus (Future scenarios)

Deforestation

Plantation development (area and increment)

Land use changes

Forest degradation

Biomass change

Wood supply and demand future balance

Biodiversity loss risk

Soil/water conservation

Appendix 4: Information for electronic networking

Country: ____________________ Name of person filling this form _________________

Position or post: ______________________________________________________________

(1) Has someone been nominated to coordinate this project at National level YES / NO

(2) Address of the person for correspondence regarding the Country Capacity Building Project.

His/Her Name: ____________________________

Position: _________________________________

Mailing Address: ___________________________________________________________

___________________________________________________________

___________________________________________________________

Telephone: ___________________________________________________________

Fax: ___________________________________________________________

Email: ___________________________________________________________

(3) More information about the computer and Electronic Network Facilities

Macintosh Yes / No

IBM Yes / No

Speed _______Mhz

Type Motorola / Pentium or other _______________

RAM _________Mega bytes

Hard disk _________Mega / Giga bytes

Floppy 5(1/4)", 3(1/2)", SD / HD

Modem Type ___________ and Speed less than 14.4 kbps/14.4/more than 14.4

Type of Internet TCP / IP or shell _____________________________________________

Operating System Macintosh/OS2/Unix/Windows3.14/Windows95/WindowsNT3.5/Windows 4.0

Word Processing: Word / Word Perfect and other __________________________________

GIS ___________________________________________________________

Remote sensing ___________________________________________________________

(Please fill the form and return it personally to Dr. Kailash C. Govil, Regional Project Coordinator, Country Capacity Building Project, Room No. 124, Forest Survey of India Building, Dehradun)

APPENDIX 5: Technical papers

Section 1: Deforestation and Forest Accessibility

by Massimiliano Lorenzini, FAO

Introduction

The word deforestation indicates the conversion of forests to other land cover types. These changes can be produced by natural events like fire, tornado, and avalanches as well as by human beings. In this paper a methodology based on the use of GIS information, is evaluated as a potential tool to improve the knowledge of the human-induced deforestation processes.

The problem, that many researcher are nowadays approaching is related to the following question:

Where is the deforestation taking place and

Where is likely going to take place in the future?

On a global scale this is a very challenging problem since local scale deforestation models, usually based on a great number of variables, in most of the cases cannot be applied on a global scale due to the lack of information for such variables all over the world.

Simple models have been implemented in the past 10 years, which parameterize in a quite simple but robust way the obvious observation that people are the cause of the deforestation; the population pressure explains very well the depletion of the forests.

Nevertheless tests are in progress attempting to evaluate where the population pressure is likely to affect the forest.

Study area and material

The present study was carried out using global coverage GIS layers limited the analysis to the Central Africa Region. This includes the following countries Cameroon, Central Africa Republic, Congo, Equatorial Guinea, Gabon and Zaire.

The idea of attempting an accessibility analysis become feasible few months ago when EROS DATA CENTER released the new global 30 arc seconds (approx. 1 km resolution) D.E.M. GTOPO30. This linked to other global digital datasets like the Digital Chart of the World and the Global Land Cover Characterization Dataset, represents a formidable toolkit for global studies related to forestry applications.

Aside these wall-to-wall spatial databases a number of selected multitemporal observation (full Landsat scenes) were available for the region (see next map) These were used to assess the reliability of different accessibility maps.

The hypothesis

The hypothesis to be tested was the following:

People convert forests in other land -use covers due to two main reasons:

· There are forests available and either the forests or the land are of good quality for clearings. Obviously the forested lands accessibility is a key factor to determine the convenience in selecting on place or another.

· People need to either convert forested lands to other, more profitable, land-use practices or log them because of the value of the trees. This value can be determined in term of energetic need (fuelwood) or trade income (commercial logging). The weight of these needs is determined by a number of factors, mainly related to the socio-economic context. Large areas of the Brazilian Amazon have been converted from forest to grassland to support cattle grazing. Shifting cultivators in Tropical South East and Insular Asia rely on burn and slash practices for their villages agricultural sustainability. Trees felling can be caused by commercial or self-sustainability needs. The demand of forested land is, in both the cases, influenced by their accessibility. It is reasonable to think that people move from their villages/towns to convert forested lands into other land-use cover types. It is realistic to assume that this action is not isotropic but will take place along preferential paths. These were considered, in this study, to be function of the road network and the landscape morphology.

First phase Modelling

The model tested in the Central Africa study is based on a cost distance algorithm available within the ARC/INFO GIS Package. Two surfaces were generated independently from two datasets urban settlements and roads. These were used in the final cost map as weighting factors so that the total cost increases when people move far away from both the towns/villages and roads.

Step-1- a slope map was generated from the D.E.M. map expressed as percent rise.

Step-2- a first raster map was generated as the least accumulative cost surface resulting from the function sum cost* distance from villages/town) estimated from the villages\town mapped in the DCW. The resulting map looks like a D.C.m. where the peaks (white) indicate high cost regions and the valleys (black) low cost regions centered in the urban settlements.

Step-3- a second raster map was generated as the least accumulative cost surface resulting from the function sum (cost* distance-from-roads) estimated from all the roads mapped in the DCW.

Step4- The final cost map, shown below, was produced combining the two maps obtained during step 1 and step2 with the following formula:

Final cost = int(ln(cost-urban*cost-road) )+1

Accessibility vs. Deforestation

In order to assess the value of the accessibility map information derived from multi temporal remote sensing data was used. 23 sites in the Central Africa region were analyzed in the past year with the scope of monitoring the processes of change in the Central Africa forests.

For each site, one of the final outputs was a change map depicting 14

9 out of the 100 possible) categories of land cover changes. Two of them were relevant for the present study) DEFORESTATION and b) STABLE FOREST.

The first category was defined as change of land cover from forest (closed, open or long fallow.

With the change maps available within the GIs it was possible to overlay each of the 23 sites with the accessibility map. This process produced individual as well as a cumulative partial/statistical breakdown of the DEFORESTATION and STABLE FOREST category by accessibility belts.

Ranking forested areas on the basis of accessibility belts could be a viable approach for surveys based on sampling techniques where stratification, oriented to minimize the errors in estimating deforestation, could improve the final results.

These findings, if confirmed by similar studies in other regions, could be useful in setting quantitative parameters to the likelihood of the forest to be depleted by mankind.

Second phase Modelling:

The first phase modelling represented a preliminary exploratory analysis oriented to evaluate in a semi-quantitative way the relationship between accessibility and deforestation. The initial results authorized a more in-depth validation phase, which was carried out analyzing more than 100 satellite scenes throughout the Tropics. The accessibility map was hence produced to cover most of the tropical countries.

The analysis, performed on a sub-regional basis, clearly indicates with different threshold, deforestation patterns and accessibility surfaces are highly related.

Applications

The preliminary analysis indicated the potentiality of the accessibility map as indicator human induced disturbance on forests. An application of this map was the assessment of forest volume densities in the Sub-region 44 (Continental South Asia) where it was found that observed volumes could be predicted using climatological indicators (Weck's Productivity Index) and disturbance/degradation indicators (accessibility).

Accessibility belts were grouped into 3 major zones to highlight, within each of the original TRESS category, areas where the combined presence of human beings (indicated by town/ villages and roads) and favourable topographic conditions can be interpreted (and, on a sampling basis, validated by high resolution satellite observation) as forest zones at high, medium and low risk of deforestation/degradation.

SECTION 2: The Area Production Model (APM)

by Walter Antonio Marzoli, Forest Biometrician/Ecologist, FAO

A CASE STUDY: ADILABAD

Example of a case study developed for one district of Andhra Pradesh - India

1. Introduction

The APM is a simulation model for long-term land use changes and prediction of yields from agriculture and forestry. APM aims to offer the possibility of harmonising the use of sectoral inputs and balancing supply and demand through the simulation of consequences under various conditions (scenarios). Such model can be used by decision-makers at the policy level to examine possible alternatives through identifying consequences of various strategies under given conditions.

A case study has been developed for the district of Adilabad using APM in order to show one example of practical utilization of the data collected described in Part 1. The data required for the APM are the following:

Land use Descriptive data Projection data
  Population

Agricultural productivity

Area for each land use class

Priority for land use changes

Population growth

Productivity growth

Socio-economic development

Biomass/energy data Biomass production

Agriculture crops

Agriculture residues

Autoproduction

Energy requirements

Change in energy demand
Forestry data Existing forest data

Growing stock

Increment

Exploitation

New plantation data

Increment factor

Exploitation factor

Area to be established

Management specifications

(growth, rotation, etc.)

The data set required for APM has been collected for all district of Andhra Pradesh (see Appendix 1), only minor adjustments are needed to prepare an input data set for a given district. The case study for Adilabad was worked out as example.

In the context of Andhra Pradesh the APM can be used for the forecast of the following variables:

Estimates the demand for new agricultural land and the resulting deforestation.

Project the supply and demand of biomass for energy and their balance. This is tightly related to forest degradation since the biomass deficit is mainly filled by over exploitation of natural forests.

Simulate the development of new forest plantation and their impact on the future energy balance.

The Adilabad case study was carried out using alternative development scenarios. The reference scenario chosen is based on the assumption of no significant changes in development patterns. Basically the observed development during 1981-91 has been projected over a 30-year period (1991 to year 2021). This basic scenario will be used as a reference to compare the results of several development alternatives.

The data used as input were extracted from the Districts Information Summary listed in Annex 1 and organized into standard input form for APM as described in the next paragraph.

2. Input data

Input data set for basic scenario: APM run 00

STATE = ANDHRA PRADESH DISTRICT = ADILABAD

STARTING YEAR: 1991

NO OF FIVE- YEAR PERIODS: 6

TOTAL AND RURAL POPULATION (*1000): 2083. 1601.

TOTAL LAND AREA (HA* 1000): 1644.

DISTRIBUTION OF TOTAL LAND AREA (HA*1000):

AGRICULTURE LAND

SUBSTANCE FOOD 356

MARKETED CROPS 262.

TOTAL 618.

In Adilabad subsistence food crops include Rice, Millets, Other cereals and pulses. Marketed crops are Cotton, Groundnut, Seasamum, Castor, Sugarcane and Tobacco.

FARM FORESTS

NATURAL FORESTS 74.0

TOTAL 74.0

The area under this categories represents forest blanks (i.e. designated forest land not actually covered with trees), assuming that part of this areas should be available for Joint Forest Management.

INDUSTRIAL FORESTS

NATURAL FORESTS 627.0

`PLANTATIONS 22.0

TOTAL 649.0

Area of natural forests and plantations.

OTHER LAND

POTENTIAL AGRICULTURE LAND 23.00

POTENTIAL FOREST LAND 179.00

UNPRODUCTIVE LAND 101.00

TOTAL 303.00

The culturable waste land was classified as potential forest land while agricultural fallows where classified as potential forest land (for afforestation). Unproductive land refers to barren and other lands.

1*** INPUT SPECIFICATION SET NO 2 ***

GROWTH FACTORS PER YEAR FOR 6 FIVE-YEAR PERIODS:

TOTAL POPULATION 1.025 1.024 1.023 1.023 1.023 1.022

RURAL POPULATION 1.019 1.018 1.017 1.017 1.017 1.016

GROSS DOM PROD 1.000 1.000 1.000 1.000 1.000 1.000

PROD SUBSIS FOOD 1.006 1.006 1.006 1.006 1.006 1.006

PROD MARKET FOOD 1.006 1.006 1.006 1.006 1.006 1.006

The total population growth during 1981-91 was 2.5%, in this basic scenario a slight decrease in population growth, from 2.5% to 2.2% in year 2021, is assumed. The rural population growth will be 1.9% and 1.6% respectively.

In the present run the development of GDP was assumed to be stable in absence of reliable data and also due to some inconsistencies in the APM.

The agricultural productivity growth was purposely kept to the level of 1981-92 (+0.6% per year) for the whole simulation period.

ENERGY DEMAND (GIGACALORIES/CAPITA/YEAR) AND CHANGE/YEAR

RURAL POPULATION 2.68 1.00

URBAN POPULATION 2.29 1.00

The energy requirements for rural and urban population were carefully estimated using several data sources. The estimate accounts for the reported daily energy consumption and the consumption patterns like the share of commercial fuels and the biomass sources. In particular in the district the animal residues (cow dung) are generally marketed as source of income and not used for energy production. Therefore fuelwood and agricultural residues are the main sources of energy, besides a marginal share of commercial fuels (Kerosene, gas etc.), especially in urban areas.

AUTOPRODUCTION (M3/HA) BY LAND CLASSES WITH 3 SUB-CLASSES EACH:

AGRICULTURE LAND . 2 . 2 . 0

FARM FOREST LAND . 2 . 0 . 0

INDUSTR FOR LAND . 6 1.0 . 0

ENVIRON FOR LAND . 0 . 0 . 0

OTHER LAND . 2 . 2 . 1

AUTOPRODUCTION (TON/HA) OF RESIDUES ON 3 CLASSES OF AGRICULTURE LAND:

.2 .3 .0

Autoproduction refers to the production of biomass (wood or resident) suitable for energy as a by-product of the principal crop. Autoproduction includes (i) wood production of scattered trees and shrubs on agricultural and other land including forest blanks and ( ii ) production of agricultural residues suitable for energy (cotton sticks etc.). It also includes (iii) the production of biomass of natural forests and (iv) the fuelwood extracted from timber plantations.

It is difficult to give correct estimates of autoproduction since very few studies exist on the subject. Using the available literature the wood production on agricultural land was estimated at 0.2 m3/ha/year. The yield of natural forest was roughly estimated at around 1% of the biomass or 0.6 m3/ha/year. The production of residues is proportional to the crop yield and it is estimated between 0.2-0.3 tons per hectare.

Finally the priority for land use transfer were estimated as follows:

PRIORITY ORDER FOR TRANSFER OF LAND TO AGRICULTURE

1. OTHER LAND, POTENTIAL AGRICULTURE LAND

2. FARM FOREST LAND, NATURAL

3. OTHER LAND, POTENTIAL FOREST LAND

4. INDUSTRIAL FOREST LAND, NATURAL

5. ENVIRONMENTAL FOREST LAND, NATURAL

The demand for new agricultural land will be met first by culturable waste land and then by natural forests, when potential agricultural land is over.

3. Simulation results (basic scenario)

3.1 Land use changes

(all areas in thousands of hectares)

Year Area of subsistence Accumulated transfer Culturable waste land Natural forests area
1991 356 0 23 627
1992 361 5 18 627
1993 365 9 14 627
1994 370 14 9 627
1995 375 19 4 627
1996 380 24 0 626
1997 384 28 0 622
1998 389 33 0 617
1999 393 37 0 613
2000 398 42 0 608
2001 403 47 0 603
2002 407 51 0 599
2003 412 56 0 594
2004 416 60 0 590
2005 421 65 0 585
2006 425 69 0 581
2007 430 74 0 576
2008 435 79 0 571
2009 439 83 0 567
2010 444 88 0 562
2011 449 93 0 557
2012 454 98 0 552
2013 459 103 0 547
2014 464 108 0 542
2015 469 113 0 537
2016 474 118 0 532
2017 479 123 0 527
2018 480 128 0 522
2019 488 132 0 518
2020 493 137 0 513
2021 498 142 0 508

The table above shows the expected increase of agricultural land obtained from culturable waste land for the first 4 year and afterwards the demand is met by converting natural forests to agriculture. The natural forests to agriculture. The natural forests would decline to 627 000 ha in 1991 to 508 000 in year 2021 with an annual deforestation of 4000 ha.

The results are just indicative and should be used for further investigation, but are still significant to demonstrate the existing trends.

3.2 Energy Balance

The second issue of APM deals with present and projected biomass for energy balance. The balances are calculated using Teracalories (calories * 10^9) and can be converted into cubic meters of wood equivalent using a conversion factor of 2.6 gigacalories to one m3 of wood. The balance between demand and supply shows a deficit of 1 300 000 m3 of wood equivalent in 1991, rising to 3 300 000 m3 in year 2021. This gap is mainly filled by over exploitation of natural forests through illicit fellings. If the amount of wood required is taken from natural forests the degradation rate can be estimated at 2 m3 per hectare in 1991, rising to 6 m3/ha in 2021.

The projected demand and supply are as follows

All columns in teracalories except last column in million m3

                 
Year Supply Total demand Balance Balance

Million m3

  Agricultural residues Wood from agricultural land Forest yield Wood from other land Total production      
1991 599 241 1064 105 2009 5394 -3385 -1.302
1992 603 243 1064 103 2013 5523 -3510 -1.350
1993 607 245 1064 101 3017 5655 -3638 -1.399
1994 610 246 1064 100 2020 5790 -3770 -1.450
1995 614 248 1064 98 2024 5928 -3904 -1.502
1996 618 250 1063 96 2027 6070 -4043 -1.555
1997 622 252 1056 96 2026 6211 -4185 -1.610
1998 625 254 1049 96 2024 6356 -4332 -1.666
1999 629 256 1042 96 2023 6504 -4481 -1.720
2000 633 257 1034 96 2020 6656 -4636 -1.783
2001 637 259 1027 96 2019 6812 -4793 -1.843
2002 640 261 1020 96 2017 6967 -4950 -1.904
2003 644 263 1013 96 2016 7126 -5110 -1.965
2004 647 264 1006 96 2013 7288 -5275 -2.029
2005 651 266 999 96 2012 7454 -5440 -2.093
2006 622 268 992 96 2011 7624 -5613 -2.159
2007 658 270 985 96 2009 7794 -5785 -2.225
2008 662 272 977 96 2007 7969 -5962 -2.293
2009 666 274 970 96 2006 8147 -6141 -2.362
2010 670 275 962 96 2003 8329 -6326 -2.433
2011 674 277 955 96 2002 8516 -6514 -2.5.5
2012 678 279 947 96 2000 8702 -6702 -2.578
2013 682 281 939 96 1998 8893 -6895 -2.652
2014 686 283 932 96 1997 9088 -7091 -2.727
2015 690 285 924 96 1995 9288 -7293 -2.805
2016 694 287 916 96 1993 9491 -7498 -2.884
2017 697 289 908 96 1990 9694 -7740 -2.963
2018 701 291 901 96 1989 9902 -7913 -3.043
2019 705 293 893 96 1987 10114 -8127 -3.126
2020 709 295 886 96 1986 10330 -8344 -3.209
2021 713 296 878 96 1983 10551 -8566 -3.295

4. Development scenarios

4.1 Land use changes

That approach to land use changes is relatively simple. Adilabad district suffers from a chronic lack development. It has traditionally been a `forest' district with several peculiarities in socio-economic and ethnic characteristics. In the course of time the high speed of population growth has led to an increasing demand for agricultural land and in most cases the land converted into agriculture was carrying forests on medium to steep slopes. The hazards related to soil denudation and erosion as well as habitat destruction are well evident in the district.

In order to balance the growing pressure of population it is necessary to improve the yield and the production of agricultural land. Adilabad rank last among the Andhra Pradesh districts for food crops productivity with only 602 kg/ha compared to over 2300 kg in Hyderabad district. It is interesting to note that even drier zones of the State (e.g. Ananthapur and Mahabunagar districts) achieve higher yields. The soil and climate conditions are generally favourable for agriculture in Adilabad and a consistent increase of land productivity and socio-economic development should be established as urgent targets. According to the basic scenario an increase of 2.5% per year would be sufficient to balance the growing population pressure. Such increase would lead to a productivity level of slightly more than double in the next 30 years. This appears to be feasible considering that the present level is very low as shown in the next table.

District Productivity of food crops average 1991-92 (kg/h) Annual Productivity change 1981-92 (%)
HYDERABAD 2329 3.95
WEST GODAVARI 2300 -0.26
NELLORE 2141 3.71
KARIMNAGAR 2107 2.82
GUNTAR 2018 1.91
CUDDAPAH 1955 6.60
NIZAMABAD 1890 1.05
KRISHNA 1875 2.17
CHITTOR 1870 4.83
NALGONDA 1729 5.69
EAST GODAVARI 1685 -1.26
WARANGAL 1626 3.55
SRIKAKULAM 1600 2.35
VIZIANAGARAM 1585 3.30
PRAKASAM 1583 5.51
KHAMMAM 1390 5.64
ANANTHAPUR 1255 7.04
KURNOOL 1170 5.24
MEDAK 1162 1.57
RANGA REDDI 2205 3.20
VISHAKHAPATNAM 1073 1.77
MAHABUBNAGAR 922 2.91
ADILABAD 602 0.57

4.2 Energy balance

As described earlier the projection of energy balance under present conditions show a very serious negative trend. Even today the pressure on the natural forest is well evident and according to the simulation results it is estimated to become three time higher over the next 30 year. There is a considerable risk of heavy growing stock reduction and land degradation and there is an urgent need for corrective measures.

In order to estimate the effects of the possible corrective measures some alternative scenarios were formulated and tested. The development scenarios for energy balance are more complex than for land use changes. The necessary actions can be subdivided into (i) demand side; and (ii) supply side.

Among the possible scenarios the following have been tested:

DEMAND SIDE

d1. Decrease in population growth from 2.5% to 2.0 in year 2021

d2. Decrease of per capita energy consumption due to increased energy efficiency and to use of alternative energy sources. In this scenario the annual decrease of per capita demand was estimated at - 1% per year, or a reduction of 26% by year 2021

d3. Same as d2 but with a more consistent decrease in per capita demand, set to -2% per year or a reduction of 45% by year 2021

d4. Combined effect of d1 and d3

SUPPLY SIDE

s1. Improvement in wood production on agricultural land through tree planting and agro-forestry. The estimated wood production from agricultural land was expected to double from 0.15 m3/ha/year to 0.3/m3/ha/year.

s2. Development of fuelwood plantations. A total amount of 100 000 ha of fuelwood plantations established over 30 years is simulated in this scenario. The selected target is only indicative and should be formulated together with APFD, keeping in mind the financial and technical constrants. The land designated for reforestation is represented by forest blanks and from degraded (open) forests. The species to be planted should be high yielding. AMAI of 10 m3/ha was assumed, given the relative site fertility and presuming a suitable species selection, with a rotation period of 20 years and 4 coppicing cycles 1 every 5 years.

s3. Combined effect of s1 and s2.

The individual and the combined effect of each scenario were tested through successive runs of the APM program. In total 20 combination were tested and for each scenario the projected fuelwood deficit was computed. The results are tabulated thereunder in a matrix format where each cell represent the combined effect of two actions, one on the demand and one on the supply side.

Supply side

Actions

Demand side

Actions

  No change Reduction of population growth rate (1) Improvement in energy efficiency (1% per year) Improvement in energy efficiency (2% per year) (2) Combined effect of (1) and (2)
  Projected fuelwood deficit for year 2021 (figures in thousands cubic m)
No change -3296 -2986 -2172 -1404 -1281
Improvement of wood production in agriculture land (1) -3099 -2795 -2043 -1255 -1089
Fuel wood plantation development (2) -2331 -2037 -1281 -493 -323
Combined effect of (1) + (2) -2197 -1900 -1141 -352 -186

The analysis provides 4 main scenarios i.e. present conditions (00), maximum effort on the supply side only (04), maximum effort on the demand side only (30), maximum effort on both supply and demand (34).

From the table above the following consideration can be made;

- The absence of corrective measures will lead to a very severe degradation of the existing forest resources.

- Corrective measures are necessary from both sides i.e. demand and supply

- The comparative analysis of the scenarios shows that the proposed development on the demand side are the most effective. Even in absence of actions on the supply side the projected deficit is of 1.28 million m3 while which is less than the maximum result on the supply side alone (-2.20 million m3). In addition the actions on the demand side are likely to be more affordable: the targets of per capita demand reduction (-25% and -45% respectively) are within the range of the expected improved energy efficiency by use of closed chullas, coupled with the increase of alternative sources (e.g. bio-gas). According to the simulation results this is the most effective measure.

- The present output from forests is very low compared with their extent. Efforts in improving the wood production is needed to prevent further forest degradation and destruction.

5. Conclusions and recommendations

The case study of Adilabad shows the potentiality of APM as a tool for development planning. Using the standard data collected for all districts it was possible to identify the present trends for the district and to quantify the effects of some alternative scenarios. This should be useful to the planners and policy makers as a tool for decision making.

In particular the main conclusion for Adilabad can be summarized as follows:

- At present both deforestation and forest degradation are occurring in the district. Three is an urgent need of corrective measures to avoid a severe destruction of the forest resources in the near future.

- Deforestation is mainly due to the rural population growth combined with a substantial stagnation of agricultural productivity. Concerted actions between forest and agriculture department as well as other related development agencies are needed to reduce the demand of new agricultural land through improvement of agricultural crop yields.

- Forest degradation is the consequence of the energy deficit. Fuelwood is the main source of energy in the district and it is being widely used by both rural and urban population for cooking. In the absence of alternative sources fuelwood is collected by the villagers in the natural forests and also marketed to the towns. The disproportion between demand and supply is causing an unsustainable pressure on the remaining natural forests.

- While analyzing the possible actions to preserve the natural forests it was found that the most effective measures are to be taken on the demand side. The first priority is to identify and promote appropriate technologies that improve the energy efficiency. The solutions must be simple enough and well adapted to the local cultural and socio-economic environment. A considerable effort is justified in this direction since an increase of 50% in the energy efficiency over the next 30-year is essential to re-equilibrate the fuelwood balance.

-Besides the efforts on the demand side it is also necessary to intervene on the supply side. According to this preliminary simulation a target of 100 000 of fuelwood plantation is required over the next 30 years, or 3 300 ha per year.

The conclusions above are based on the analysis of a few scenarios selected by the consultant according to his experience and after discussion with forest Officers in Andhra Pradesh. The main effort here was to collect and organize data in a consistent way so that they could be used for modelling. Many other development scenarios can be built and tested using different assumptions. Moreover the APM can be applied to any other district of Andhra Pradesh. The main goal of the present case study is to stimulate the Forest Department to use APM as a tool for strategic planning.

APM has been used here at district level. The next step is to collect and organize more detailed data for running the model at mandal or forest division level. Ideally APM should be used at both levels (district and division/mandal). Data are being collected and organized to fulfill this objective.

District: ADILABAD

Average annual rainfall (mm): 1149 Area (thousand ha): 1612.8

POPULATION: (thousands)

  1981 1991 Density (sq. km) 1991 Growth 1981-91
Total 1639.0 2082.5 129 2.42
Rural 1321.9 1600.9 99 1.93
Agricultural 513.4 598.4 37 1.54
Scheduled tribes 272.9 354.9 22 2.66

FORESTS: (areas in thousands of hectares)

Closed Open Total Shift. Recorded Difference
Forests Forests Forests Cultivation Forest area Recorded-actual
488.2 161.4 649.6 0.0 723.2 73.6

LANDUSE: (areas in thousands of ha)

Geographic area Forest with trees Land without trees Net area sown Misc. tree & crops Pastures Culturable waste land Agric. fallows Barren & other land
1643.8 649.6 73.6 562.7 9.3 46.1 23.1 178.6 100.8
percent 39.5 4.5 34.2 0.6 2.8 1.4 10.9 6.1

AGRICULTURE: F00D CROPS (area in percent of total sown area)

Rice Millets Other

cereals

Total

cereals

Pulses Other

Food

crops

Total

food

crops

10.8 31.0 4.2 46.0 14.2 3.1 63.3

AGRICULTURE: NON FOOD CROPS (area in percent of total sown area)

Groundnut Seasamum Castor Coconut Cotton Tobacco Other

Non

food

Total

non

food

0.4 4.2 0.2 0.0 29.9 0.0 2.0 36.7

AGRICULTURE PRODUCTIVITY:

Average yield per ha of food grains: 527

FOOD CROPS: (yield in kg/hectare of principal crops)

Rice Wheat Maize Millets Pulses
886 1118 1566 244 221

NON FOOD CROPS: (yield in kg/hectare of principal crops)

Groundnut Seasamum Castor Sugarcane Tobacco Cotton
998 75 255 6940 1491 83

LIVESTOCK: (number of heads in thousands)

Cattle Buffaloes Total

Cattle & buffaloes

Density

Cattle & Buffaloes per sq. km

Sheeps Goats
720 161 881 55 119 197

SECTION 3: Forest Resources Assessment

by Shri S.K.Chakrabarti, ISS, Forest Survey of India, Dehradun

Introduction

The only source of assessing the growing stock in our country is the working plans at the divisional level of various state forest departments. The forest Survey of India had been conducting field inventory in the forest areas of the country ever since its inception as PISFR in 1965. PISFR has conducted field inventory in the selected areas covering 23 million ha upto 1981. After the upgradation and reorganization of PISFR as FSI in 1981, field inventory of the entire forest area of the country was included in the mandate. Since then an additional area of 45 million ha has been inventoried constituting about 80% forest area of the country. But there has been no attempt in the past for determining the growing stock of forest resources at the national level.

Methodology for Growing Stock Assessment at the Country Level

State/UT wise growing stock for the entire country (major forest strata-wise) was first assessed during 1995 as a part of National Forest Action Plan (NFAP), a project sponsored by FAO, using information available from the vegetation maps (density) thematic maps (forest types) and the ground forest inventory (average vol./ha) done by the FSI. For this purpose the maps of 1:50,000 scale of all the states and Union territories (I) density, (2) forest types and (3) average vol./ha for each combination of (density, type), were divided into grids of 2.5' x 2.5' (lat.x long). This exercise yielded information on the extent of forest cover, composition (21 species strata), density (3) and inventory data on growing stock. The following information for each grid was collected:

a. Density

The landuse category occupying more than 50% area of the grid was taken into account e.g. if more than 50% area is forested, the grid was marked as forested otherwise `non-forested'. Then in each forested grid only one major density class was taken as the density class of the entire grid. This information was collected from the vegetation maps prepared by FSI on 1:250,000 scale based on satellite imageries interpretation for the period 1989-91. Each grid was classified on either of the following three density classes;

Crown density Code

1. Very dense forest 70% and above D1

2. Dense forest 40% to 70% D2

3. Open forests 10 to 40% D3

Areas, which were having, less than 10% forest density were treated as scrub. Grids falling under non-forest and scrub were not used further. Those grids, which were spread over in more than one state, it was included in the state in which its maximum area fell.

b. Forest types/strata

The major forest type/strata in each grid was marked using information from the

following sources.

Thematic maps prepared by the FSI on 1:50,000 scale using aerial photographs were used for marking the major species composition of each grid.

For the areas for which thematic maps were not available in FSI, the information on species composition was collected from the stock maps of the State Forest Department

For the areas for which neither thematic maps nor the stock maps of State Forest Departments were available, the information on forest types was collected from the inventory field forms in which this information is recorded for each survey.

In case of areas for which none of the above sources of information was available on species composition, the information of adjoining areas was taken into consideration by taking into account same agro-ecological zone/sub-zone

The forest types obtained were:

1. Fir 11. Sal

2. Spruce 12. Bamboo (tree crop in bamboo)

3. Fir-spruce 13. Dipterocarpus

4. Blue pine 14. Khasi pine

5. Deodar 15. Khair

6. Chir-pine 16. Salai

7. Mixed conifers 17. Alpine Pastures

8. Hardwood mixed with 18. Miscellaneous

conifers

9. upland hardwoods 19. Western Ghat Evergreen

10. Teak 20. Western Ghat Semievergreen

21. Western Ghat Deciduous

c. Forest Inventory Data

Sampling Design - Forest Area

A national inventory design is followed for the whole country under which the SOI toposheet of 1:50,000 scale is divided into 2.5'x2.5' grids, each grid representing about 18-20 sq.km area. In each grid, two plots of 0.1 ha each are marked on the toposheets. The plots falling within the forest areas are inventoried. The inventory field form also gives the forest density and forest type under which the plot falls. From the inventory important parameters like growing stock, vol./ha etc. are estimated. The data of forest inventory done by the FSI in various states was used for determination of average vol./ha for each combination of (Forest density, Forest type) in each mapsheet at 1:250,000 scale.

Estimation of growing stock

In the present methodology, for estimation of the growing stock of any state, the number of grids for each combination of forest density and forest type was calculated. The areas of i.e. 1800 multiplied the number of grids ha to arrive at total area under each mapsheet for the same combination. Then the area was multiplied by the average vol./ha of the same combination. The growing stock was first estimated for each mapsheet and then summing up all the data in the mapsheets for each state, the total growing stock of each state was arrived at. Wherever the vol./ha was not available, it was borrowed from nearby areas considering the agro-ecological zones (i.e., areas falling under the same agro-ecological zone was taken into consideration).

Growing Stock

Adopting the methodology as described in the previous chapter, the Growing stock and the area under various density classes and forest stratum for various States/UT's has been worked out. The State/UT-wise details are given in Table 1 forest typewise growing stock is given in Table 2. The Growing Stock of the country is estimated to be 4740.858 million Cu. M which works out to be 74.42 Cu. M per hectare of the vegetation cover.

1:250,000 sheet No. 78 M Quadrant No. 9 (1:50,000)

Grid No. Forest
  Density Type
0000    
0100    
0200    
0300    
0400    
0500    
0001    
0101    
0201    
0301    
0401    
0501    
0002    
0102    
0202    
0302    
0402    
0502    
0003 D3 Misc.
0103 D3 Misc.
0203    
0403    
0503    
0004 D2 Misc.
0104 D2 Misc.
0204    
0304    
0404    
0404    
0005 D2 Misc.
0105 D2 Misc.
0205    
0305    
0405    
0505    

Table 1: Extent of Forest cover and Growing stock in India

           
State Growing Stock (million cu m) Vol./ha

(cu m)

  D1 D2 D3 Total  
Andhra Pradesh 33.246 178.019 80.129 291.394 61.66
Arunachal Pradesh 54.077 648.893 72.155 775.125 112.80
Assam 55.245 192.812 56.366 304.423 124.20
Bihar - 65.634 31.086 96.720 36.38
Goa, daman & Diu 5.194 7.040 0.410 12.644 101.16
Gujarat 1.281 41.352 21.288 63.921 53.07
Haryana 0.699 0.567 0.166 1.432 27.92
Himachal Pradesh 5.984 231.046 17.380 254410 203.50
Jammu & Kashmir 27.321 301.222 130.386 458.929 224.49
Karnataka 25.280 224.969 22.162 272.411 84.23
Kerala 57.013 25.069 16.801 98.883 95.67
Madhya Pradesh 19.709 573.222 105.251 98.182 51.56
Maharashtra 29.733 144.007 51.814 225.554 51.43
Manipur 39.680 19.629 36.143 95.452 54.10
Meghalaya 28.443 22.581 53.116 104.140 66.00
Mizoram 2.353 27.134 36.525 66.012 35.30
Nagaland - 42.622 52.265 94.887 66.10
Orissa - 183.737 62.413 246.150 52.21
Punjab - 0.556 0.394 0.950 7.07
Rajasthan - 6.107 6.785 12.892 9.84
Sikkim 4.533 30.499 4.254 39.286 125.9
Tamil Nadu 0.314 48.969 20.319 69.602 39.31
Tripura - 6.219 9.233 15.452 27.90
Uttar pradesh - 300.889 37.744 338.633 99.71
West Bengal * 5.427 15.830 1.602 22.859 37.68
A & N Islands* 65.450 13.797 0.397 79.644 119.62
D & N Haveli - 0.817 0.054 0.871 42.20
Chandigarh @ - - - - -
Delhi @ - - - - -
Grand Total 455.120 3359.100 926.638 4740.858 74.42
Vol./ha 128.33 95.94 36.85 74.42  
Percentage 9.6 70.85 19.55 100  

Note: *-The volume of mangrove forests has been estimated separately.

@- The growing stock is insignificant.

Table 2: Distribution of growing stock over various forest strata of India

Sl.No. Forest Strata Total Growing stock (million cu m) Percentage Vol./ha
1. Fir 153.033 3.23 373.52
2. Spruce 9.550 0.20 285.93
3 Fir -Spruce 31.215 0.66 245.59
4 Blue pine 81.175 1.71 191.5
5. Deodar 27.473 0.58 200.68
6. Chir Pine 111.960 2.36 92.67
7. Mixed Conifers 383.932 8.10 228.42
8. Hardwood mixed with conifers 47.018 0.99 93.38
9. Upland hardwoods 111.710 2.36 64.74
10. Teak 320.546 6.76 52.73
11. Sal 515.459 10.88 63.86
12. Bamboo(Tree crop in Bamboo) 36.371 0.77 37.73
13. Dipterocarpous 0.638 0.01 131.35
14 Khasi Pine 7.271 0.15 42.2
15. Khair 2.406 0.05 12.1
16. Salai 3.107 0.07 15.42
17. Alpine Pasture 0.619 0.01 99.89
18. Miscellaneous 2794.010 58.94 68.44
19. Western Ghat evergreen 47.403 1.00 119.07
20. Western Ghat 37.560 0.79 131.51
  Semi evergreen      
21. Western Ghat Deciduous 18.357 0.38 76.42
  Grand Total - 4740.858 100 74.42

For the GFRA - 2000, it is proposed to estimate the area as well as growing stock under three major forest types

a. Conifers

b. Broad leaved

c. Bamboo

The growing stock assessed in 1995 as per the above three categories is in Table 3.

Table 3: Statewise Growing Stock by Broad Forest Strata and Density

(million cu.m)

                     
State Conifers Broad Leaved Bamboo Grand
  Dense Open Total Dense Open Total Dense Open Total Total
Andhra Pradesh - - - 210.831 79.911 290.742 0.434 0.218 0.652 291.394
Arunachal Pradesh 37.148 0.751 37.899 664.184 70.874 735.058 1.638 0.530 2.168 775.125
Assam - - - 245.692 52.174 297.866 2.365 4.192 6.557 304.423
Bihar - - - 64.493 30.606 95.099 1.141 0.480 1.621 96.720
Goa,Daman & Diu - - - 12.234 0.410 12.644 - - - 12.644
Gujarat - - - 42.633 21.288 63.921 - - - 63.921
Haryana - - - 1.266 0.166 1.432 - - - 1.432
Himachal Pradesh 65.955 13.390 79.345 171.075 3.990 175.065 - - - 254.410
Jammu & Kashmir 322.416 127.24 449.65 6.127 3.148 9.275 - - - 458.929
Karnataka - - - 250.200 22.162 272.362 0.049 - 0.049 272.411
Kerela - - - 82.082 16.801 98.883 - - - 98.883
Madhya Pradesh - - - 592.931 105.251 698.182 - - - 698.182
Maharashtra - - - 173.740 51.814 225.554 - - - 225.554
Manipur 1.190 1.703 2.893 55.765 33.713 89.478 2.354 0.727 3.081 95.452
Meghalaya 0.126 0.180 0.306 47.775 44.264 92.039 3.123 8.672 11.80 104.140
Mizoram - - - 28.817 34.743 63.560 0.670 1.782 2.452 66.012
Nagaland - 0.113 0.113 41.616 52.012 93.628 1.006 0.140 1.146 94.887
Orissa - - - 183.131 62.345 245.476 0.606 0.068 0.674 246.150
Punjab 0.108 0.143 0.251 0.448 0.251 0.699 - - - 0.950
Rajasthan - - - 6.107 6.785 12.892 - - - 12.892
Sikkim 5.146 0.007 5.153 29.886 4.247 34.133 - - - 39.286
Tamil Nadu - - - 49.283 20.319 69.602 - - - 69.602
Tripura - - - 6.109 8.833 14.942 0.110 0.400 0.510 15.452
Uttar Pradesh 101.626 6.741 108.36 199.036 30.651 229.687 0.227 0.352 0.579 338.633
West Bengal 4.321 - 4.321 16.936 1.602 18.538 - - - 22.859
A & N Islands - - - 79.247 0.397 79.644 - - - 79.644
Dadra & N.Haveli - - - 0.817 0.054 0.871 - - - 0.871
Grand Total 538.03 150.26 688.30 3262.46 758.81 4021.27 13.72 17.56 31.28 4740.85
Vol/ha 169.25 99.07 146.58 93.12 32.98 69.28 40.04 28.27 32.46 74.42
Percentage w.r.t Grand Total 11.35 3.17 14.52 6.82 16.01 84.82 0.29 0.37 0.66 100

Note: In the above Table Dense = D1 + D2, Open = D3

Annual Increment

Apart from estimating the growing stock of the country, an attempt was also made, in absence of any precise estimates, to have an idea of the annual increment of India's forests by using Von Mentals' formula (I=2 GS/ r, where GS= growing stock of the forest type and r = rotation year). Since the growing stock of various states/UT s has been calculated for different forest strata and there are various species which fall in one stratum, a general period of rotation was adopted and the increment was calculated on the basis of Von Mentals' formula given in Table 3 & 4 below:

Table 4: Distribution of Annual Increment over various states/UT s of India

Sl.No. State/Uts Annual Increment

(million cu m)

1 Andhra Pradesh 5.929
2 Arunachal Pradesh 15.243
3 Assam 6.061
4 Bihar 1.715
5 Goa, daman & Diu 0.231
6 Gujarat 1.459
7 Haryana 0.027
8 Himachal Pradesh 1.603
9 Jammu & Kashmir 6.402
10 Karnataka 5.574
11 Kerela 1.983
12 Madhya Pradesh 14.122
13 Maharashtra 5.008
14 Manipur 1.889
15 Meghalaya 2.150
16 Mizoram 1.332
17 Nagaland 1.903
18 Orissa 4.432
19 Punjab 0.023
20 Rajasthan 0.292
21 Sikkim 0.767
22 Tamil Nadu 1.394
23 Tripura 0.316
24 Uttar Pradesh 5.818
25 West Bengal * 0.433
26 A & N Islands * 1.494
27 D & N Haveli 0.022
28 Chandigarh @ -
29 Delhi @ -
  Grand Total 87.622

Table 5: Distribution of Annual Increment over various forest strata of India

Sl.No Forest Strata Annual Increment (million cu m)
1 Fir 1.722
2 Spruce 0.105
3 Fir-spruce 0.347
4 Blue pine 1.083
5 Deodar 0.366
6 Chir-pine 1.865
7 Mixed conifers 3.850
8 Hardwood mixed with conifers 0.783
9 Upland hardwoods 1.862
10 Teak 8.013
11 Sal 8.591
12 Bamboo (tree crop in bamboo) 0.909
13 Dipterocarpus 0.011
14 Khasi pine 0.181
15 Khair 0.080
16 Salai 0.104
17 Miscellaneous 55.876
18 Western Ghat Evergreen 0.790
19 Western Ghat Semievergreen 0.625
20 Western Ghat Deciduous 0.459
  Grand Total 87.622

Followup of the last workshop of FAO-FSI

During the October workshop, this methodology was discussed in detail. It was agreed that the same methodology will be followed for the current assessment of forest cover i.e. SFR-1997 (based on satellite imagery of 1994-95) so that a picture of degradation of forest may emerge by comparing statewise and countrywise growing stock based on SFR-1993 and SFR-1997.

The work of assessment of the country is in progress. As a pilot study, the growing stock of one state i.e. Manipur has been completed based on same volume factors, which was used in 1995 assessment. The following tables 5 & 6 give a picture of the area of growing stock figures for two assessments.

Table 6: Comparison of Forest area in Manipur based on two assessments of SFR-1993 (satellite imagery of 1989-91) and SFR-1997 (satellite imagery of 1994-95)

(000 ha)

                   
  1993 assessment 1997 assessment
Sl. Forest Density Density
No. Type D1 D2 D3 Total D1 D2 D3 Total
1 Chir pine 2.2 0 1.7 3.9 2.6 0 1.6 4.2
2 HW mixed with conifers 0 0 29.9 29.9 0 2.6 27.1 29.7
3 Upland HW 22.1 27.9 67.0 117.0 31.4 34.0 65.3 1307
4 Sal 1.6 0 0 1.6 2.6 0 0 2.6
5 Depterocarpus 3.9 0 1.3 5.2 5.2 0 1.6 6.8
6 Bamboo 8.6 11.8 49.3 69.7 0 0 63.8 63.8
7 Khasi pine 1.9 6.0 24.8 32.7 2.6 7.8 19.1 29.5
8 Miscellaneous 237.9 206.8 1057.4 1502.1 185.5 219.4 1069.6 1474.5
    278.2 252.5 1231.4 1762.1 229.9 263.8 1248.1 1741.8

Table 7: Comparison of Growing Stock in Manipur based on two assessments of SFR-1993 (satellite imagery of 1989-91) and SFR-1997 (satellite imagery of 1994-95)

(million cu.m)

                   
  1993 assessment 1997 assessment
Sl. Forest Growing stock Growing stock
No. Type D1 D2 D3 Total D1 D2 D3 Total
1 Chir pine 0.371 0 0.045 0.416 0.434 0 0.039 0.473
2 HW mixed with conifers 0 0   0.723 0 0.200 0.665 0.865
3 Upland HW 6.243 2.362 2.269 10.874 8.832 2.877 1.776 13.485
4 Sal 0.260 0 0 0.260 0.434 0 0 0.434
5 Depterocarpus 0.651 0 0.032 0.683 0.869 0 0.201 1.070
6 Bamboo 1.427 0.927 0.727 3.081 0 0 0.942 0.942
7 Khasi pine 0.312 0.507 1.036 1.855 0.434 0.666 0.797 1.897
8 Miscellaneous 30.416 15.833 31.311 77.560 23.805 16.798 31.667 72.270
  Total 39.680 19.629 36.143 95.452 34.808 20.541 36.087 91.436
    Overall vol/ha 54.17 Overall vol./ha 52.5

SECTION 4: Workshop on country capacity building in Forest Resources Assessment in South Asian Countries

by Ishtiaq Uddin Ahmad, Deputy Conservator of Forests

1. Introduction

Forest assessment is done to capture information on various parameters which influences forest growth and management. This information base thus created helps in deciding adoption of management's options like silvicultural regime, tending operations, logging, transport or the like. Today forest management embraces, more than just stand information, which includes environment and social parameters. In context to tropical forestry in particular one can not think of forestry in isolation of the people or avoiding the issues relating to environments. Integration of these information into the forest management plan is very important. The idea is to bring about harmonized and balanced management planning towards participatory approach. In such planning, one must, on one hand takes into consideration the traditional forestry production targets, on the other hand, one also considers peoples involvement and social impacts.

So, forest management at present context needs a holistic approach. Timely and quickly data acquisition is important. Remote sensing now a days integrated with Geographic Information System (GIS) is a useful tool to face this problem. A wide variety of sensors is available with different spectral characteristics, geometric resolutions and periodicity. The advent of computer has revolutionized the databased management system. The combination of remotely sensed data and GIS gives not only an actrual images of the earth surface but may provide also a detailed base for planning management.

2.1 Historical background

In this part of the world first methodical inventory was done in Sundarban (Mangrove) forest in 1930 by a British Forester Mr. Curtis. He established transect lines all over the Sundarban forest. Finally he produced a forest cover map at a scale of 1:63360. After 1930 the forest was again inventoried in 1958 by Canadian forestal forestry. Aerial photographs were used in this inventory. Along with the stock and stand table a forest cover map of 1:15,840 was developed. This inventory was mainly to study the feasibility of establishing a newsprint mill be using Excoecaeria agallocha (Gewa) wood. The present news print mill at Khulna is the outcome of the 1958 inventory proposal. Forest department developed a working plan for a period of 10 years by using the result of that inventory.

Almost at the same time the Forestal Forestry of Canada undertook the inventory of the Chittagong Hill Tracts region which totals 0.67 million ha of forest land. Detailed forest composition had been interpreted and forest cover map at a scale of 1:15840 was developed based on aerial photographs of 1:15,000 (approx). A contour map and a land classification map were also developed for the same region.

After that, with the financial assistance from the UNDP/FAO the Forest Department with the collaboration of FAO experts carried out inventories of different forest areas including a countrywide inventory of village forests to assess the village wood resources of the country. The inventories under the auspices of UNDP/FAO helped to build the country capability in the field of inventory by improving the skill of the manpower and logistics. For the first time in Bangladesh the status of the village wood resources has been determined through a detailed inventory in 1979. The inventory helped to determine the quantum of wood the village forest provided to the people beside the wood resource comes from states owned forest lands. This gave an opportunity to undertake different developmental programmes to improve the village wood resources of the country.

Cluster plot sampling has been followed so as to capture changing conditions within a stand/landscape unit. A cluster of 5 plots in a cross arrangement oriented along the cardinal directions with a centre plot and the other four plots situated 100 m (in some cases 50 m ) north, east, south and west of the centre plot, respectively. Each plot in a cluster consists of a few sub-plots.

2.2.2 Forest Resources Statistics & Database

Computer based data entry and validation program and field data processing

program were developed specially for this inventory. Data processing to generate the required statistics and other information needed in integrated forest management planning have been developed.

The final inventory report includes detailed stand and stock tables for trees by DBH class, species area and volume per hectare and for the whole Division as well as by stratum, forest type, compartment, block and range. The detailed statistics on seedlings saplings and poles as well as nypa and bamboo are also included.

2.2.3 Other data base

At the same time socio-economic survey has been conducted in the target forest divisions. This survey is to determine peoples attitude towards forestry, demand of forest produce, dimension of demand, number of population around forest reserves, rate of deforestation, rate of encroachment etc. These are all to develop an integrated management to arrest further degradation of the forest resources.

Besides socio-economic survey special inventory has also been conducted for the conservation areas. This survey has been conducted to determine further candidate areas to qualify to be declared as protected areas. Through this survey a data base has been developed on different parameters showing status of flora and fauna.

Following that, another UNDP/FAO forestry project along with a strong component to carryout inventory of the Hill forest & coastal plantation was launched in 1981. That project was entirely implemented by the working plans division of the forest department with the expert assistance from FAO. The inventory of these forests were carried out by using aerial photographs and SPOT satellite image in the area where photographs were not available. In 1984 Sundarban forest was again inventoried with the assistance from British ODA by the help of aerial photographs.

2.2 Forest Inventory of Recent Time

Forest department from 1995 to 1997 carried out a massive inventory program in the Hill & Coastal forest areas including Sunderban Reserved Forests under a loan agreement with International Development Agency. The inventory was carried out for forest resources management planning purposes.

2.2.1 Objectives and design of the inventory

The primary objectives of the inventory are threefold. The first to generate

information on the status of the forests of the target forest divisions and the second primary objectives is to provide "abstract" time series data for yield modelling purposes. The third major objectives is to set up a system of hidden recurrent same plots for continuous monitoring and assessment of change in the target forest areas. Such information will be used for forest resources management planning purposes taking into account not only the timber/wood resources but also other important resources like bamboo, rattan, nipa, medicinal plants, wildlife and tourism as well as social and environmental considerations.

Systematic sampling is used in all the target divisions. The sample plots were laid following a grid system. In the hill forest grid interval of 40" by 40" is used mostly in combination with smaller grids 20" or 10" to cover required number of plot clusters to attain the target precision of estimates. In the case of Sundarbans, a systematic sample of one minute by one minute grid is used. The standard error of the estimated total volume of trees in each of the management units should not exceed 5%.

Database and associated tabular database is entered into the computer, it can be used as tool for management purposes such as production of thematic maps, area calculation visualization of different scenarios etc.

2.2.4 Continuous Forest Assessment

3. Resource Information Management System

3.1 The Management System & GIS

This system also should integrate the functionality of the previous RIMS tabular database program and of the Environmental Information Management System (EIMS) and socio-economic data, which is also based on tabular data.

Main components for data input are the forest inventory, the aerial photo interpretation and various map sources (e.g. forest cover maps, topographic maps, maps showing administrative boundaries, thematic maps etc.). On the other side, the system delivers outputs for those components, e.g. support for the stratification in the forest inventory, base map for transfer of updated photo interpretation results etc.).

The directives for RIMS-GIS have to come from the FD management, whereby the term `directives' under these circumstances stands mainly for general management rules and prescriptions.

· The General concept & interaction of RIMS-GUI.

The RIMS-GUI was programmed to fulfill the following tasks and outputs:

· Flexible use of the geographic data bases (e.g.enable overlays, intersections, buffering etc. of any spatial data base with any other spatial data base available in the system)

· Easy-to-use combination of geographic data and tabular data

· Allow selection of areas of interest (reporting or planning areas) in several ways:

· Based on administrative boundaries (e.g. Division, Range, Beat).

· Based on free-hand polygons defined by the user (simply by indicating the area of interest in a map on the screen).

· Enable data entry and data editing.

· Allow output of maps, table, charts and summarizing statistics.

· Allow scenarios and predictions.

· Easy-to-use updating/editing of necessary inputs as volume functions, yield tables and management prescriptions.

· Easy-to-understand graphical user interface.

·

4. Application of management information and planning system

· Area size of different landuse, forest types, number of trees by species

· Silvicultural treatment

· Forest inventories for management purposes

· Socio economic data on settlements within or near Forest Reserves

· Environmentally sensitive information

Rims- GUI is a graphical user interface for RIMS-GIS. It is to facilitate the handling and analysis of above mentioned data base.

Integrated in RIMS-GIS are also programs for :

· Selection of area of interest

· Intersection (overlaying) of different spatial data sets.

· Loading of event themes (e.g. sample plot data, which can be georeferenced)

· Generation of buffers around polygons, lines or points

· Determination of co-ordinates of points of different projection system

· Generation of grids and graticules for map production

· Generation of latitude / longitude point grids (e.g. for planning of sample grids)

· Area calculation

· Volume calculation

· Editing yield tables and management prescriptions

· Generation of maps using standardized formats customized for the need of the Forest Department

· Generation of reports

The system helps to reach a decision about the location of a new plantation, that is located on a suitable soil and is close to a population centre. The information can be presented in a shortest possible time in a form of map, tabular data and graphs. The greatest advantage is the capability to evaluate multiple scenarios or options efficiently and effectively

5. Conclusion

The growing importance of forestry demands quick appraisal of forest status, people socio-economic condition and the parameter responsible for the environmental degradation. So forest management at present context needs a holistic approach. For that it is necessary to handle on multiple scenarios together to see their affect individually or interactively.

GIS has already proved to be an efficient management too particularly for the planners and decision makers. RIMS-GIS has been established in the Forest Department of Bangladesh with a great expectation and hope that a dynamism can be brought in the field of forest management of the country.

SECTION 5: CCB - FRA 2000 - The Biodiversity Assessment

by M.Lorenzini, FAO

According to the United Nations Convention on Biodiversity

"biological diversity refers to the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems".

Developing a methodology, to be applied on a global basis and based on the gap analysis experience, requires an accurate definition of the goals and, subsequently, the tools which can lead to the achievement of the same goals.

Scope of the document

The document presented by the author contains the outline of a methodology aimed at organizing a number of indicators, some of them recursively mentioned in earlier CCB-FRA2000 documents, with a "view" of the Biological conservation issue.

It is the author's feeling that, inspite of the many efforts to build-up an organic/systematic proposal clarifying CCB-FRA2000's objectives in the assessment of biological diversity, definitions, roles and integration of various proposed indicators have as of yet not been achieved.

In view of this need and on the basis of past experiences a number of publications were reviewed by the author related to the Gap Analysis, since its conceptual framework is very close to how we foresee the structure of an Ecosystem Report, Gap Analysis was developed in the early 80s and it has been adopted in a number of countries (USA, Australia, etc.) to support conservation programs.

Being the assessment of forest area and deforestation over the last decade the primary goal of FRA2000, the question to which FRA2000 will try to answer is:

"how will the current status and the changes in land-use, measured during the assessment, likely to affect the biological diversity associated to the forest ecosystems"

Adopting Gap Analysis to CCB-FRA needs

Developing a methodology, to be applied on a global basis and based on the gap analysis experience, requires an accurate definition of the goals and, subsequently, the tools which can lead to the achievement of the same goals.

The CCB-FRA assessment of biodiversity can be implemented adapting the Burley's approach to the actual FRA targets only if we take into account the major differences between the framework (biological conservation) within which gap analysis and related information were developed, and a realistic vision of what should be the main objective of the CCB-FRA assessment, say the identification of critical zones in terms of nature/mankind interaction, and the kind of information which can be generated on a global basis useful to achieve the goal.

a combination of the two datasets should serve to classify and identify the landscape/biological elements to be analysed.

The analysis of the status of forest ecosystems, carried out with the use of geographic information system (GIS), relies on a number of basic layers:

Landscape/Biological elements

Combining the Eco-floristic zones and Vegetation types maps will lead to a number of unique combinations of actual vs. potential land-cover conditions. EFZs and vegetation maps can be seen as the first two levels (ecosystem and community) of the hierarchy:

Ecosystem - EFZ Ia & IIa

Community - Dense almost primary forests

Guild - Dipterocarpus forest

Species - Dipterocarpus spp.

Organism - -

Gene - -

Following is a brief description of the information content of the two maps :

· Eco-floristic Zones

This data-set integrates ecological with the floristic information. The parameters applied in the ecological classification are ; climatic, physiographic, and edaphic. Climatic parameters include mean annual rainfall, rainfall regime, length of the dry season, relative humidity and temperature. The physiographic contours and the soils map are used to further subdivide the bio-climatic zones. Within the major ecological regions thus established, floristic zones are defined using the dominant or characteristics woody species of the flora, with attention given to their successional patterns.

Eco-regions have been mapped with a consistent methodology across all regions. Maps exist for Africa, Continental South and Southeast Asia, Insular Asia and South America. (Mexico and the countries of Central America are not covered). The floristic zones are continent specific.

· Vegetation cover/condition

The vegetation cover/condition have been defined by using remote sensing images and existing country maps. They are expressions of vegetational characteristics such as density, continuity of plant cover, height, etc. and human impact on the vegetation. The legend includes classes like dense forests, secondary forests, woodlands, thickets, savanna. The vegetation map together with eco-floristic zones, may be used to map the different stages of the regressive series or, in very rare cases, the evolutionary tendency of the vegetation within a zone.

FRA 2000 goal is not so much related to define conservation strategies but to depict the status of forest ecosystems. In view of this, ancillary information have to be integrated with the primary elements described earlier in order to build up a complete overview of the state of the ecosystems.

Due to time and financial constraints potential contributors will be extracted from the current FRA data holdings:

· Population time-series at sub-national level (+ related administrative units map);

· Statistics about deforestation at sub-national level (+ related administrative units map);

· GIS layers related to urban settlements and other human infra-structures (road/railroads);

· A product called accessibility map derived from the combination of the above mentioned layers and DTM;

· The first two parameters, demography and forestry statistics, can be associated to each landscape/biological element on the basis of the areas or fraction of areas of the administrative units reporting such statistics. Data are stored in a statistical database named FORIS: Forest Resources Information System.

· A second group of statistics, associated to each landscape/biological element, is derived from spatial database reflecting, according to existing cartography, the spatial patterns of people distribution and forest disturbance preferential paths on the basis of urban settlements, roads and topography. Each landscape/biological element is hence further described in terms of areas under various levels of likely disturbance (high, medium, low no risk).

Management Status

A world-wide overview of the status of conservation of biological diversity can be obtained using a global database developed by the World Conservation Monitoring Center (WCMC) which, sponsored by the International Union for Conservation of Nature (IUCN) has been collecting, over the past decade, georeferenced information related to protected areas. These are classified according to the IUCN definitions of :

I Strict nature reserve/wilderness area. Protected area managed mainly for science or wilderness protection.

II National Park. Protected area managed mainly for ecosystem protection and recreation.

III Natural monument. Protected area managed mainly for conservation of specific natural features.

IV Habitat/species management area. Protected area managed mainly for conservation through management intervention.

V Protected landscape/seascape. Protected areas managed mainly for landscape/seascape conservation and recreation.

VI Managed resource protection area. Protected area managed for the sustainable use of natural ecosystems.

Analysis and expected output

A major function of gap analysis is to present, in tabular as well spatial format, statistics evaluating the current status of each landscape/biological element in terms of past/present degradation processes and its current level of protection. Aggregating landscape/biological elements into higher hierarchical levels, say EFZs or Vegetation types, or rearranging statistics on a national and/or sub-national level will allow to generate information which will serve us the precursor to additional analysis designed to in-depth studies for specific areas.

The biodiversity spatial/statistical information system relies on a number of integrated data sources, which, as detailed in the previous chapter, will enable analysts to perform complex queries.

On the basis of such information it will be feasible to select subsets of the main database with critical features e.g.:

The Ecosystem report

Following the proposed guidelines the ecosystem report will become an information system including maps and linked statistics. The report will be fully documented in each of its components (both spatial and statistical database). Worked-out examples will show methods to

Additional studies : Study of species-richness

The study will be performed using FRA 1990 approach. The main steps in assessing the risk of biological diversity loss will be the following:

SECTION 6: Forest Cover Change - Assessment Methodology

by Sandeep Tripathi & P.K.Pathak

OBJECTIVES

· BIENNIAL FOREST COVER ASSESSMENT OF INDIA

· TOTAL GEOGRAPHICAL AREA OF INDIA: 328 million ha.

· TOTAL FOREST COVER:63.34 million ha.(19.26% OF GEOGRAPHICAL AREA)

· VEGETATION MAPS AT 1:250000 SCALE

DETAILS OF SATELLITE DATA

· DATA PERIOD : OCTOBER - NOVEMBER

· DATA SOURCE : NATIONAL REMOTE SENSING AGENCY

· SENSOR : IRS - IB (LISS-II)

· RESOLUTION : 36.25 m

· MEDIA : HARD COPY /

COMPUTER COMPITABLE TAPES (CCT)

I.VISUAL INTERPRETATION

· DATA PROCUREMENT IN HARD COPY SATELLITE IMAGERY

· DRAWING OF BASE MAP ON1:250000 SCALE

· VISUAL INTERPRETATION OF IMAGERY

· COMPARISION WITH PREVIOUS CYCLE VEGETATION MAP

· GROUND VERIFICATION

· DISTRICT WISE AREA ESTIMATION

II. DIGITAL INTERPRETATION

VAX-11/780 DIGITAL IMAGE PROCESSING SYSTEM HARDWARE:

-3 GRAPHIC WORKSTATION

-5 ALPHA NUMERIC TERMINALS

-A0 SIZE DIGITISER

SOFTWARE:

VIPS-32

DIP STEPS

· A. IMAGE PREPROCESSING

· B. CLASSIFICATION

· C. AREA CALCULATION

· D. GROUND TRUTHING & MAP PLOTTING

IMAGE PREPROCESSING

1. RADIOMETRIC CORRECTION:

CARRIED OUT AT NRSA

2. GEOMETRIC CORRECTION:

IMAGE TO MAP RECTIFICATION SELECTING GCP

3. NORMALISED DIFFERENCE VEGETATION INDEX (NDVI)

APPLICAION OF NDVI ON FCC

CLASSIFICATION

· MASKING OF FOREST AREAS INTERACTIVELY

· EXTRACTION OF MASKED FOREST AREAS FROM NDVI IMAGE

· DENSITY SLICING

· SHADOW EXTRACTION

· MOSAICING THE CLASSIFIED IMAGE

· SHEET EXTRACTION AT 1:250,000 SCALE

AREA CALCULATION

· OVERLAYING CLASSIFIED IMAGE ON DISTRICT BOUNDARIES

· IMAGE TO IMAGE RECTIFICATION

· DISTRICT WISE AREA ESTIMATION OF DIFFERENT FOREST CLASSES

GROUND TRUTHING & MAP PLOTTING

· POST CLASSIFICATION GROUND TRUTHING

· POST GROUND TRUTHING CORRECTION

· PLOTTING OF FINAL MAP THROUGH VERSATEC ELECTROSTATIC PLOTTER

FUTURE PROSPECTS

· VEGETATION MAPPING-SWITCH OVER TO DIGITAL MODE

· UPGRADATION OF DIP CAPABILITIES

· ACQUISITION OF HIGH RESOLUTION DATA

· FUSION OF HIFH & COARSE RESOLUTION DATA

· ACQUISITION & DIGITIZATION OF DIVISION ADMINISTRATIVE/FOREST BLOCK BOUNDARIES

· G.I.S. DATABASE

· FOREST ATLAS

SECTION 7: Estimation of forest cover for standard reference years using a modelling approach

Estracted from: FAO Forest Resources Assessment, Forest Resources Information System (FORIS)

Concepts and methodologies for estimating forest state and change using existing information

Report prepared by Walter Antonio Marzoli, Forest Biometrician/Ecologist

Country Capacity Building Project, FAO-Forest Resources Assessment Programme

ESTIMATION OF FOREST COVER FOR STANDARD REFERENCES YEARS FOR TROPICAL COUNTRIES

The technique of assessment consists of the following three steps (i) establishment of a computerised database, (ii) development of deforestation model (or and adjustment function), and (iii) computation of results for the standard reference years.

1. Establishment of a database

The Forest Resources Assessment 1990 was not intended to reproduce the available official statistics but to make an objective appraisal of the current state of forest resources and of the rates of changes, following a standard methodology and classification system.

The data compiled by the Project consists of three categories;

1.1. Tabular data:

· area of land use and vegetation types (if possible multi-date observations),

· forest function, land ownership and legal status,

· forest management and forest plantation,

· forest volume, biomass and production,

· forest harvesting and utilization

The Guidelines for assessment based on existing data were prepared in the three official FAO languages to compile the country statistics and bring them in a framework of common concepts and classification. Using the guidelines, the country reports were reviewed and reliable information extracted, edited and stored as part of the Project database with the acronym FORIS which stands for Forest Resources Information System.

A special effort was made to collect information on forest cover area and its change in time. The data sources are very heterogeneous generally, and include recent and past forest inventories and maps based on different techniques (aerial photographs, satellite imagery and ground inventories). The data collected for each country will be re-appraised in the standard categories defined earlier for land use and forest types.

The following adjustments may be necessary:

(i) Conceptual adjustments: the classification system adopted in the existing data may not correspond to the standard classification. Basically, two types of adjustment may be necessary,

(a) Original classes must be grouped into one single broader category. This is the case for example when data are available by ecological zones within one forest formation (e.g. humid, moist, dry, lowland, mountain forests, etc. within broad-leaved closed forests). In this case no particular problems arise. However particular attention should be paid to the distinction between closed and open formations.

(b) One original category needs to be subdivided into two or more standard categories (i.e. inverse situation). This case is more difficult to handle and a combination of indirect methods and sound judgement has to be used depending upon the quality and quantity of information available.

(ii) Area adjustments. One of the following could arise in assessing forest area for the reference dates,

(a) Complete forest inventories or forest maps available.

If complete national forest inventory or map coverage is available, then the procedure of reappraising data into the standard classification described earlier should be used.

(b) Incomplete or missing forest inventory

If no forest inventory or forest map is available or if the country coverage is partial then area assessment will have to be carried out using existing vegetation maps. A description of the techniques used to handle missing inventory data is described in section

The tabular information was compiled for most of the countries at sub-national level (district, province, and state) and the original inventory data were incorporated in the FORIS database together with description of the sources and of the classification used. This database will be continuously updated as new information becomes available and constitutes the basis for estimating the state and change of the tropical forest resources.

1.2. Map data:

It includes vegetation types, ecofloristic zones and country and subnational boundaries.

Realising that deforestation is a location-specific process driven by, among other things, population pressure and environmental conditions (particularly the population carrying capacity of the area), demographic and ecological parameters were included in the database and integrated with the statistical data in the form of a GIS. From the outset it was realised that a GIS was essential to handle statistical and spatial data from a variety of sources, in a variety of formats and in different projections. Therefore, GIS development was given special attention by the Project.

In the course of time, more spatial data were added to the Project GIS including LANDSAT 1.5 Wold Reference System Grid, potential global and forest biomass, mean annual precipitation and biotemperature, topography, protected areas, and vegetation map from NOAA/AVHRR satellite imagery (for some regions).

1.3. Remote sensing data

A remote sensing based survey of the tropical forests using high resolution

Satellite data of two dates, one close to 1980 and the other close to 1990, was implemented at 117 sample locations. State and change information was interpreted and stored in the form of change matrices following the standard procedure developed. These data were analysed to obtained in-depth knowledge of the process of deforestation and forest degradation on the sample locations, by geographic sub-regions and ecological zone. As mentioned earlier the results of the sample survey will be presented in a separate paper. For the present study the forest cover change data derived from remote sensing have been used to perform a local calibration of the model parameters for Africa where the existing multi-date data were very scarce.

2. The Deforestation Model

2.1. Model building

The collected data on forest cover area have different reference year and need to be brought for reporting purposes to the standard years, namely 1980 and 1990. This was done with help of deforestation model (or forest area adjustment function) which correlates the forest cover change in time with ancillary variables by means of regression analysis. Aim of the model approach were (i) to adjust the sub-national units inventory data to 1980 and 1990, (ii) to provide a methodology to correctly expand to all units the available forest area change data, and (iii) to develop the understanding of the causes and of the dynamics of the deforestation process.

Model building: Step 1

The first step was to study the correlation between forest cover and some explanatory variables. Population density showed a significant correlation with forest cover as expected since deforestation is one of the effects of man-environment interaction Moreover the use of population density was interesting since population can be a substitute for time, so that units with different baseline years can be processed together by substituting population to the X variable.

A number of simple regression were performed with data of the same country or of different countries and years using cross-sectional analysis. The results of the regression analysis were encouraging and two major conclusions could be drawn: (I) In terms of mathematical formulation the transformation of population density into its logarithm gave the best fits; and (ii) in terms of the general shape of the curve an S-shaped curve concave in the early stages of development with low population (see the graph for Brazil on the next page) and convex at higher levels of population (see example for Thailand) seemed to describe the general trend.

SECTION 8: Forest Fire - Problems related to the assessment of forest area -The country perspective

by Ranjana Gupta, Dy. Director, Forest Survey of India

The ancients regarded fire, along with air, water, and land, as a basic element of the universe. Fire exerts a powerful fascination. An instinctive awareness and fear of it are deeply ingrained in mankind. Wild fires have covered large areas in the tropics. Next to climate and soil, fire has been the most important single factor affecting the extent, composition, and character of forest and other vegetative cover on wildlands. A bad master, fire is also a useful servant. Carefully and selectively used on a prescribed basis it can perform many useful tasks in forest management.

DEFINITION AND NATURE OF FOREST FIRES

Fire as general term, is applied to the common physical phenomenon resulting from the rapid combination of oxygen with a substance and is characterized by heat, light, and usually flame. A forest fire is unenclosed and freely spreading combustion which consumes the natural fuels of forest i.e. duff, frass, weeds, brush, and trees.

CAUSES OF FOREST FIRES

Fires may be natural or man made

The fires caused by man can be divided into classes-

(i) Unintentionally

(ii) Deliberately and intentionally.

1. Natural causes- may be lightning, rolling stones or rubbing of dry bamboos with each other. The number of fires that may be caused by all the three natural causes are not more than 5% of the total number of fires caused in any one year.

2. Man's Carelessness- About 95% of the fires in this country are caused by man.

(i) The accidental fires may be due to following causes-

(a) Smokers - Throwing of burning match stick or bidi or cigarettes stumps in the forest by graziers or other travellers.

(b) Campfire - Includes forest fire originating from fires built for cooking by cartman or travellers.

(c) Rail ways- throwing of glowing coal pieces by trains speeding through the forest.

(d) Debris burning- (Clearing)- Including forest fires which originate from fires set for clearing land for purposes like control burning, cultivation or pastoral purposes, disposal of such fires spread on ground.

(e) Illicit collection of forest produce- Throwing of fire carelessly ater collection of honey etc. During course of Mahua flower collection also people burn the area to make the surface clean.

(f) Control burning of fire line

(g) Control burning of grasses

(h) Falling of dry needles or leaves on electric poles giving out sparks.

(i) Throwing of torch wiid by villagers while passing through the forest in the night

(2) Those caused by man deliberately

(a) Burning of undergrowth and grass to collect MPF.

(b) Including new shoots of grass in summer by burning dry grass.

(c) Personal enmity of one wing to other wing of deptt.

(d) By kendu leaf staff during bush cutting (during course of bush cutting they used to burn the area instead of bush cutting to save money as well as getting flush.

(e) By villages aggrieved with forest official.

(f) During destroying or charring the stumps of illicitly felled trees in the forest.

CLASSIFICATION OF FOREST FIRES

(a) On the basis of causative factors-

(1) Natural fires,

(2) Accidental fires,

(3) Deliberate or intentional fires.

(b) On the basis of the place of their action-

(1) Creeping fire - is defined as a forest fire spreading slowly over the ground with low flame.

(2) Ground fire - is defined as a forest fire that burns the ground cover only i.e. the carpet of herbaceous plants and low shrubs, which cover the soil. E.g. in deodar forests as a results of slash disposal when it burns inside the humus for days without gice out flames and then causes a huge conflagration.

(3) Surface fire - is defined as forest fire which burns not merely the ground cover but also undergrowth. e.g. the fires in the plains

(4) Crown fire - is defined as a forest fire which spreads through the crowns of trees and consumes all or part of the upper branches and foliage, e.g. in coniferous forest. The above classes of fires are not exclusive, and independent of each other.

EXTENT OF FIRE INCIDENCE IN FOREST AREAS OF THE COUNTRY

                   
State Forest Sample Extent of fire incidence Total
  Area Plots Very Heavy Heavy Frequent Occasional No fire Unrec.  
Arunachal Pradesh 14826.71 2037 60.58 5.75 521.99 3335.27 10016.34 886.78 14826.71
Assam 15427.8 2462 70.91 0 590.25 4551.13 10176.68 38.91 15427.88
Bihar 5347.01 296 57.718 0 452.6223 3330.7426 1505.927 0 5347.01
Himachal Pradesh 10269.4 4878 163.7 0 671.45 3811.36 5054.92 567.98 10269.41
Jammu & Kashmir 3331.75 428 7.5 0 60.98 1089.58 2088.05 85.64 3331.75
Haryana & Punjab 1180.72 145 0 0 41.54 332.48 806.7 0 1180.72
Karnataka 13223.3 1780 59.71 30.33 470.64 3342.94 9309.79 9.89 13223.3
Manipur 15154 1880 0 151.54 454.62 5758.52 8789.32 0 15154
Madhya Pradesh 19625.91 1947 136.53 23.07 1838.83 10644.29 6983.19 0 19625.91
Maharashtra 8165.54 1304 0 0 186.03 4222.57   0 8165.54
Meghalaya 9905.65 1659 26.75 0 269.12 3347.25 5230.91 1031.6 9905.65
Nagaland 14954.91 1128 0 0 1084.231 12038.703 1831.976 0 14954.91
Orissa 20143.36 2972 204.42 78.5 923.19 11345.548 7258.182 333.52 20143.36
Rajasthan                  
Sikkim                  
Tripura                  
Uttar Pradesh                  
West Bengal                  

Dadra & Nagar

                 
Grand Total                  
Percentage                  

SECTION 9: Tool for assessment of forest biological diversity

by Claire Elouard & Rani M.Krishnan, French Institute of Pondicherry

FAO-GOI Regional Project on " The status of Forest Resources Assessment in the South Asia sub-region & Country Capacity Building needs"

Introduction

Biodiversity can be assessed both at the landscape level and at the species level. Vegetation types are defined according to their species composition, in accordance with the bioclimatic conditions. Assessing biodiversity at the species level helps characterizing the stand studied as well as can be used as part of the factors to be taken into account for the identification of high conservation value areas which areas which need protection.

The study presented below was part of a FAO Training Programme entitled "Assessment of forest biological diversity". Conducted at the French Institute of Pondicherry in 1996. This Programme aimed at training foresters from three South-East Asian countries, Vietnam, Laos and Cambodia, to diversity assessment. Part of the programme was theoretical and field-work was conducted to apply the methods presented for assessing diversity.

The data used in this paper have been collected during the field-work with the aim of learning the manipulation of some tools for biodiversity assessment. Due to the limited time allocated to the field-work, only a small number of plots were laid. The results cannot therefore be used as a reference to characterize the forest types studied.

Material and Methods

Study area

The field work and data collection were undertaken in three forest types, e.g., evergreen, moist deciduous and dry deciduous forests, in Karnataka State, South India. The evergreen forest type is located in the Makut Reserve Forest, Kodagu (Coorg) District; the moist deciduous and dry deciduous forests are located in the Bandipur National Park, Mysore District.

The medium elevation evergreen forest studied belongs to the Mesua ferrea-Palaquium ellipticum type, as defined by Pascal (1984). The moist deciduous forest belongs to the Lagerstroemia microcarpa-Tectona grandis-Dillenia pentagyna type and the dry deciduous forest belongs to the Anogeissus latifolia-Pterocarpus marsupium-Terminalla spp. Type (Pascal 1984).

Data collection

The data presented below were collected using the following method, which consists of a cluster of 20x20 m plots.

Using compass, a square transect is established and measurement points are made every 20 m interval in evergreen forest and every 50 m in deciduous forest where variability and density are less. The increment of distance in between two plots allows then to capture higher species diversity. These measurements concern only the boundary of the square established. At each of the measurement points, a 20x20-m plot is established with the following measurements:

- The 20x20 m plot laid is divided into 10x10 m subplots ;

- All individuals with gbh > 30 cm (trees) are located (co-ordinates x and y) within the 20x20 m plot and measured for height and girth;

- All individuals with 30 >gbh >3 cm (saplings) are located within one 10x10 m subplot and measured for height and girth;

- In the same subplot, a smaller plot measuring 5x5 is laid and all individuals with gbh < 3 cm (seedlings) are measured for the height; heights are classified into intervals of 20 cm;

- Within this 5x5 m subplot, the percentage of the area covered by grasses, Strobilanthes, bamboo's and reeds is estimated in the deciduous forest;

- The slope is measured and spatial mapping of all individuals with gbh > 30 cm is undertaken in the 20x20-m plot.

The method was adjusted to fit to the field conditions and the time allocated for it. Therefore, the total number of measurement points- which should be 20 according to the method- were reduced to 9 in evergreen forest, to 9 in moist deciduous forest and to 4 in dry deciduous forest. The area therefore varied according to the forest types (Table1).

Table 1. Number of plots and total areas sampled in the forest types

Forest types No. plots Area (m2)
Evergreen 9 3600
Moist deciduous 9 3600
Dry deciduous 4 1600

Data analysis

Structural analysis

The structural analysis involved girth and height distribution among the trees, the relationship between the height and diameter with the representation of the past, present and future populations of trees for data obtained from both fixed-area plot and cluster methods. The basal area, biovolume estimation and the density can also be calculated and used to characterize the stand studied.

The data were analysed as follows:

- Frequency histograms for height and girth classes;

- Relationship between the height and diameter: plotting h=100d, following Oldeman (1984). The girth (gbh) was converted into diameter (dbh) using the formula

gbh= *dbh.

Diversity analysis

The data collected from plot and cluster methods have been used in this analysis. In order to understand the variations of the values obtained by the indices with varied sample size, the analysis has been undertaken at the quadrat or plot level and at the community level. Species richness was estimated with the total number of observed species and the Chao estimator. Species diversity was estimated with Simpson and Shannon-Wiener indices.

-Number of observed species

The number of species is represented as Sobs.

-Cho estimator of species richness (Chao, 1987)

The Chao index, which corrects Sobs with additional terms by taking the role of rare species into account , was calculated using the formula :

where a = number of species represented by 1 individual

b = number of species represented by 2 individuals

Sobs = total number of species observed.

- Simpson index (Magellan 1988)

The Simpson index is defined from as the probability that two individuals randomly and independently selected belong to the same species. This index is calculated using the formula:

where ni =individuals in the species i; N = total number of individuals.

The Simpson index can be defined in two ways:

· as =1-D, also called a dominance index; it varies between O(for a one-species community) and 1-1/S (for an even community composed of S species);

· as ' = 1 /D; it varies between 1 (for a one-species community) and S.

- Shannon index (Magurran 1988)

- For a given species richness S, the maximum value of the Shannon index is obtained when the species distribution is even (i.e., pi =1/S, max(H)= lnS)

The Evenness value is therefore calculated as follows,

An example of indices calculation is given in Annex 1.The indices can be calculated for each 20x20m plot and for the total population recorded with all the 20x20m plots laid. The total population is obtained by adding all the plots species and individuals (the list is constituted and then sorted to get the number of individuals belonging to each species). The total values for the indices cannot be obtained by summing the values for each plot, but have to be calculated separately for the total population.

Results

Structural analysis

The analysis is illustrated here with the example of the tree data (gbh>30 cm) from the evergreen, moist deciduous and dry deciduous forests. The analysis uses the frequency data to understand the distribution of species and individuals across vertical and horizontal space and the application of the relationship between height and girth to understand the structural stratification of the forest.

Height distribution

The relative height of the ensembles in the forest is an important criterion for understanding the nature of the forest. The average height of the forest stand can vary depending upon the history of the site, the topography, the abiotic factors and the climatic climax of the area considered. In general, lowland dipterocarp forests harbour tall and emergent trees can reach up to a height of more than 35m. In the higher altitudes, there are stunted forests where the tallest trees will seldom be taller than 20m. Evergreen forests usually present higher and larger individuals than deciduous forests.

Evergreen forest

Maximum number of trees were encountered at the under-storey (<15 m) and subcanopy (15-25m) levels. The emergent level (>40m) is almost absent (less than2%). The tallest tree reaches 43 m. Normally, individuals with gbh>30 cm girth are taller than 5m. the representation of individuals in the lower height classes is due to the disturbance (natural and man-made); in cases of half broken or bent trees due to tree falls, or lopping ,the trees can have a girth>30cm, but be less than 5m in height.

Moist deciduous forest

Moist deciduous forests are usually not taller than 30m height, the first structural ensemble (canopy level) varying between 20 to 30 m (Pascal.1986). The tallest tree sampled is 30.5 m height. The canopy level, lower than in evergreen forests (reaching over 40 m height), is one of the structural differences between evergreen and deciduous forests. The number of structural ensembles is also reduced, the emergent level is absent in deciduous forests (Pascal, 1988)

Dry deciduous forest

The total height of the trees is lower in dry deciduous forests. Most of the trees belong to the height class 10-15m. Few tall trees (20-25m) are indicated for this forest type.

Girth distribution

The frequency of girth classes is also used to obtain the same kind of information as for height. In timber-oriented surveys, they formed important criterion for exploiting the species at an optimum girth class. The current studies on biodiversity use this kind of data available to understand the patterns of girth class distribution in an undisturbed forest.

Evergreen forest

This population presents trees belonging to all the girth classes, including the large ones. The presence of large trees, mostly belonging to timber species, indicated the absence of heavy exploitation and minimal disturbance and canopy species within the small girth classes shows that this forest population contains an important part of young trees, ensuring the regeneration and perennity of the forest stand.

Moist deciduous forest

The presence of trees of large girth (_100 cm) shows that the forest area sampled has had minimal disturbance. A large number of trees (29% of the population) belong to the small girth classes (30-50 cm). These girth classes comprise trees belonging to the different strata (Table 2). The presence of young trees of the canopy level shows that the population's future is ensured.

Dry deciduous forest

The girth classes range from 30 to 210 cm. This wider distribution and therefore the presence of large mature trees ensures the reproduction of the stand. Trees are poorly represented in the smaller girth classes (21% of the population). This can be related to fires occurring each in this dry ecosystem, injuring or destroying the saplings and young trees. Though large trees are found in this dry deciduous forest, the major part of the trees are distributed within the girth. Though large trees are found in this dry deciduous forest, the major part of the trees are distributed within the girth classes 50-100 cm (52%). This feature is common in dry deciduous forests, where very few species reach a large girth, with a preponderance of small and medium girth species. The small girth classes, though poorly represented, contains in a major part trees of the canopy level, constituting its future stand (Table 2).

Table 2. Distribution of the trees of the girth classes 30-50 cm within the strata

Strata Evergreen forest Moist deciduous forest Dry deciduous forest
Em,1 51% 89% 67%

Height and girth relationship

The relationship between the diameter and the height is the expression of the growth programme that determines the successive architectural phases of the trees (Oldeman 1974). The simple average model for the expression of the relationship between height (h) and trunk diameter (d) is h=100d (in meters). Trees growing spontaneously are found to the right of the line or on the line and form the `set of the present'. The older trees are located right of the set of present and called as `set of the past'. They are characterized by a large diameter. Trees located to the left of the line form the `set of the future', these individuals have not yet expressed their full architectural development: they first grow in height to reach their strata level and once they get the maximum light available, they grow in diameter. The layering of the ensembles in the natural forest is not always distinct due to the presence of trees of the future, which fill all the gaps. The model has been found valid for tall evergreen forests of French Guiana (Oldeman 1974), Indonesia (Laumonier 1980), Africa (Devineau 1975), Western Ghats, India (Pascal 1984, Ramesh 1989, Pelissier 1995).

Evergreen forest

The set of the future is strongly represented, with accumulation of several trees ready to take over (0.1-0.3m diameter and 10-30 m height). The set of present and past are also present.

Moist deciduous forest

Relationship between diameter and height shows that a significant number of trees are seen in the set of the present and even in the past. Few individuals are present in the lower diameter classes, representing the set of the future and the potential for regeneration.

Dry deciduous forest

The set of the future is quite inexistant in this forest. Trees are distributed in the sets of the present and the past. This puts forward the question of the perennity of this forest.

Diversity analysis

The data entry, the data format required for this kind of analysis and the application of the necessary formulae used for estimating diversity were applied using Microsoft Excel spreadsheets.

Abbreviations used

- N: Total number of individuals

- S: Total number of observed species

- Chao: Chao estimator of species richness

- Simpson (D): Simpson index (species diversity)

- Shannon (H): Shannon index (species diversity)

- (a, b): number of species with only 1 individual (a) or only 2 individuals (b); Chao index cannot be calculated if b=0;

- * : no data.

Evergreen forests have been reported to be the richest forests in the Western Ghats, in comparison with deciduous forest. Along with the bioclimatic gradient is a species richness and diversity gradient from the evergreen forests to the moist deciduous and dry deciduous forests. Endemic species, largely present in the Western Ghats (Ramesh etal. 1997), are mainly distributed in the evergreen forests.

Evergreen forest

The number of trees species observed is 59 in 3600 m2 area (Table 3). The compiled data (total of the plots) show a high Chao estimator: the stand is represented by many species with 1 to 2 individuals (15.3%). Dipterocarpus indicus (Dipterocarpaccae) dominates (39 individuals, 18%), followed by Knema attenuata (Myristicaceae; 14 individuals, 7%), and in a smaller extent Dimocarpus longan (Sapindaceae; 11 individuals, 5%) and Mesua ferrea (Clusiaccac; 11 individuals, 5%).

Though the stand is dominated by 4 species, diversity remains quite important as there are many species represented by few individuals. Evenness, quite high, reflects this feature, as well as the Shannon index (H'), relatively close to the maximum value (Hmax).

Moist deciduous forest

The number of tree species recorded with this method is 18, with 134 individuals (Table 3). The number of species is regular among the plots, and the number of individuals varies from 8 to 27. Dominance is clearly shown by Anogeissus latifolia (59 individuals, 44%), Terminalia crenulata (27 individuals, 20%) and Tectona grandis (15 individuals, 11%). Rare species are present, though 3-7 individuals represent most of species, after the dominant species.

The diversity is low both reflected by the low values of Simpson (1/D), Shannon (H) and Evenness (E) indices.

Dry deciduous forest

The number of tree species recorded with this method is 11, represented by 42 individuals. Though few individuals represent half of the species, the Chao estimator is quite low. Terminalia crenulata (13 individuals, 31%), Anogeissus latifolia (7 individuals, 17%) and Tectona grandis (6 individuals, 14%) are dominant. Diversity, though higher than in the moist deciduous forest, remains quite low, due to the high dominance of the three species mentioned above.

  Indivi. Spp Chao (1/D) (1-D) H'(Hmax) E
Evergreen 215 59 104.00 (25,8) 19.00 0.95 3.45(4.08) 0.85
Moist deciduous 134 18 24.13 (7,4) 3.98 0.75 1.88(2.89) 0.65
Dry deciduous 42 11 11.50 (2,4) 6.94 0.86 2.07(2.40) 0.87

Reference

Chao A., 1987. Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 43: 783-791.

Devineau J-L.., 1975. Etude quantitative des forests-galeries de Lamto (Moyenne Cote d'Ivoire). These 3eme Cycle, Paris.

Ganesh T, Bawa K.S Ganeshan R & Davidar P., 1996. Assessment of plant biodiversity at a mid-elevation evergreen forest of Kalakad-mundanthurai Tiger reserve, Western Ghats, India. Current Science, 71: 379-391.

Magurran A. E., 1988. Ecological diversity and its measurement. Croom Helm ltd, London 179 pp.

Olden R. A. A 1974. L'architecture de la forest guyanaise. Memoires O.R.S.T.O.M., 73, 204 p.

Pascal J.P. 1986. Explanatory booklet on the forest map south India. Institut Francais de Pondichery.

Pascal J. P. 1988. Wet evergreen forests of the Western Ghats in India: ecology, structure, floristic composition and succession. Travaux de la section scientifique et technique, 20bis Institute francais de Pondichery, 345 pp.

Pelissier R., 1997. Heterogeneite spatiale et dynamique d'une foret dense humide des Ghats occidentaux de l'Inde. Publications du Department d'Ecologie, n 37. Institut Francais de Pondichery, 150 pp.

ANNEX 1

  Species No sp. No.Indiv. Simpson D Chao pi Inpi H E

Total

Anogeissus latifolia 1 7 0.02 11.50 0.17 -1.79 0.30  
  Emblica officinalis 1 1 0.00   0.02 -3.74 0.09  
  Grewia tilifolia 1 3 0.00   0.07 -2.64 0.19  
  Lagerstroemia parviflora 1 2 0.00   0.05 -3.04 0.14  
  Pterocarpus marsupium 1 1 0.00   0.02 -3.74 0.09  
  Stereopspermum personatum 1 2 0.00   0.05 -3.04 0.14  
  Tamilandia uliginosa 1 2 0.00   0.05 -3.04 0.14  
  Tectona grandis 1 6 0.02   0.14 -1.95 0.28  
  Terminalia cenulata 1 13 0.09   0.31 -.17 0.36  
  Terminalia paniculata 1 3 0.00   0.07 -2.64 0.19  
  Ziziphus glaberima 1 2 0.00   0.05 -3.40 0.14  
    11 42 0.14       2.07 0.87
      1722 0.86     Hmax 2.40  
        6.94          
Plot 1 Emblica officinalis 1 1 0.00 9.50 0.10 -2.30 0.23  
  Pterocarpus marsupium 1 1 0.00   0.10 -2.30 0.23  
  Sterospermum personatum 1 1 0.00   0.10 -2.30 0.23  
  Tamilnadia uliginosa 1 2 0.02   0.20 -1.61 0.32  
  Terminalia cenulata 1 5 0.22   0.50 -0.69 0.35  
    5 10 0.24       1.36 0.84
        0.76          
        4.09          
Plot 2 Anogeissus latifolia 1 4 0.17 3.00 0.44 -0.81 0.36  
  Grewia tilifolia 1 3 0.08   0.33 -1.10 0.37  
  Tectona grandis 1 2 0.03   0.22 -1.50 0.33  
    3 9 0.28       1.06 0.97
        0.72          
        3.60          
Plot 3 Anogeissus latifolia 1 2 0.02 5.00 0.17 -1.79 0.30  
  Lagerstroemia parviflora 1 2 0.02   0.17 -1.79 0.30  
  Tectona grandis 1 3 0.05   0.25 -1.39 0.35  
  Terminalia cenulata 1 3 0.05   0.25 -1.39 0.35  
  Terminalia paniculata 1 2 0.02   0.17 -1.79 0.30  
    5 12 0.14       1.59 0.99
      132 0.86          
        7.33          
                   
Plot 4 Anogeissus latifolia 1 1 0.00 14.00 0.09 -2.40 0.22  
  Stereospermum personatum 1 1 0.00   0.09 -2.40 0.22  
  Tectona grandis 1 1 0.00   0.09 -2.40 0.22  
  Terminalia crenulata 1 5 0.18   0.45 -2.79 0.36  
  Terminalia paniculata 1 1 0.00   0.09 -2.40 0.22  
  Ziziphus glaberima 1 2 0.02   0.18 -1.70 0.31  
    6 11 0.20       1.54 0.86
        0.80          
        5.00          

SECTION 10: Problems Related to the Assessment of Forest Area: The Country Perspective - GRAZING

by Ranjana Gupta

Introduction

Forests are an important source of food for cattle in most parts in India.

Thelivestockpopulation of India accounts for about 15 percent of the total livestock

population of the world and for only 2 percent of the total world's geographical area. As

per the 1982 census, the livestock population in India was 415.9 million and is expected

to cross 500 million by 2000 AD. The Livestock population in India has shown a rising

DAMAGE TO THE FOREST BY GRAZING ANIMALS-

Grazing animals graze seedlings making regeneration a problem.

(I) Seedlings are trampled and crushed or their roots get exposed by the hoof of moving animals.

(II) Uncontrolled heavy grazing compacts the soil, reduces its porosity and causes increased run-off and causes soil erosion.

(III) Depletion of nutritive palatable grasses which cannot even regenerate resulting in reduced grazing capacity.

(IV) Heavy continuous grazing results in dry and grazing resistant inferior species.

TABLE 1 : LIVESTOCK POPULATION IN INDIA 1951-1982

Deputy Director, Training & Vegetation Mapping Unit, FSI, Dehradun.

The increase was not uniform in different States of the country. The density of livestock population in the states is given in Table 2. It is observed that almost in all the States the density of livestock is in excess of the carrying capacity of the land.

       
State Number of livestock units  
  Per ha. of cropped area Per ha of Geographical area Per capita
Andhra Pradesh 9.83 4.40 0.67
Assam 9.59 4.21 0.49
Bihar 10.17 6.54 0.58
Gujarat 5.77 3.28 0.51
Haryana 5.56 7.12 0.58
Himachal Pradesh 16.00 5.07 1.16
Jammu & Kashmir 16.80 3.50 0.97
Karnataka 7.12 3.99 0.64
Kerala 6.24 4.59 0.22
Madhya Pradesh 7.44 3.60 0.82
Maharashtra 5.01 3.30 0.48
Manipur 6.93 0.69 0.36
Meghalaya 12.16 1.21 0.76
Nagaland 4.75 0.69 0.62
Orissa 7.90 4.45 0.82
Punjab 5.99 7.91 0.54
Rajasthan 7.31 3.70 1.44
Sikkim 9.57 1.21 1.00
Tamil Nadu 11.35 5.65 0.51
Tripura 8.92 3.19 0.56
Uttar Pradesh 8.80 7.27 0.51
West Bengal 11.31 9.74 0.54

The NCA (1976) estimated the requirements for the projected livestock population for the year 2000 as given in the table 3.

TABLE 3 : REQUIREMENT OF FODDER FOR PROJECTED LIVESTOCK POPULATION IN 2000
               
Category Projected Requirement per head per day(kg) Requirement per year for projected population in 2000 (million tonnes)
  Population in 2000 (million) Concentrates Green

fodder

Dry fodder Concertrates Green fodder Dry fodder
Cattle              
(I) males working and breeding 73.04 0.25 5 5.5 6.66 133.3 146.7
(ii) female milch and dry              
(a) Non descript 21.35 0.2 5 4 1.56 38.9 31.2
(b) Improved Indigenous 11 1.2 10 6 4.82 39.69 24.1
(c ) cross bred 18.9 2.75 20 6 18.97 136.1 41.4
(d) young stock cross bred 15.98 15 10 2 8.75 58.3 11.7
Others 26.5 0.5 5 1.5 4.84 48.4 14.5
(B) Buffaloes              
(i) males working and Breeding 6.98 0.2 5 5 0.51 12.7 12.7
(ii) Female milch and dry              
(a) Non descript 12.99 0.5 5 5 2.37 2.37 2.37
(b) Improved Indigenous 17.6 1.5 1. 6 9.64 64.3 38.5
(c ) Young stock 19.04 0.1 5 2 0.7 34.8 13.9
               
Total for bovines         58.82 590.1 358.4
               
(C) Improved sheep 60 0.3-   0.4 6.57-   8.8
(D) Improved Goats 40 0.3- - 0.4 4.38-   5.8
(E) Horses & Ponies 0.8 0.5 -   0.15- -  
(F) Camels 1 0.5 -   0.18 -  
Total for others       1 1.28-   4.6
Grand Total         70.1 590.1 373.00
Demand and Supply of Fodder

It is believed that nearly 30% of fodder requirement is met from forests.Various estimates of the requirement of feed and fodder for different categories of livestock have been made in the past. (Table 4)

Table 4 : ESTIMATES OF DEMAND AND SUPPLY OF FODDER IN INDIA.

(Million tonnes)

(Source : R.V.Singh (1994).

* Forecast based on projected livestock

Population.

# Agricultural crop residues.

** Estimates include fodder requirement

only for improved sheep and goats.

The fodder demand for the year 2000 as assessed by NCA and by the Committee on Fodder and Grasses vary mainly because estimates of NCA are close to maintenance ration of 2% body weight while those of the committee are based on 3% body weight.

Availability of Fodder

According to Singh (1994) dry grass yield of 2.0 and 1.5 tonnes per hectare may be expected from forests and other grazing areas. Grass harvesting may, however, be possible only from two-third of the forest areas because of their location. On this basis, he estimated the yield of dry grass to be about 190 million tonnes annually. Total dry fodder availability according to him may be about 440 million tonnes.

Expected Fodder Production

The fodder production in the country falls very much short of the requirement, the shortage of green fodder is more than that of the dry fodder. According to Singh (1994) with the availibility of 440 and 292 million tonnes of dry and green fodder, respectively, against the respective demand of 949 and 1136 million tonnes, there is big gap between the demand and supply of fodder. In view of the continuing deterioration of the forest areas and grasslands. Dry fodder production from these sources is likely to decrease while the requirement will increase. The gap between the demand and supply of fodder will thus continue to widen if fodder production foe livestock is not given the attention it deserves.

Another report (Ranjhan 1994) indicated a wide gap between supply and demand or dry matter, digestible crude proteins and total digestible nutrients. The demand and supply of feed and fodder in the year 1991 showed that there was a gap of 13% in the staff straw/stover and 18% in green fodder between the availability and requirement.

Current Estimates and Projections

In Table 5 the livestock population of 1987 was considered and using the cumulative growth rate the projected livestock population has been estimated. Based on the data of Table 5 and norm of odder as adopted by NCA, the annual requirement of green fodder and dry fodder has been estimated. It has been found that the annual requirement of green fodder and dry fodder for 1996 for the livestock (except poultry) is 593 million and 482 million tonnes respectively. Projected figures for 2001 and 2006 A.D. have also been given (Table 6)

Table 5 : PROJECTED POPULATION OF LIVESTOCK IN INDIA

(Million)

The Gap between Demand and Supply has led to heavy grazing and FSI has done and assessment of grazing. The results are indicated in the Table 7.

TABLE 7 : EXTENT OF GRAZING INCIDENCE IN FOREST AREAS OF THE COUNTRY

(BASED ON THE INVENTRY CONDUCTED BY FSI SINCE ITS INCEPTION)

(Sq.km.)

SAMPLING DESIGN AND METHODOLOGY

A 1:50,000 scale Survey of India topographic sheet was divided into 36 grids of 2 ½' x 2 ½' of latitude and longitudes. In each of such grids two sample points were marked. The inventory data was collected from a square plot of 0.1 ha. Laid out at each of these sample points. Qualitative data such as land use, crop composition of tree crop and its density, intensity of erosion in the area, fire and grazing incidence, regeneration status etc. are recorded in the PLOT DESCRIPTION FORM (PDF). The basis of assessment is occular, by examining a surrounding area of about 2 ha. Around the plot centre.

PREVENTIVE MEASURES

SECTION 11: Global Forest Resource Assessment 2000 - a user's perspective

By J.S.Grewal, Conservator of Forests

Working Plans Circle, Maharashtra forest department, NAGPUR, India

Introduction

Assessment of renewable natural resource such as forest is of paramount importance for policy makers and planners at all levels. But this is more so at the cutting edge planning level because this forms the input for all action plans which are to be implemented in the field. So ideally speaking the picture at national or global level should flow out of the integration of information at the lower levels. The local level information being voluminous and patchy, pertaining to different time periods and available in different formats is not very amenable to consolidation with good degree of reliability. This at times lead to projections or estimations, which may not be very correct. It is with this background in mind that FAO has started interacting with various countries well in advance so that better uniformity and consistency in data can be achieved and the gaps if any can be hopefully plugged before constructing the big picture.

In India national level forest inventory is compiled by Forest Survey of India, Dehra Dun. They are also engaged in the forest cover assessment and its change based on satellite data. Methodologies are fairly standardized and a series of published reports are available. Besides this macro level inventory by FSI, many states in India are carrying out much detailed inventories at their level too. The data from these inventories is used for the preparation of working plans. The data at this level is very voluminous and is not generally available in a format, which can lend itself to easy manipulation. Maharashtra state has taken the lead in computerizing inventory which makes the data available in electronic format. This can be easily and quickly manipulated to generate various types of useful information both at the planning and implementation level.

Forest Management Functions

Basically a forest manager has to perform the following core functions:

· Planning

· Implementation

· Control

· Organisation

To be able to perform these functions a forest manager has to seek answers to many questions of the following type:

To find clear answers to questions of the above kind lot of data is required. Moreover this data has to be in such format that it can be viewed and synthesised easily and quickly. Some of the typical data needs of a forest planner are listed below. This list is by no means comprehensive, it is indicative only.

Data needs for forest planner

· Structure

· Crown density

· Regeneration status

· Crown density

· Site quality

· Negative influences

· Sensitive spots

· Topo features

· Soils

· Climatic data

· Moisture regime

· Biotic influences

· Markets

Need for digital databases

The problem with the non-digital databases arises because of the sheer volume of data. It is very difficult to synthesise and comprehend such large volumes through manual systems. The other major problem with manual systems is that of retrieval. Information is generally collected from various sources and at various times, which not only overloads the system but makes it very unreliable and inconsistent too.

Managers for land-based systems have to invariably deal with multitude of maps. These maps are available on different scales and at times even in different projection systems, which makes it very difficult to manipulate geographical information. Because of these and many more problems it is much more convenient to capture data digitally and centrally.

A computerised inventory system

Data entry is very easy with many drop down lists so as to enforce better data validity. The following screenshot gives an idea of data entry process.

The sample plot level data is consolidated online by the flick of a button at the compartment level. Custom queries can be made on any combination of fields to generate information. So it acts as a very good DSS for the working plan officers. A sample screen below indicates this potential.

Crop composition from compartment to compartment can be visualised through utility depicted in the following screenshot.

Besides these and many more online features many ready made reports can be generated for preview or printing. Samples of two reports are shown below.

This Inventory Management System has been integrated into Geographical Information System so as to have both spatial and attribute information in a more meaningful manner.

SECTION 12: Forest Naturalness

by P.C. Tyagi, IFS, Joint Director, FSI

The term and definition of FRA2000 of developing countries in its section I has classified forest naturalness according to the degree of human intervention. The forests has been sub divided into

(i) Forest undisturbed by man

(ii) Forest in semi natural condition

(iii) Plantations

The other wooded lands has been subdivided into the first two categories, Forests

undisturbed by man has been defined as natural forest which are dynamic have natural species composition, dead wood occurs on forest floor, have natural age structure and natural regeneration process, are large viable biological units with no known human intervention or where human intervention occurred long time age (since time imemorial).

The Semi natural managed forests/other wooded land has been defined as which is neither forest land undisturbed by man nor plantation as defined separately.

This includes:

Various forms of secondary forests

Disturbed primary forests

Logged forests with exotic under planting

Managed forests regenerated artificially or naturally with .

Indigenous crops.

Semi-natural othe wooded land includes forests fallows or scrub formation plantation has been defined as those forest stands, established by planting or/and seeding in the process of afforestation or reforestation.

The forest formation classes for forests undisturbed by man are (I) closed forests (II) Open forests. These formations are difficult to discern as the records of working these forest has to be checked to place them in forest formatiom categories.

Forests are very dynamic natural systems and have been subjected to natural or man made disturbances. Natural disturbance e.g. fire, landslide, inundation by water have affected the forest and set the clock of ecological succession.

The old growth forests have been described by the adjective primeval, ancient, wilderness, virgin, pristine while in forester's terminology they are called as over-matured, decadent, and senescent, old growth. The old growth forests may be defined as a climax forest that has never been disturbed by man.

Even though a broadly applicable definition of old growth may be impossible to develop it is still useful to consider the kinds of criteria that are likely to have ecological significance and that could be adopted to form locally appropriate definition.

The old growth forests can be classified as per the age and disturbance criteria. For age criteria we have to consider:

(2) Has the forest reached an age at which average net annual growth is close to zero?

(3) Have the dominant trees reached the average life expectancy for that species on that type of site?

(4) Has the forest's current annual growth rate declined below the life time average annual growth rate?

For disturbance criteria we have to consider:

(5) Has the forest been extensively or intensively cut?

(6) Has the forest ever been converted by people to another type of eco-system?

The task of determining the forested tract of India where the forests are in a natural state undisturbed by man, are climax forests, stable and still retain the primary natural character is quite difficult.

Since the advent of scientific forestry in India most of the forests have been worked for timber. The forests which were located in remote corners of the country, far away from the nearest rail and road head and were therefore inaccessible to man may not have been worked, however the local population/tribals living close to the forests may have used some forest produce. These forests may have been protected later on by being brought under the perview of the Indian Forest and Wildlife Act and could have possible been put in the Protection Working Circle in the Working/Management Plan.

In 1981 the State Forest departments were requested to furnish information on the undisturbed natural forests, still retainting the primary characters. Very few state could identify patches of such forests. Most of them gave information of the National Parks and Wildlife Sanctuary of their area, which they considered as well protected.

The area's selected for the Protection working circle of the working plan has been cited by some foresters as possible areas to qualify for inclusion in natural areas. Most of these areas lies on steep slopes inaccessible to man. However area's which have been degraded due to excess working, natural calamity etc. are also placed in protection working circle for rehabilitation and hence protection these areas will not wholly qualify to be placed in the category of Natural forests.

The core areas of the Protected Area Network are mostly area's free from disturbance from man and livestock and ge the highest level of protection in the management of National Parks and sanctuaries. In the core area habitat manipulation or any other management intervention is not permitted. These area's are basically rich in biodiversity and natural processes are not influenced by any intervention. These are closed systems which are protected by the Protected Area management by regular patrolling. However research work is sometimes permitted in these area. In due course of time these area's will develop into natural ecosystem totally undisturbed by man where natural process with dominate the forest eco-dynamics.

The details of Protected Area network state wise and Natural Area undisturbed by man identified by different states is given in Table 1. The rest of the country's forest are seminatural forests which are use for the benefit of the people.

Table 1. India's Forest by Naturalness categories (Sq.km.)

             
State/UT Geographic Actual Forest cover Forest
  State/UT

Total area

Area Percent Natural* Protected area Percent of

State/UT area

Andhra Pradesh 275,068 43,290 15.7 1987.5 12205.50 4.54
Arunachal Pradesh 83,743 68,602 81.9 2001.25 9582.98 12.08
Assam 78,438 23,824 30.4 N.A. 1920.58 2.54
Bihar 173,877 26,524 15.3 N.A. 4449.23 2.66
Delhi 1,483 26 1.7 N.A. 13.20 0.90
Goa 3,702 1,252 33.8 N.A. 462.78 12.98
Gujarat 196,024 12,578 6.4 151.00 17223.94 9.13
Haryana 44,212 604 1.4 N.A 344.08 0.80
Himachal Pradesh 55,673 12,521 22.5 N.A 6232.87 11.42
Jammu & Kashmir 222,235 20,440 9.2 N.A 14822.22 6.69
Karnataka 191,791 32,403 16.9 990.5 6710.39 3.55
Kerela 38,863 10,334 26.6 151.00 2679.88 6.80
Madhya Pradesh 443,446 131,195 29.6 82.83 17189.77 4.03
Maharashtra 307,690 46,143 15.0 564.92 14953.94 5.02
Manipur 22,327 17,418 87.0 1200.0 266.65 1.24
Meghalaya 22,429 15,657 69.8 701.56 303.65 1.40
Mizoram 21,081 18,775 89.1 592.00 810.00 3.99
Nagaland 16,579 14,221 85.8 585.91 226.43 1.42
Orissa 155,707 46,941 30.1 473.00 7739.55 5.15
Punjab 50,362 1,387 2.8 N.A 295.34 0.06
Rajasthan 342,239 13,353 3.9 N.A 9520.20 2.88
Sikkim 7,096 3,129 44.1 1566.3 1011.10 14.68
Tamil Nadu 130,058 17,064 13.1 1201.0 3072.66 2.34
Tripura 10,486 5,546 52.9 558.40 603.62 6.01
Uttar Pradesh 294,411 33,994 11.5 N.A 13066.40 4.59
West Bengal 88,752 8,349 9.4 N.A 2779.30 3.26
A&N Islands 8,249 7,613 92.3 7141.47 1273.13 18.41
Chandigarh 114 7 6.1 N.A 25.42 22.10
Dadra & Nagar haveli 491 204 41.5 N.A 0.00 _
Daman & Diu 112 3 2.7 N.A 2.18 2.20
Lakshdweep 32 Nil Nil N.A 0.00 _
Pondicherry 493 Nil Nil N.A 0.00 _
Total 3,287,263 633,397 19.27 19948.28 149788 4.70

(Figures furnished by State Forest Departments)

SECTION 13: Non Wood Forest Products

By Smt. Ranjana Gupta, IFS, FSI, Dehradun

Introduction

The term Non Wood Forest Products (NWEP) or "Minor Forest Products" (MFP) cover all forest products other then "major forest products", which consist of timber, small wood and fuel woo. MFP includes grasses, fruits, leaves, barks, exudates, animal products, soil and minerals. In India about 3000 plant species yield economically significant produce. About 126 plant species have been developed as tradable commodities

For the sake of convenience in dealing with the subject, minor forest products of commercial importance in India may be divided into the following classes:

1. Fibres and flosses

2. Grasses(other than oil-producing grasses) bamboo's and canes,

3. Essential oils(including oil-yielding grasses)

4. Oil seeds,

5. Tans and dyes,

6. Gums, resins, and oleo-resins,

7. Drugs, spices, poisons, and insecticides,

8. Edible products,

9. Animal, mineral, and miscellaneous products

Importance of NWFPs

In the global forest scenario in general and in the Indian scenario in particular, there has been a shift in the forestry objectives and approaches from exploitation to conservation, revenue to services, maximum to sustained yield, prohibitive to participatory management. This holistic forest management approach has lead to an increased understanding of non-timber forest products. Their importance can be gauged by the fact that they provide-

(i) Sustenance for 50 million people

(ii) 40% of the official revenue

(iii) 60-70% of export earnings

(iv) 55% of forest based employment

(v) 35% of income tribal households

(vi) 50% of income to 20-30% to rural labour

Policy Guidelines

The national forest policy 1988 aims at

(i) Providing sustenance to people rather than deriving economics gains

(ii) Sustainable management

(iii) Protection and judicious harvest

(iv) Institution building for marketing

(v) Involvement of local people in all activities i.e. from regeneration to marketing.

Collection and marketing of NWFPs

Non Wood Forest Products have certain inherent disadvantages with regard to exploitation. The constraints in their collection and marketing are as below:

(i) NWFPs are bulky and scattered

(ii) Found in difficult and inaccessible terrain

(iii) Perishable commodities

(iv) Inadequate information on collection, processing, grading and storage

(v) Poor quality control

(vi) Un organised collection and marketing

The NWFPs have remained largely neglected because

(i) Their values have not been qualifies.

(ii) No exclusive studies have been done to assess their distribution, extent, use and renewability.

(iii) We have very little data about them

(iv) No management plan has been prepared except for bamboo and tendu leaves.

The contribution of Non Wood forest products to the forest revenue of some

States is given in table-1.

Table-1. Revenue contribution of NTFPs (1985-86)

( In 000' Rs)

  Timber Revenue NTFP Revenue Total Revenue NTFP as % of Total
Andhra Pradesh 314140 231284 545524 42.4
Arunachal Pradesh 64960 15770 80730 19.5
Assam 1960 55790 57750 96.6
Bihar 386380 73620 460000 16.0
Goa, Daman, Diu 270 480 2750 17.5
Gujarat 265000 18880 283880 6.7
Harayana 35600 7540 43140 17.5
Himachal Pradesh 235500 74520 310020 24.0
Jammu & Kashmir 32050 80565 112615 71.5
Karnataka 509800 53790 563590 9.5
Kerala 355000 72400 427400 16.9
Madhya Pradesh 1940600 1200530 3141130 38.2
Maharashtra 769800 196300 966100 20.3
Manipur 4490 1950 6440 30.3
Meghalaya 16040 8100 24140 33.6
Mizoram 1530 1220 2750 44.4
Nagaland 36160 17000 53160 32.0
Orissa 249300 235000 484300 48.5
Punjab 31500 13860 45360 30.6
Rajasthan 12900 86670 99570 87.0
Sikkim - 2330    
Tamil Nadu 18070 258540 276610 93.5
Tripura 28740 4110 32850 12.5
Uttar Pradesh 347000 221840 568840 39.0
West Bengal 238300 4380 242680 1.8
A & N Islands 94700 3030 97730 3.1

It is evident that in states like Assam and Tamil Nadu the contribution of NWFPs to total revenue is more than 90% while in Rajasthan it is 87% and in Madhya Pradesh 38%.

The number of species used for various end used as also the quantities collected vary greatly from place to place and season to season

The table-2 shows the potential annual production and the outturn of bamboos in different states of India during 1984-85. As against the potential availability of about 4.6 million tons of bamboo in the country, the annual production is 3.2 million tons per year.

Table-2 Potential Vs Production of Bamboos

             
Name of state Bamboo area

(m ha)

Potential availability

(m ton)

Bamboo Outurn

(m ton)

Of Numbers
Andhra Pradesh 1.98 0.255 0.307   147.731  
Arunachal pradesh 0.78 0.200     1026.000 (1982-83)
Assam 1.00 1.210        
Bihar 0.57 0.200 0.080      
Gujarat 0.19 0.046 0.065 (1983-84)    
Himachal Pradesh 0.01 0.003 0.003      
Karnataka 0.60 0.475 0.086      
Kerala 0.06 0.108     1016.343  
Madhya Pradesh 1.49 0.800 0.316 (1985-86)    
Maharashtra 0.85 0.300 0.376 (1983-84)    
Manipur 0.25 0.200 0.001 (1983-84)    
Orissa 1.05 0.489     162483.62  
Punjab   0.009 0.0007      
Tamil Nadu 0.54 0 0.011 (1983-84)    
Tripura 0.28 0.215 0.028      
Uttar Pradesh 0.40 0.041     9307.000  
West Bengal 0.02 0.008     104.206  
Total 10.03 4.559 1.276      

A table showing the potential and collection of oil seeds, gums and resins and other NWFPs is given in the table-3

Table-3 Potential Vs Production of NTFPs (Other than Bamboos)

  Potential Collectable Collection
  (MT) (MT) (10 year avg.) (MT)
Tree based Oil Seed      
1.Sal 65,00,000 20,00,000 1,50,000
2.Mahua 4,90,000 3,50,000 1,00,000
3.Karanj 1,00,000 85,000 50,000
4.Kusum 60,000 50,000 25,000
Gum & Resin      
1.Gum Karaya 6,000 4,500 2,500
2.Stick Lac 60,000 30,000 15,000
Other NWFPs      
1.Tamarind 5,00,000 3,50,000 2,35,000
2.Myrobalans 45,000 20,000 10,000
3.Tassar(both wild & dp, estocated enclu-Eri-Muga) 2,000 1,400 1,000
4.Tendu leaves 10,00,000 5,00,000 3,00,000
5.Chironjee 30,000 20,000 5,000
Crops Mainly in Tribal Areas      
1.Niger seed N.A. N.A 5,25,000
2.Psyllium Husk&Seed N.A. N.A. 50,000
3.Medicinal Herbs      
(a) Sarpagandha N.A. N.A. 600
(b) Kuth N.A. N.A. 1,000
(c) Hops 50 N.A. N.A
(d) Agarwood N.A. N.A. N.A.
(e) Safed Musli N.A. N.A. N.A.

The export of some important non-timber forest products is given in Table-4

Table-4 EXPORT OF IMPORTANT NN-TIMBER FOREST PRODUCTS

           
    1988-89 1990-91
    Qty

(in tonnes)

Value Rs.

(in lakhs)

Qty

(in tonnes)

Value Rs.

(in Lakhs)

1   2 3 4 5
  Walnuts 3539.65 1475.03 4236.93 1938.46
  Whole, shelled fresh, dried        
2 Tamarind fresh, dried 6271.7 486.74 2382.48 260.68
3 Tamarind seed and seed powder 3111.89 126.05 5394.95 210.59
4 Cinnamom bark Flower and small seeds 77.16 14.17 40.55 2.01
5 Cloves 0.32 0.49 47.97 19.88
6 Cumin black (kali ziri) and cumin powder 354.35 120.46 287.22 91.12
7 Celery seed and powder 2997.17 315.89 3199.11 414.03
8 Fenugreek seed and powder 3575.47 366.71 3748.1 304.56
9 Other Ginseng Root 1132.03 336.12 1652.06 752.83
10 Psylliun husk and seed. 14004.26 5227.34 16648.49 6380.14
11 Sandal wood Chips and dust and wood used for carving. 2480.41 2137.89 3525.06 3298.57
12 Senna leaves and pods 4369.28 348.48 3721.41 372.68
13 Ayurvedic & unani herbs 2183.33 245.22 2633.6 351.6
14 Plants and parts of plants used for perfumery pharmacy/insecticides or similar purposes 4561.24 344.36 4526.11 458.43
15 Lac and products 7157.38 1933.49 6958.09 1742.18
16 Karaya Gum (Indian Tragacanth) 1830.91 986.44 598.53 422.55
17 Other natural gums 330.84 89.61 373.56 141.32
18 Brooms and brushes 976740 52.08 3123498 136.69
19 Henna leaves & Powder 4623.4 410.7 4653 390.89
20 Tendu leaves 7359.49 1165.78 4893.11 2118.13
21 Sal oil & Sal fat refined 1103.42 247.70 1443.96 357.55
22 Deoiled sal meal 21749 175.49 39902 460.83
23 Mentha preparation   418.61   955.97
24 Menthol 174.88 525.44 313.54 737.66
25 Mentha arvensis 141.01 231.39 58.09 99.96
26 Dementholised Mentha arvensis oil 177.72 290.79 19.10 27.17
27 Sandal wood oil 25.83 629.09 37.43 1327.59
28 Pine oil 10.95 3.31 11.65 3.96
29 Myrobalan extract 458.5 56.45 1090 174.45
30 Cutch 1295.96 92.78 1231.98 228.04

Because of the constraints listed earlier it is very difficult to assess the total non-wood forest resource of the country. Therefore emphasis has been given to some important products like bamboo, tendu leaves, gums, Harra and sal seeds of the state of Madhya Pradesh (M.P). The table-5 given below shows the average bamboo production category wise in M.P.

Table 5 : M.P.- Average Bamboo Production Category wise

from 1973-74 to 1994-95 (in Notional Tonnes)

Year Average Commercial Bamboo Average Indus Bamboo Average Total Exploited
1973-75 25257.75 216937.1 242194.93
1975-80 83101.33 284894.2 367995.54
1980-85 104679.2 191470.6 296149.8
1985-90 135091.4 200267.4 335358.8
1990-95 133619.8 116093.2 249713

Tendu leaves (Diospyros melanoxylon) are a source of revenue to various states forest departments specially M.P. because of their use in the bidi industry. The table-6 below indicates the annual production and revenue of tendu leaves in different states.

Table-6 Annual Production of Tendu leaves

State Production Year

Andhra Pradesh 224900 Qtl. 1985-86

Bihar 667.13 Std.Bags 1983-84

Karnataka 15440Qtl. 1984-85

Madhya Pradesh 3765.075 Std.Bags 1985-86

Maharashtra - 1984-85

Orissa 408000 Qtl. 1979-80

Rajasthan - 1984-85

Uttar Pradesh 223384 Qtl. 1984-85

West Bengal 24371 Qtl. 1985

As per the table-7 given below Madhya Pradesh, Orissa, Maharashtra and Andhra Pradesh accounts for over 85% of the total tendu leaf production in the country.

Table-7 Major Contributors Of Tendu Production

State Production(`000 tones) % of Total
Madhya Pradesh 123.00 41.0
Orissa 50.0 16.7
Maharashtra 45.0 15.0
Andhra Pradesh 39.0 13.0
Bihar 24 8.0
Rajasthan 6.5 2.2
Uttar Pradesh 5.0 1.6
Gujarat 5.0 1.6
Tamil Nadu 2.0 0.7
West Bengal 0.5 0.2
All India 300.0 100.0

The average collection of tendu leaves in Madhya Pradesh between the years 1970-1995 is given in the table-8 below

Table 8 : Madhya Pradesh - Average Collection of Tendu Leaves

Year Average Standard Bags Collected
1970-75 2344.2
1975-80 2780.6
1980-85 4559.4
1985-90 5420.8
1990-95 4721.2

The average production of Kullu gums, other gums, harra and sal seeds in Madhya pradesh between the years 1970-1995 is given in table-9

Table-9 M.P. Average production of Kullu Gum, Other Gums Harra & Sal

Seeds (1970-95)

(in thousand quintals)

         
Year

Average Production

 
  Kullu Gums Other Gums Harra Sal Seeds
1970-75 14.2658 4.2666 184.4928 279.275
1975-80 6.4616 4.0092 153.4334 412.628
1980-85 1.57133 2.7202 115.8102 417.4806
1985-90   14.1064 287.9432 468.2912
1990-95   15.1377 113.6112 359.8028

All the above tables indicate that the production of the various non-wood forest products vary greatly. However they indicate the trends.

In the light of the above the following assessment methods are proposed

(i) Direct assessment

(b) Year marking of funds in proportion to revenue collection

(c) Making use of people institutions like VHPS and FPCS

(d) Linking all parallel plans like biodiversity etc.

(i) Remote Sensing

(b) Workout the extent of similar areas to project total estimates (applicable to sal products, khair, sandal wood, bamboos tendu etc.)

(c) Estimation on the basis of association with principal species.

(i) Using participatory methods

(a) Studies can be done on local organised and unorganised market, intermediaries, organised major markets and end users.

(b) Information may be gathered from traditional users like villagers and tribals.

(c) Peoples institutions like Forest Protection Committees (FPCs) and Village Forests Protection Committees (VFPCs)

(d) Tools like Participatory Rural Appraisal (PRA) and Rapid Rural Appraisal (RRA) can be effectively used to generate primary data base.

(e) Data generated can highlight entry points for participatory management which will give a shape to NWFP sector

Thus there is a need for evolving an integrated approach drawing from the efforts of local forest departments, Forest Corporation, village panchayats, Non-Governmental Organisations, FPCs and VFPC.

SECTION 14: Forest Ownership

Mr. R.K.Upadhyay, IFS, Professor, IGNFA, Dehradun.

1. Origin of intension of ownership:-

The intension of ownership was very clear even during 1894 Policy.

The sole object with which state forests are administered is the public benefit. In some cases the public to be benefited are the whole body of tax payers, in other, the people of the tracts in within which the forest is situated; but in almost all cases the constitution and preservation of a forests involve, in greater or lesser degree, the regulation of rights and the restriction of privileges of user in the forest area which may have previously been enjoyed by the inhabitants of its immediate neighbourhood. This regulation and restriction are justified only when the advantage to be gained by the public is great; and the cardinal principle to be observed is that the rights and privileges of individuals must be limited, otherwise than for their own benefit, only in such degree as is absolutely necessary to secure that advantages. The forest of India, being State Property, may be broadly classified under following reading.

Status of Forest Ownership in 1894 Policy

Status of Forest Ownership in 1952 Policy

1.3 1988 National Forest Policy:-

The 1988 National Forest Policy aims to ensure environmental stability and maintenance of ecological balance. Forest land or land with tree cover should be treated as national assets and should be safeguarded for providing sustained benefits to entire community. The 1988 policy, emphasizes that the rights and concessions should always remain related to carrying capacity of forests.

2. Legal meaning of ownership:-

Ownership may be regarded as an aggregate or bundle of all possible rights which, together, make up an absolutely unrestrained enjoyment. The absolute ownership is also known as "dominus" or "full owner". The owner had the use, the whole of the products, the rights entirely to consume, and the rights of transferring or alienating at pleasure.

If a certain right to use some of the produce or to do something, or have something done, were so to speak, broken off and separated from, the total of rights which made up full ownership, such a separate right is known as "servitude".

As per Indian Forest Act 1927, the forest right (servitude) are not personal e.g. Rights of grazing. The forest rights may also extinguished in the process of settling a reserved forests. A forest right exist without any way touching the ownership.

3. Legal Forest Cover of India:-

4. Effectiveness of legal enactment in protecting forest cover:

4.1 The Forest (Conservation) Act, 1980 came into existence during October, 1980. The forest land diversion before 1980 was very high and on an average about 1.75 lakh hectare area of forest land were getting diverted annually. After the FC Act, 1980 came into operation, the forest land diversion was reduced (in Annexure 4.1 (a))

4.2 Statutory Provisions:

The subject of forest are within the perview of directive principle and fundamental duties as per the constitution of India.

4.2.1 Directive Principles of State Policy

The directive principle under Article 48 A of Indian Constitution reads as follows:

Article 48 A - Protection and Improvement of Environment and safeguarding of forest and wild life - The state shall endevour to protect and improve the environment and to safeguard the forest and wild life of the country)

It is a constitutional duty not only of state but also of every citizen to improve the environment and natural resources of the country. It has been held by the court that in case of violation by individuals or by state, the provision of Article 48 A are enforceable by law.

The protection of environment is a fundamental duty of every citizen as per Article 51 A of the constitution which was incorporated by 42nd Constitution amendment during 1976. The Article 51 A reads as follows:

Article 51 A "It shall be the duty of every citizen of India -

(g) "To protect and improve the natural environment including forests, lakes, rivers and wildlife and to have compassion for living creatures".

The fundamental duties are also enforceable by law.

4.3 Provisions for regulating private forest:

There is legal provisions by state government for regulation and management of private forest like Tamil Nadu Forest (Preservation) Act, 1955, U.P. Private Forest Act, etc,.

4.4 Legal definition of Forests:

The judicial activism in India is playing a very important role in protection of environment. During the year 1996 the Apex Court of country has delivered a land mark judgement and have define forest as : "any area notified under act and also recorded forest in any government record.

5. Annual recoconcilition of forest cover: A Case Study :

The forest in India is mainly owned by the government. Often it has been found that although the area has been notified as forest on record but reality is otherwise. I found a good system of reconciliation of record in Tamil Nadu state which may be useful if it is apply for verification in other states as well.

5.1 Zamabandi:

In Tamil Nadu, there is system of reconciliation of land record annually known as Zamabandi. This is the most effective system of checking the ownership of land either private or government owned land. This is a very exhaustive exercise carried out taluk wise in the every district. As per this exercise the forest area in Tamil Nadu state is 21, 47, 149 hectares (as per 1991-92 reconciliation ) while as per forest record area is 22, 52, 472 hectares (Annexure 5.1 (a), 5.1 (b) & 5.1 (c) Further the detail exercise in this regard was carried out during 1987-88 and it has been reconciled district wise (Annexure 5.1 d)

5.2 Encroachment

With increasing population, there is pressure on forest land. The encroachment of forest land changes the matrix of ownership. The details of population (Annexure 5.2 (a) state wise and area under encroachment (Annexure 5.2 b) state wise will collaborate the matrix. This is significant because the encroachment of forest land generally get regularized after some time by the government.

Annexure 3.1 (a)

Geographic area, recorded forest area and actual cover of various State/UT (sq. km.)

Annexure 3.1 (b)

Distribution of Recorded forest in various State/UT (Sq. km.)

Annexure 3.2

1980 Nil

1981 2672.04

1982 3246.54

1983 5702.01

1984 7837.59

1985 10608.07

1986 11963.11

1987 72780.05

1988 18765.35

1989 20365.05

1990 138551.38

1991 625.21

1992 5686.94

1993 11785.64

1994 13527.69

Annexue 5.1 (a)

Classification of Land area as per revenue records ofTamilnadu 1991-92

Area in ha.

1. Forests 21,47,149.00

2. Barren and unculturable lands 5,07,291.00

3. Land put to non-agricultural uses 18,52,752.00

4. Culturable waste 3,11,015.00

5. Permanent pastures and other

grazing lands 1,22,980.00

6. Miscellaneous tree crops and groves

not included in the net area sown 2,26,811.00

7. Current fallows 10,61,253.00

8. Other fallow lands 10,63,803.00

9. Net area sown 57,25,901.00

Total geographical area 1,30,18,955.00

Annexure 5.1 (b)

Area under forests in Tamil Nadu at the end of each plan period

Area in ha _________________________________________________________

Plan period Reserved Reserved Unclassed Total

Forest land

I 1955 22,36,106 3,87,838 - 26,23,943

II 1960-61 17,23,000 3,81,400 - 21,04,400

III 1960-65 17,25,895 3,79,733 - 21,05,628

Annual 1968-69 17,26,363 3,73,236 - 20,99,599

IV 1973-74 17,27,391 3,65,138 - 22,65,173

V 1978-79 18,38,348 3,83,925 43,900 22,31,986

VI 1984-85 18,32,900 3,37,700 61,386 22,31,986

VII 1989-90 19,20,696 2,43,886 61,386 22,25,968

VIII 1990-91 19,28,740 2,50,222 61,386 22,40,348

Annexure 5.1

Forest area in Tamil Nadu for the decennium

(1981 - 82 - 1991 - 92)

_____________________________________________________________

Year Reserved Reserved Unclassified Total

Forest lands lands

1981-82 18,11,900 3,33,168 56,362 22,01,430

1982-83 18,23,948 3,23,691 56,362 22,04,001

1983-84 18,26,410 3,40,723 56,362 22,23,495

1984-85 18,32,90 3,37,700 61,386 22,31,986

1985-86 18,40,785 3,40,600 61,386 22,42,771

1986-87 18,54,945 3,34,341 61,386 22,50,672

1987-88 18,69,093 3,24,702 61,386 22,55,181

1988-89 18,83,810 3,24,655 61,386 22,25,968

1990-91 19,28,740 2,50,222 61,386 22,40,348

1991-92 19,36,701 2,54,385 61,386 22,52,472

Annexure 5.2 (a)

Population of India, States and Union Territories

1901-1991 (in `000)

Annexure 5.2 (b)

Forest Area under Encroachment as on 31.3.94

State/UT Forest Area under Encroachment (Ha.)

Andhra Pradesh 29159.97

Arunachal Pradesh 49767.04

Assam 219209.04

Bihar 20413.30

Goa .....

Gujarat NA

Haryana 1.69

Himachal Pradesh .....

Jammu & Kashmir .....

Karnataka

Kerala 49917.56

Maharashtra 212949.00

Manipur 67274.00

Meghalaya

Mizorum NA

Nagaland NA

Orissa 74383.00

Punjab 5770.00

Rajasthan 10556.61

Sikkim NA

Tamilnadu 15423.75

Uttar Pradesh 18488.65

West Bengal 60020.00

A & N Islands ---

D & N Haveli

Chandigarh NA

Delhi

Lakshadweep

Pondichery

All India

SECTION 15: Assessment of Forest Plantation Areas

By Dr. D. Pandey ,Director, Forest Survey of India

INDUSTRIAL WOOD PRODUCTION FROM FOREST PLANTATIONS

Countries Plantation areas in Percentage share in 1000 ha. (% of the industrial wood total forest area) production

Argentina 830 (2.2) 60

Brazil 4805 (1.2) 60

Chile 1747 (17.3) 95

Japan 10400 (40.1) 55

New Zealand 1480 (16.2) 95

South Africa 1428 (16.8) 100

Spain 2170 (25.8) 81

Zambia 43 (0.5) 50

Zimbabwe 110 (0.4) 50

UNCERTAINTY ABOUT PLANTATION AREAS-PROBLEMS

EVALUATION / INVENTORY OF PLANTATIONS

DEFINITIONS

REDUCTION FACTORS FOR ESTIMATING NET AREAS OF FOREST PLANTATIONS

Global Reduction Factor 0.70

Regional Reduction Factors

Tropical Africa 0.70

Tropical America 0.80

Tropical Asia and Pacific 0.61

Countries with national level forest plantation inventory

Brazil 0.87

Colombia 0.57

Cote D'Ivore 0.81

Laos 0.47

Nicaragua 0.92

Philippines 0.26

Sri Lanka 0.70

HIGHLIGHTS OF FOREST PLANTATIONS IN DEVELOPING

COUNTRIES - 1995 (million ha.)

PLANTATION AREA BY SPECIES IN INDIA - 1995

SPECIES NET AREA (in ha.)

Auriculiformis 617,600

Other Acacias 1,852,800

Casuarina 617,600

Sissoo 494,080

Eucalyptus 3,088,000

Other HW 3,582,080

Other SW 617,600

Other Pines 247,040

Teak 988,160

Terminalias 247,040



SECTION 16: Planning A Protected Area Network

The National Wildlife Action Plan 1983 advocates for national planning and implementation of a comprehensive network of protected areas. The action plan states that

1.1 Establish a network of scientifically managed protected areas such as national parks, sancturies and biosphere reserves, to cover representative and viable samples of all significant bio geographic sub-division within the country. Such protected areas should have an adequate geographic distribution.

Wildlife Institute of India was entrusted the task to formulate plans for such a network. Four main subjects areas were identified.

a) Preparation of Bio-geographical classification of India

b) Important biological and practical parameters necessary in planning protected areas.

c) A review of existing protected areas.

d) Recommending the new protected areas to ensure an adequate network covering the range of biological diversity in the country

The IUCN Commission on National Parks and protected areas in its working Session in Corbet National Park in India in 1985 formulated the first goal and subsidiary objective of conversation for South and East Asia.

"Goal 1 The establishment of a representative network of protected areas within the Indo-malayan realm

Objective 1.1. To use modern bio geographic concepts to prepare reviews of natural habitats and ecological communities within each nation and determine the adequacy of protected area coverage of these habitats and communities".

The result of this excercise was a biogeogriphical classification prepared by the Wildlife Institute of India (Rodgers and Panwar 1998). The Classification is at four scales,

a) The Bio geographic Zones - Containing major species groupings. Large distinctive units of similar ecology. Distinctive set of physical and historical condition example The Himalayas, The Western Ghats

b) The Biotic Province- Secondary units within a zone, Provinces contain some distinctive species elements as, for example, the differences between the North West and West Himalayas.

c) The Biogeographic Region- A tertiary set of units within a province having typically distinct landforms. They may or may not contain distinctive species elements e.g. Kinnaur, Garhwal & Kumaon distinction within the Western Himalayas

d) The Biomes-major ecosystem groupings found within each province and region e.g. alpine, Sub-Alpine, temperate confer forests within the Western Himalayas.

The principles zones recognised are

1) The Trans-Himalayas

2) Himalayas

3) Indian Desert

4) Semi-Arid Zone

5) Western Ghats

6) Deccan peninsula

7) Gangetic Plain

8) North East India

9) The Islands

10) The Coastal margins

The Biogeographical zones are shown in Table I. The comparison between differernt classification is shown in Table II. The distribution of the area of State into various Biogeographical Zone & Biotic province is shown in Table III.

Table I : The Bio geographic Classification of India (Rodgers and Panwar, 1998)

Biogeographical Zone Biotic Province Biome
A. Palaearctc    
1) Trans-Himalayan 1A) Ladakh Mountains Mountains
(Tibetan)   Steep Valleys
  1B) Tibetan Plains Tundra Valley & Plains
    Lakes and Marshes
2) Himalayan 2A) N.W.Himalaya All with Alpine
  2B) W.Himalaya Temperate Conifer
  2C) Central Himalaya Temperate Broadleaf
  2D) East Himalaya Sub-Tropical Forests
B. Palaeotropical: African    
3) Desert 3A) Thar Saltflats
    Desert grasslands
    Scrublands
  3B) Kachchh Saltflats
    Scrublands
4) Semi-Arid 4A) Punjab Scrublands
    Bhabar forests
    Wetlands
  4B) Gujarat-Rajputana Dry deciduous forests
    Hill forests
    Thorn forests
    Scrublands
    Wetlands
C. Oriental: Indian Peninsula    
5) Western Ghats 5A) Malabar Coast Evergreen forests
    Moist deciduous forests
    Wetlands
  5B) Western Ghats Mountains Evergreen forests
    Moist deciduous forests
    Mounyane forests
    Grassland
    Wetlands
6) Deccan peninsula 6A) Central Highlands Sub-Tropical forests
    Dry deciduous forests
    Moist deciduous forests
    Wetlands
  (1) Satpura-Makai  
  (2) Vindhya-Bagelkhand  
  6B) Chhota-Nagpur Dry deciduous forests
    Moist deciduous forests
    Plateau Wetlands
  (1) Chhota-Nagpur

(2) Garhjat Hills

 
     
  6C) Eastern plateau Sub-Tropical forests
    Moist deciduous forests
    Coastal Plain
    Wetlands
Bio geographical Zone Biotic Province Biome
  (1) Eastern Ghats North  
  (2) Chatisgarh &  
  Dandakaranya  
  6D) Central Plateau North Sub-Tropical forests
    Dry deciduous forests
    Moist deciduous forests
    Wetlands
  (1) Maharashtra  
  (2) Telangana  
  6E) Southern Deccan Dry deciduous forests
    Thorn Forests
    Wetlands
  (1) Tamilnadu Plains  
  (2) Eastern Ghats (South)  
  (3) Karnataka Plateau  
7) Gangetic Plain 7A) Upper Gangetic Plain Sivaliks
    Bhabar-Terai
    Alluvial Plain
    Wetlands/River
8) Coasts 8A) West Coast Mangrove
  8B) East Coast Brackish Lake-lagoon
    Mudflats
    Sandy/Rocky Littoral
  8C) Lakshadweep Island Scrublands
    Coastal Habitats
  8D). Orient : Indo-Malaysian  
9) North-East India 9A) Brahmaputra Valley Bhabbar-Terai
    Alluvial Plain-Grassland
    Alluvial Plain-Woodland
    Evergreen forests
    Moist deciduous forests
    Wetlands/River
  9B) North-East Hills Evergreen forests
    Moist deciduous forests
    Sub-Tropical forests
    Temperate forests
    Wetlands
10) Islands 10A) Andaman Islands Evergreen forests
    Moist deciduous forests
    Coastal habitats
  10B) Nicobar Islands Evergreen forests
    Moist deciduous forests
    Coastal habitats

Table II- Comparison of three biogeographic Classifications for Conservation Planning in India.

Wildlife Institute of India

(1987)

Udvardy (1975)

(Modified 1985)

Mac Kinnon

(1986)

Palaeoarctic
1A Ladakh
Palaeoarctic

2.48.12 Tibetan Highlands

 
    Indo-Himalayan Realm
2A North West Himalaya   South Ha) Western
2B West Himalaya 2.49.12 Himalayan Highlands Himalayas Hb) West Nepal
2C Central Himalaya   Hc) East Nepal
2D East Himalaya   Hd) Eastern
     
Palaeotropical:Ethiopian    
3A Kutch 2.20.7 Thar Desert 15b Salt Flats
3B Thar   15a Thar
     
  Indo-Malayan  
4A punjab 4.14.4 Rajasthan 15d Udaipur Hills
4B Gujarat-Rajwara   8d Gir Peninsula
     
:Indo-Malayan    
5A Malabar Coast 4.1.4 Malabar Rain Forest 1 Western Ghats
5B Western Ghats    
6A South Deccan 4.17.4 Coromandel 12 Coromandel
6B North Deccan 4.15.4 Deccan Teak 14 Deccan
6C Eastern Highlands 4.16.4 Andhra Pradesh 8c North Deccan
6D Chota Nagpur   11 East Ghats
6E Central Highlands   8b Chota nagpur
7A Upper Gangetic Plain 4.13.4 Ganges Monsoon Forest 8a Ganga
7B Lower Gangetic Plain 4.3.1 Bengalian Plain Forest 3a West Bengal
     
8A Brahamaputra Valley 4.4.1 Assam-Burma Rain

Forest

3b Brahmaputra
8B Assam Hills   3c Assam Hills
     
9A Andamans 4.27.13 Andaman/Nicobar 20 Andamans
9B Nicobars 4.27.13 Andaman/Nicobar 21 Nocobars
     
9C Lakshadweep 4.24.13 Laccadives 17 Laccadives
10A West Coast Margins - -
10B East Coast Margins - -

Table III

Zone Zone Name Biounit State   Area (km2)  
        State Province Zone
01 Trans Himalaya 01A Himachal Pradesh 7185.62    
      Jammu & Kashmir 102081.47 109267.09  
    01B Himachal Pradesh 63752.82    
      Jammu & Kashmir 65110.26    
      Sikkim 3140.74 74626.82 183894
02 Himalaya 02A Himachal Pradesh 22714.27    
      Jammu & Kashmir 46082.81 68797.08  
    02B Himachal Pradesh 10915.68    
      Uttar Pradesh 39546.86    
        7.09    
    02C Sikkim 3744.45    
      West Bengal 15534.64 5279.08  
    02D Arunachal Pradesh 79333.75 79333.75 203880
03 Desert 03A Rajasthan 171951.95 171951.95  
    03B Gujarat 18441.81    
      Rajasthan 0.00 18441.81 190394
04 Semi-Arid 04A Chandigarh 155.33    
      Delhi 1309.96    
      Haryana 42769.08    
      Himachal pradesh 7404.89    
      Jammu & Kashmir 8212.08    
      Punjab 49185.21    
      Rajasthan 4875.47    
      Uttar Pradesh 5827.90 119699.91  
    04B Gujarat 114047.10    
      Madhya Pradesh 115616.39    
      Rajasthan 153233.24    
      Uttar Pradesh 7042.00    
      Disputed 9.79 389948.52 509648
05 Western Ghats 05A Dadra & Nagar Haveli 444.83    
      Damman & Diu 35.63    
      Goa 2652.43    
      Gujarat 6974.71    
      Karnataka 9050.03    
      Kerela 22110.51    
      Maharashtra 22860.87    
      Pondicherry 5.85    
      Tamil Nadu 1543.10 65677.96  
    05B Dadra & Nagar Haveli 32.14    
      Goa 420.15    
      Gujarat 2013.63    
      Karnataka 26090.29    
      Kerala 12721.92    
      Maharashtra 13476.96    
      Tamil Nadu 10164.57 64919.66 130598
06 Deccan Peninsul 06A Bihar 2643.42    
      Madhya Pradesh 187478.34    
      Maharashtra 12268.30    
      Uttar Pradesh 29991.37 232381.43  
Zone Zone Name Biounit State   Area (km2)  
        State

Component

Province Zone
    06B Bihar 81894.41    
      Madhya Pradesh 28428.86    
      Orissa 49170.69    
      Uttar Pradesh 2572.81    
      West Bengal 9470.45 171537.22  
    06C Andhra Pradesh 32922.91    
      Madhya Pradesh 85740.66    
      Orissa 82386.04 201049.62  
    06D Andhra Pradesh 116377.45    
      Gujarat 794.21    
      Karnataka 33826.97    
      Madhya Pradesh 9333.91    
      Maharashtra 238177.51 398510.05  
    06E Andhra Pradesh 103043.66    
      Karnataka 119257.14    
      Maharashtra 7610.90    
      Pondicherry 175.09    
      Tamil Nadu 109419.85 339506.65 1342985
07 Gangetic Plain 07A Delhi 145.30    
      Madhya Pradesh 100.48    
      Uttar pradesh 199404.98 199650.76  
    07B Bihar 82814.20    
      Orissa 8813.71    
      West Bengal 50909.38 142537.28 342188
08 Coasts 08A Daman & diu 63.38    
      Goa 494.37    
      Gujarat 46455.28    
      Karnataka 1031.18    
      Kerela 4572.91    
      Maharashtra 3354.20    
      Pondicherry 2.27 55973.59  
    08B Andhra Pradesh 16442.08    
      Meghalaya 0.00    
      Orissa 9920.34    
      Pondicherry 317.92    
      Tamil Nadu 10145.06    
      West Bengal 23358.39 60183.78  
    08C Lakshadweep 462.50 462.50 116620
09 North East 09A Assam 63871.48 63871.48  
    09B Assam 11704.07    
      Manipur 21447.94    
      Meghalaya 21594.08    
      Mizoram 20288.43    
      Nagaland 15976.80    
      Tripura 10036.45 101047.77 164919
10 Island 10B Andaman & Nicobar 1721.66 1721.66  
    10A Andaman & Nicobar 5195.11 5195.11 6917

Conservation status of forests in India linked to the legal category of protection under forest and Wildlife Act. The Wildlife protection Act 1972 recognizes the following categories of protected areas:

1) National parks

2) Sanctuaries

3) Game reserve

4) Closed areas

The terms and definition for FRA 2000 of developing countries in its section II describes the protection status as per IUCN categories as

I- Strict nature reserve/wilderness area. Protected area managed mainly for science or wildness protection.

II- National Park. Protected area managed mainly for ecosystem protection and recreation.

III- National monument. Protected area managed mainly for conservation of specific natural features.

IV- Habitat/species management area. Protected area managed mainly for conservation through management intervention.

V- Protected landscape/seascape. Protected areas managed mainly for landscape/seascape conservation and recreation.

VI- Managed resource Protection area. Protected area managed for the sustainable use of natural ecosystem.

As per Kotka III recommendation the information required is on protection status of

(i) Forests and

(ii) Other wooded land, sub-divided by

(i) IUCN categories 1 & 2, and (ii) IUCN categories 3,4,5 & 6.

IUCN Categories 1 and 2 are almost equivalent to the National parks and categories 4 and 6 to the sanctuary established in India as per the provision of Wildlife protection act 1972

Analysis of protected area data

All analysis and planning has been undertaken within the frame work of biogeographicl classification of India. The protected areas was ascribed to the relevant biogeographic zones and province. Information on biome within each protected area or within each biogeographic unit was not readily available. As an alternative presence/absence data was used for analysis of biome coverage.

Analysis by State and Biogeographic unit:

India has a network of 85 national parks (36619 sq.km., 1.15% of the land area) and 450 wildlife sanctuaries (113169 sq.km., 3.55%) which together covers some 149788 sq.km. or 4.70% of the countries land surface. The size of the sanctuaries and National Parks vary a lot. Protected area distribution is summarised in table V. States with particularly low protected area coverage include Nagaland, Meghalaya, Manipur, Delhi, Haryana and Punjab. Biogeographic zone with no coverage are : Zone 1, the Trans Himalayas; Zone 7, the Gangetic Plain; and Zone 8, North East India. These zones are of considerable conservation importance as they have many critically endangered species. The Protected area in each Bio-geographic zone and Province is given in Table VI.

Table V-Protected area status

                     
State

&

Union

Territory

Area

Km2

Existing Protected Area Status Protected area status (NP+WL)
    No.of

NPs

Area

Km2

% of

state Area

No. of

WLs

Area

Km2

% of

state Area

No. of

PA

Area

Km2

% of

state

area

Andhra Pradesh 268786 4 373.23 0.14 20 11832.27 4.40 24 12205.50 4.54
Arunachal Pradesh 79334 2 2468.23 3.11 10 7114.75 8.97 12 9582.98 12.08
Assam 75576 2 930.00 1.23 8 990.58 1.31 10 1920.58 2.54
Bihar 167352 2 567.32 034 19 3882.51 2.32 21 4449.83 2.66
Delhi 1455 0 0.00 0.00 1 13.20 1.01 1 13.20 1.90
Goa 3567 1 107.00 3.00 4 355.78 9.97 5 462.78 12.98
Gujarat 188727 4 479.67 0.25 21 16744.27 8.87 25 17223.94 9.13
Haryana 42769 1 1.43 0.003 10 342.65 0.80 11 344.08 0.80
Himachal Pradesh 54596 2 1440.00 2.64 31 4792.87 8.78 33 6232.77 1142
Jammu & Kashmir 221487 4 4650.07 2.10 15 10172.15 4.59 19 14822.22 6.69
Karnataka 189256 5 2472.18 1.31 20 4238.21 2.24 25 6710.39 3.55
Kerala 39405 3 536.52 1.36 12 2143.36 5.44 15 2679.88 6.80
Madhya Pradesh 426699 11 6485.72 1.52 35 10704.05 2.51 46 17189.77 4.03
Maharashtra 297749 5 958.45 0.32 24 13995.49 4.70 29 14953.94 5.02
Manipur 21448 2 81.80 0.38 1 184.85 0.86 3 266.65 1.24
Meghalaya 21594 2 269.44 1.25 3 34.21 0.16 5 303.65 1.40
Mizoram 20288 2 250.00 1.23 3 560.00 2.76 5 810.00 3.99
Nagaland 15977 1 202.02 1.26 3 24.41 0.15 4 226.43 1.42
Orissa 150291 2 1212.70 0.81 17 6526.85 4.34 19 7739.55 5.15
Punjab 49185 0 0.00 0.00 6 295.34 0.60 6 295.34 0.06
Rajasthan 330061 4 3856.53 1.17 22 5663.67 1.72 26 9520.20 2.88
Sikkim 6885 1 850.00 12.35 4 161.10 2.34 5 1010.10 14.68
Tamil Nadu 131273 5 401.63 0.31 17 2671.03 2.03 22 3072.66 2.34
Tripura 10036 0 0.00 0.00 4 603.62 6.01 4 603.62 6.01
Uttar Pradesh 284386 7 5430.82 1.91 29 7635.58 2.68 36 13066.40 4.59
West Bengal 85273 5 1693.25 1.99 15 1086.05 1.27 20 2779.30 3.26
Disputed * 166                  
Andaman & Nicobar 6917 8 901.00 13.0394 94 372.13 5.38 102 1273.13 18.41
Chandigarh 115 0 0.00 0.00 1 25.42 25.35 1 25.42 22.10
Dadra & Nagar Haveli 477 0 0.00 0.00 0 0.00 0.00 0 0.00 -
Lakshadweep 462 0 0.00 0.00 0 0.00 0.00 0 0.00 -
Pondicherry 501 0 0.00 0.00 0 0.00 0.00 0 0.00 -
Daman & Diu 99 0 0.00 0.00 1 2.18 3.44 1 2.18 2.20
Total (India) 3182192 85 36619 1.15 450 113169 3.55 535 149788 4.70

Table VI: Summary of Protected Area Statistics in Each Biogeographic Zone

                       
Biogeographic Area Size (sq km) Existing Conservation Status
Zone Province   No.of Parks Area % No. of WLs Area % Total

PA

Area %
1 A 186,200 1 600 2.3 1 200   2 800  
2 A 69,000 3 2,262 2.6 20 1,743 2.0 23 4,025 4.5
  B 72,000 2 718 1.0 15 3,167 4.4 17 3,885 5.3
  C 12,300 3 1,006 8.2 2 228 1.8 10 1,234 10.0
  D 83,000 2 2,290 2.8 4 1,474 1.8 6 3,764 4.5
2 Total 236,300 10 6,296 2.5 46 6,612 2.6 56 12,908 6.0
                       
3 A 45,000 0 0 0 2 5,718 12.7 2 5,718 12.7
  B 180,000 0 0 0 3 3,179 1.8 3 3,179 1.8
3 Total 225,000 0 0 0 5 8,897 4.0 5 8,897 4.0
                       
4 A 107,600 1 28 0.0 11 307 0.3 12 335 0.3
  B 400,400 4 842 0.2 37 10,546 2.7 41 11,388 2.9
4 Total 508,000 5 670 0.2 48 10,853 2.2 53 11,723 2.3
                       
5 A 59,700 1 86 0.1 3 154 0.3 4 240 0.4
  B 99,300 6 1,987 2.0 35 13,708 13.7 41 15,695 15.6
5 Total 159,000 7 2,073 1. 37* 13,862 8.7 44 15,935 10.0
                       
6 A 378,000 2 107 0.0 13 2,403 0.6 15 2,510 0.6
  B 341,000 2 251 0.1 21 16,772 4.9 23 17,023 5.0
  C 198,000 2 1,458 0.7 18 5,898 3.0 20 7,356 3.7
  D 217,000 2 516 0.2 25 7,946 3.2 27 7,562 3.5
  E 287,000 9 4,949 1.7 21 8,710 3.0 30 13,639 4.8
6 Total 1,421,000 17 7,281 0.5 98* 40,829 2.9 115* 48,110 3.4
                       
7 A 206,400 2 1,011 0.5 9 2,337 1,1 11 3,348 1.6
  B 153,000 1 10 0.0 13 896 0.6 14 906 0.6
7 Total 359,400 3 1,021 0.3 22 3,233 0.9 25 4,524 1,3
                       
8 A 65,200 1 696 1.1 5 584 8.9 6 1,280 2.0
  B 106,200 3 310 0.3 8 292 0.3 11 602 0.5
8 Total 171,400 4 1,006 0.6 15 876 0.5 17 1,882 1.1
                       
9 A 6,397 6 363 5.7 91 327 5.4 97 690 10.8
  B 1,930 0 0 0 3 20 1,0 3 20 1,0
  C 180 0 0 0 0 0 0 0 0 0
9 Total 8,507 6 363 4.3 94 347 4.1 100 710 8.3
                       
10 A   1 163   3 481   4 644  
  B   1 1,330   12 2,459   13 3,789  
10 Total   2 1,493   15 2,940   17 4,433  
Total Country - * 21,003 0.6 * 88,649 2.7 * 109,652 3.3

· The country total may not tally as several areas fall in two zones and in more than the province.

A clearer picture emerges from an examination of PA status in the biotic provinces

(Table VII).

Table VII: Distribution by Biotic Provinces

  Province Esisting PA coverage
1A Ladakh <1%
3B Thar Desert 1.8
4A Punjab Plains 0.3
5A Malabar Plains 0.4
6A South Deccan 0.7
7A Upper Gangetic Plains 1.6
7B Lower Gangetic Plains 0.6
8B Assam Hills 0.5
9B Nicobar Islands 1.0
9C Lakshadweep 0.0
10A Western Coast 4.0

Several PAs have a network totalling below 2.0% of the land area. These are:

1A Ladakh <1.0%

3B Thar Desert 1.8%

4A Punjab Plains 0.3%

5A Malabar Plains 0.4%

6A South Deccan 0.7%

7A Upper Gangetic Plain 1.6%

7B Lower Gangetic Plain 0.6%

8B Assam Hills 0.5%

9B Nicobar Islands 1.0%

9C Lakshadweep 0.0%

In some cases the lack of PA reflects the lack of natural vegetation and wildlife habitat within the province, as for example 4A, 5A, 6A, 7A, 7B. But in other cases there are still large areas of forested and/or non disturbed lands, e.g 1A, 3B. 8B, 9B.

The data analysis shows that the Pas are not distributd uniformly across the states or across the Biogeographic Zones or provinces of the country. Some states are well covered, other poorly covered.

(a) National Parks: Best covered states are:

i) Sikkim - 850 sq km or 12.75%

ii) Arunachal Pradesh - 2468 sq km or 3.11%

iii) Himachal Pradesh - 1449 sq km or 2.64%

iv) Jammu & Kashmir - 4656 sq km or 2.10%

v) Uttar Pradesh - 5430 sq km or 1.91%

(b) Total Protected Area: Best covered states are:

i) Tamil Nadu - 1011.10 sq km or 14.68%

ii) Goa - 462.78 sq km or 12.98%

iii) Arunachal Pradesh - 9582.98 sq km or 12.08%

iv) Himachal Pradesh - 6232.87 sq km or 11.42%

v) Gujarat - 17223.94 sq km or 9.13%

vi) Jammu & kashmir - 14822.22 sq km or 6.69%

v) Kerela - 2679.88 sq km or 8.80%

(c) States with a low coverage of PA include:

i) Nagaland - 226.43 sq km or 1.42%

ii) Meghalaya - 303.65 sq km or 1.40%

iii) Manipur - 266.65 sq km or 1.24%

iv) Delhi - 13.20 sq km or 0.90%

v) Haryana - 344.08 sq km or 0.80%

The distribution of protected areas by biomes having no or unsignificant area coverage includes:

Cachar Evergreen/Semi Evergreen Forest

Orissa Semi Evergreen Forest

Hollock Forests

Littoral Forests

Tropical Swamp Forests

Hardwickia-Albizia Dry Forests

Southern Tropical Thorn Forest

Tropical Dry Evergreen Forest

East Subtropical Broad leaf Hill forest

Subtropical Dry Evergreen Forest

Desert Grass and Scrublands

Saltflat Grass and Scrublands

Dry Grasslands

Riverine Habitats

Wetlands

The Wildlife Institute of India has made proposal for extending the protected area net work by either creating new protected area or extending the existing areas. The overall increase proposed is 41,690 sq. km raising the coverage to 4.6%. This proposal was made in 1988 and the present figure show that the protected area coverage is now 4.7% of the geographical area of the country.

SECTION 17: Shifting Cultivation in Nagaland : A Pilot -Study

V. N. Pandey, Joint Director, FSI

Introduction

Shifting Cultivation is practised as a form of subsistence agriculture. In shifting cultivation a forest land is cleared, the slash is burnt and the land is cultivated for a short period. Then the land is left fallow for 5-10 years during which forests develop on the abandoned land, which are cut and burnt to recultivate the land once again. This is a pernicious practice which causes loss of fertility, soil erosion, degradation of forests and loss of forest cover and biodiversity. In India, it is known by various names like jhum in the North-Eastern region, podu in Andhra Pradesh, dahiya in Madhya Pradesh, kumri in Tamil Nadu.

Many attempts have been made in the past to gather information in regard to the practice of shifting cultivation like location, extent of area covered, population dependent on it etc. A report by North-Eastern Council (NEC) in 1975 estimated the total area affected by shifting cultivation in the north-eastern region to be 2.70 million ha, while the FAO estimated it in 1981 to be 9.00 million ha which included north-eastern region and Orissa. The Task Force on states of India, extensively in Arunahal pradesh, Assam , Manipur , Meghalaya, Mizoram, Nagaland, Orissa and Tripura, and rather restrictedly in Andhra Pradesh, Bihar, Kerala, Karnataka, Madhya Pradesh, Maharashtra, Sikkim and West Bengal. It estimated the forest area affected by shifting cultivation at 4.35 million ha and number of families practising it at 6.22 lakh.

Forest survey of India has estimated the area under shifting cultivation in the north-eastern region to be 6.2 million ha. (SFR 1989) using satellite data. Details of these estimates are furnished in Table 1.

Table 1. Area affected by Shifting Cultivation in North-Eastern region and Orissa

         
State Estimate of Shifting Cultivation (million ha)
  NEC FAO Task Force FSI
Arunachal Pradesh 0.25 0.79 0.21 0.85
Assam 0.50 0.42 0.14 0.73
Manipur 0.10 1.78 0.36 1.38
Meghalaya 0.42 1.02 0.26 0.68
Mizoram 0.60 1.61 0.19 1.24
Nagaland 0.61 1.10 0.08 1.06
Tripura 0.22 0.62 0.11 0.33
Orissa - 1.66 2.65 -
Total 2.70 9.00 4.00 6.27

The studies relating to jhum cycle (crop-fallow cycle) reveal that cycles are of varying length. It is reported that his cycle, about 50-60 years back, was in the range of 30-40 years or 20-30 years. This has become as short as 3-6 years. It does not allow the natural processes of recuperation to repair the damages to the ecosystem. The cycles as observed in different states is given in Table 2.

Table 2: Shifting Cultivation (jhuming) cycle in different states of North-Eastern Region

     
States Period in Years
  National Commission on Agriculture (1976) Central Forestry Commission (1984)
Arunachal Pradesh 1-17 4
Assam 5-10 7
Manipur 6-8 6
Meghalaya 4-5 6
Mizoram 4-5 8
Nagaland 6-15 9
Tripura 4-5 8

Thus, it is evident that the estimates by different agencies are highly variable. The cause of variability seems to be inadequate data base in the absence of regular survey and estimates based on small samples.

Shifting Cultivation in Nagaland

The North-Eastern region of the country, which is rich in biodiversity, had been under large scale shifting cultivation. Because of high incidence it is proving to be very destructive. In this light, it is important to make assessments of jhuming in N-E region on a continuing basis. The remote sensing technology can be used as a tool for regular assessment of areas affected by shifting cultivation in a comprehensive manner. The study has been undertaken to assess the area under shifting cultivation in the state of Nagaland using multi-date satellite data. Satellite data used in the study are on 1:250,000 scale. The assessment is for the period from 1989 to 1996 i.e. a period of eight years between the 4th and 7th cycle of forest cover mapping of the country.

Methodology

1. The state of Nagaland is covered by 7 toposheets on 1:250,00 scale, a scale on which the forest cover map of the country is prepared. The interpretation of satellite imageries during the 7th cycle reveals that the total non-forest area in the state is 0.2319 million ha. Out of this, the area covered by permanent cultivation is 0.0610 million ha. The area of permanent cultivation was subtracted from the total non-forest area thus giving the net area affected by shifting cultivation. The total area affected by shifting cultivation was found to be 1709 sq. km. These areas were marked on a map.

2. The map showing areas of shifting cultivation of the 7th cycle was superimposed on the forest cover map of the 6th cycle. The areas affected by shifting cultivation as observed in the 6th cycle were also marked on the same map ignoring the areas common to 6th and 7th cycles.

3. The same process was repeated for 5th and 4th cycles so as to bring together all the areas affected by shifting cultivation from 4th to 7th cycle.

4. Thus all the areas affected by shifting cultivation during 1989 and 1996 were, brought on one map. It presented a consolidated account of shifting cultivation areas of 8 years. If the same area is found to be under shifting cultivation in two or more cycles, it was counted only once to avoid double counting.

Results

The total area affected by shifting cultivation during the aforesaid period of eight year was found to be 0.3580 million ha. The detail of area affected by shifting cultivation during 1989-1996 is given in Table 3.

TABLE 3 : AREAS AFFECTED BY SHIFTING CULTIVATION (jhuming) IN NAGALAND DURING THE PERIOD 1989-1996

( Note : Figures in parentheses are the areas of shifting cultivation in different cycles)

In view of the above findings it is imperative to undertake a comprehensive study on the shifting cultivation on a continuing basis, so as to arrive at a more reliable estimate of shifting cultivation.

SECTION 18: Purpose Minimum Protected Area Network for Low-Country wet zone of Sri Lanka in the Districts of Matara, Galle, Kalutara and Ratnapura.

by Sunil Liyanage, Deputy Conservator of Forests (Environmental Management), Sri-Lanka

SEMINAR ON THE REVIEW OF THE PROTECTED AREA NETWORK OF THE LOW COUNTRY WET ZONE BASED ON THE RESULTS OF THE NATIONAL CONSERVATION REVIEW

The plant world is essential to human life and plants in their variety supply not only food, but also useful and vital products to cover virtually every need; timber, medicines, paper contraceptives, fibres, spices, resins and many others. Plant cover protects soils from erosion and has a significant effect on weather patterns, as well as forming the central feature of our natural environment. Although Sri lanka, is a small island, it has a wide range of topographical and climatic variation and these contribute to the wide range of ecosystem diversity of the country. There are 23 natural eco system in the country, which belong to four main categories; Forest, Grasslands, Coastal and marine belts, and inland wetland. Forests is the most important ecosystem of the country, which has seven sub-ecosystems, including highly diverse" Tropical Wet evergreen forest or lowland rain forest in the low country wet zone.

Natural forests contain most of the floral diversity in Sri lanka with wet zone forests having the highest endemism. Around 30% of angiosperm flora and 18% of the ferns of the country are endemic. Most of this unique floral diversity concentrate in the lowland tropical rain forest. The rainforest of this region are among the biologically richest ecosystem in the whole of South Asia and consequently recognized as a `Biodiversity Hotspot" of global significance by conservation biologists (1.A.U.N Gunatilake-1994). Most of the threats to plants can be traced o man's need for food. In many locations this is leading not only to loss of species and degradation of the vegetation over very extensive areas, but also to a loss in agricultural potential itself, both in the short and long term. However it is the diversity of the plant world and threats to individual species that are more important in ecological and economical perspectives. Species data are also important in highlighting the most valuable of what are apparently superficially similar habitats. They can provide a powerful argument to strengthen existing proposals for conservation of paricular sites, complementing arguments based on ecosystem grounds where the principal aim is to ensure that adequate and representative samples of all ecosystems are protected. Conservation of the country's plant resources will assume an ever-increasing importance as social and economic development continues.

It is necessary to established proper protected area network to conserve these floral and faunal diversity. Although many scientist and researchers studied biodiversity of the country, a complete study has not been carried out. Forest Department initiated a project call "National Conservation Review (NCR)", with the financial assistance of UNDP and technical assistance of IUCN, to assess the biodiversity of natural forests. The main objective of NCR is to assess the biodiversity of natural forests of the country within a minimum time frame in order to determine a minimum net work of protected areas to represent the biodiversity of Sri Lanka and conserve hydrologically important natural forests. NCR covers all the natural forests where forest area exceed 200 ha, but in specific forest area, it covers even areas smaller than 200 ha depending on forest type, species diversity etc. Gradsect sampling system was used to maximize the biodiversity assessment and on a line transect, 5 m * 100m plots are laid down to assess the diversity. Depending on the forest type and habitat characteristics, the plot interval may vary from 100 m to 200 m or even more. Plot location is determined by using land-use and other maps. In each plot woody flora and fauna studied and number of individuals of all woody flora above 10 cm DBH, if recorded. Faunal survey is limited to mammals, reptiles, birds, amphibians, fishes land molluse and butterflies. The data collected during the field survey is entered into a computerized data base which has the ability to analyze these data. This analysis provides the biodiversity of each forest area indicating endemicity, rarity and unique species as well. It also provides the biodiversity of each forest area indicating endemicity, rarity and unique species as well. It also provides a ranking of forest areas on biodiversity according to endemism, rarity and uniqueness. A hydrological study also carried out as a separate component of NCR and all the natural forests were ranked according to importance of hydrological aspects. These information indicates the minimum number of forest areas required to be conserved in order to preserve all species.

Tropical Wet evergreen forest or lowland rain forest of the country, spreads over wet zone areas of matara, Galle, Kalutara and Ratnapura districts. The extent of natural forest areas studied in four districts are given in Table-1. The information presented regarding floral diversity, has been compiled through the net-work of Environmental Information Management System of the Forest Department and the floristic diversity of each district is also given in Table-1. Only the woody plants were studied and the situation as lower (non-vascular) plants is not well known, however one must anticipate that, in groups such as lichens, mosses and liverworts, species will be found where merit protection as much as any vascular species. Many of the flora are already known to be of value to man.

Total of 619 woody flora sp. Identified in these four districts (Annex-1) and there are302 endemic species. Faunal diversity is also very high and 25 molluses,52 insects 30 fishes, 30 amphibians, 50 reptiles, 111 birds and 25 mammals were recorded (Annex-2). Amongst the 75 forest identified, 31 forest falls within first fifty of the hydrological ranking (Annex-3).

Table-1 Forest areas studied under NCR

               
District Number of forests studied Total forest area in ha. Floristic diversity Faunal diversity Spp
      Spp Gen Fam End  
Matara 17 12539 418 447 252 198 426
Galle 19 36130 420 261 266 205 426
Kalutara 11 18782 399 257 260 194 402
Ratnapura 36 40640 628 332 338 266 555

Conservation of floristic diversity, especially endemism, considered as the main criteria in the selection of forest for protection. However faunal diversity and hydrological ranking were also given equal importance in the selection procedure. Biodiversity of the existing protected area net-work of the low country wet zone is given in Table-2.

Table-2 Existing protected area net-work of the Forest Department in the low country wet zone

                   
Forest Floristic Diversity Fanunal Diversity Hydrlogy
  New sp. Prot. Sp. N.P. Sp. Tot

Sp.

End

Sp.

Thr.

Sp.

Ero.

Rnk.

Hyd.

Rnk.

Fi.

Rnk.

Sinharaja 337 337 141 106 40 38 84 7 18
Kanneliya 27 415 63 64 26 18 97 13 33
Gilimale-Eratna 51 388 90 103 47 34 65 23 14
Kalugala 14 49 49 68 30 25 97 44 54
Massenna 13 442 36 44 18 16 5 104 30
Bambarabotuwa 11 453 25 86 28 20 81 35 40
Dellawa 8 461 17 82 27 26 26 14 4
Nahiti Mukulana 5 466 12 63 20 17 155 153 174
Oliyagankele 4 470 8 0 8 5 182 251 238
Velihena 3 473 5 24 4 4 181 224 227
Delwala 2 475 3 55 27 21 63 50 37
Kombala-Kottawa 2 477 1 57 23 19 187 131 178
Kekanadura 1 478 0 20 8 5 228 251 263
Viharakele 0 478 0 28 10 9 180 203 217
Kandewattegoda 0 478 0 26 8 3 155 228 216

Hydrological parameters also an important guide line in selection of forests areas for conservation. The management strategy of natural forests for timber production is very more environmentally friendly and these forests will be managed only for sustainable timber production. Environmental assessment will be carried out in these forests before any felling activity and in this assessment environmental aspects will be considered as higher priority. Even in felling, environmental safeguard will be considered and fellings will be carried out only according to the approved guidelines of the Forest Department and State timber Corporation. It is understood that in this context, hydrological ranks were considered as a important criteria. In this context, only the biodiversity selected as the criteria for identification forests for conservation. Faunal diversity also considered in selection of forests for conservation, but it is assumed that fauna will be conserved, when forests areas identified for conservation of flora. However the total faunal diversity of natural forests not studied in the NCR and only few important groups studied with details. In this context, floristic diversity and endemism used as the basic tool, in the selection of forest areas for conservation. However, forest areas having higher faunal diversity and endemism considered as important criteria and those forests were included to the net-work.

To conserve total floristic diversity of the low-country wet zone, it is ecessary to conserve 34 forests other than existing protected areas. However it is not feasible to protect or conserve a particular forest area only for conservation of few species. Some of these species are not endemic and not in nationally or internationally threaten status. In this context, it is decided to exclude these forests from the protected area net-work, allocating reasonable forests areas for timber production. Species which are not protected under the PAN, will be conserved in ex-situ in the natural forests areas like Delwala, Gilimale, Diyadawa.

Only 478 flora protected in the existing protected forests and this have to be expanded in order to include the remaining floristic diversity. The following forests were selected to protect floristic diversity which was not represented in the existing areas Table-3

Only 478 flora protected in the existing protected forests and this have to be expanded in order to include the remaining floristic diversity. The following forests were selected to protect floristic diversity which was not represented in the existing areas Table-3.

Table-3 Proposed forests areas for conservation of biodiversity in addition to existing protected forests network.

                   
Forest Floristic Diversity Faunal Diversity Hydrology
  New

Sp.

Prot.

Sp.

N.P.

Sp.

Tot.

Sp.

End

Sp.

Thr.

Sp.

Ero.

Rnk.

Hyd.

Rnk.

Fl.

Rnk

Morapitiya-R'kanda 248 248 269 81 28 24 50 31 35
Handapan Ella 72 320 197 57 21 16 58 10 11
Rammalakanda 54 374 143 72 26 23 9 105 38
Naklyndenlya 37 411 106 60 22 17 117 26 59
Walawe Basin 18 429 88 46 13 11 34 24 7
Kalubowitiyana 14 443 74 40 14 9 1 93 19
Kiribatgala 13 456 61 52 19 19 96 164 139
Ingiriya 10 466 51 43 13 8 168 173 192
Haycock 9 475 42 43 20 14 15 160 72
Morahela 8 483 34 62 24 19 54 54 29
Ranwaragalakanda 7 490 27 37 7 3 98 134 186
Kurulugala 7 497 20 28 12 8 93 147 115
Diyadawa 5 502 15 88 22 20 16 15 3
Gongala 5 507 10 38 17 11 70 34 25
Yagirala 4 511 6 39 12 7 141 98 114
Silverkanda 3 514 3 31 12 10 43 94 49
Tiboruwakota 3 517 0 33 14 10 12 120 46

With these forests, there are 32 foress areas identified for conservation of biodivversity and hydrological aspects. Total area of the forests selected for conservation is 29,501 ha (Table-4). This indicate that total protected area net-work of the Forest department in the low country wet zone is about 60586 ha (Table-5)

Table 4: Extent and Number of forests selected for conservation

District Number of forests studied Total Forest area in ha. No of forests selected for conservation Extent of selected forests Percentage of selected forest area
Matara 17 12539 4 5029 40.11%
Galle 19 36130 4 3298 9.13%
Kalutara 11 18782 4 9597 51.10%
Ratnapura 36 40640 5 9577 23.57%
Total 83 108091 17 27501 25.44%

Table 5. Protected Area Network for Low Country Wet Zone

Name of Forest Extent (1ha.)

Sinharaja 11300

Kanneliya 6024

13 wet zone forests 24038

17 selected forests 19224

TOTAL 60586

FRA Working Papers

1998

1999

Please send a message to fra@fao.org for electronic copies.