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Methodology and definitions



Preparatory Studies

Country data, available in the form of statistical tables or maps, have been the main source of information for the developing countries* part of the global forest resources assessment and the direct determinant of its quality and content. Therefore, a review of the current state of the country forest inventories is an appropriate starting point for discussion of the methodology of the Forest Resources Assessment Project. (see Table 17)

The findings on the current state of the country forest inventories can be summarized as follows:

Undisplayed Graphic There is considerable variation among regions with respect to completeness and quality of the information, with Asia faring better than tropical America and the latter better than tropical Africa;

Undisplayed Graphic There is considerable variation in the timeliness of the information. The data are about ten years old, on average. This is a potential source of bias in the assessment of change;

Undisplayed Graphic There are some countries which have carried out more than one assessment. These countries, however, have not used appropriate techniques, such as Continuous Forest Inventory (CFI) design, for change assessment;

Undisplayed Graphic Only a few countries have reliable estimates of actual plantations, although such estimates are essential for national forestry planning and policy-making;

Undisplayed Graphic No country has carried out a national forest inventory containing information that can be used to generate reliable estimates of the total woody biomass and its change over time.

The above findings establish the fact that the existing forest resources information is not adequate to meet the needs of global assessment.

Review of approaches to assessment

The expert consultation held at Kotka, Finland in 1987 (Kotka I) recommended that, for developing countries, the 1990 assessment follow the approach of the FAO/UNEP 1980 assessment for estimating the forest cover area at end 1990 and then estimate the change by taking the difference between the 1990 and 1980 figures. The first results demonstrated clearly, however, that this approach is not suitable for the estimation of change over time, because the resulting estimates of change had high variance, being the sum of the variance of 1980 and 1990 estimates according to the law of propagation of errors.

Keeping the above limitations in view, investigations were initiated to find an approach which could produce acceptable results making the best use of available data, appropriate techniques and new technology (remote sensing, GIS, computerised database management and modelling techniques). The following were some of the tools and techniques investigated:

i) Use of a database management system for easy storage, retrieval, analysis and updating of information;

ii) Introduction of a modelling technique to estimate deforestation objectively;

iii) Use of auxiliary variables, dynamic in nature, such as population density and population growth, for which data are readily available and which are among the important driving forces behind deforestation;

iv)Reduction in size of the assessment unit from national to a subnational level, which is ecologically and demographically more homogeneous;

v) Use of variance-reducing techniques while making estimations, such as stratification of the subnational units by ecological criteria.

Methodology of the 1990 assessment

The assessment technique is presented in the form of a flow chart in figure 15. It consisted of the following three steps: (i) establishment of a computerized database; (ii) development of a deforestation model (or an adjustment function); and (iii) computation of results for the standard reference years.

Establishment of a database

The data compiled by the Project consisted of two categories:

i) Tabular data, including forest resources, population and socio-economic data at the subnational level (province, state) on a consistent basis over the entire developing world;

ii) Spatial data, including ecofloristic zones, vegetation types and national and subnational boundaries.

A comprehensive Database Management System (DBMS) was developed in order to include statistical (FORIS) and spatial (GIS) variables used in the assessment process. Statistical variables mainly reported, and consequently stored in the data bank, at subnational unit level were integrated with cartography representing the units in a spatial way. Since both model variables and model results were stored in the DBMS, users can access the information through both statistical and spatial queries. Being the deforestation a location-specific process influenced by a number of spatial factors, various information layers were introduced in the GIS for further studies. These layers include: protected areas, road and railroad networks, hydrology, topography, climatological data and satellite information such as vegetation maps derived from interpreted Landsat or NOAA imageries, Landsat World Reference System Grids.

More than 100 tropical and non-tropical (limited to Africa and South America) countries are included in the data bank. This body of information, which will be made available to the international community during 1995 in the form of CD-ROM, includes, in an integrated manner: a) demographic, vegetational and ecological data which were used during the forest resources assessment and b) baseline results for the reference years 1980 and 1990.

GIS and tabular databases represent an important outcome since they are at the same time output of the forest resources assessment activities and input for further studies. The deforestation process dynamics requires a continuous effort in keeping this information up-to-date, especially from the side of the mandatory international bodies (FAO and other UN agencies) in close cooperation with the recipient countries which play the major role of users and producers of the information.

Adjustment function

The forest cover data contained in FORIS referred to different periods and needed to be brought for reporting purposes to the standard years, namely 1980 and 1990. This was done with the help of an adjustment function (Syn. deforestation model) which correlates forest cover change in time with other variables including population density and population growth for the corresponding period, initial forest cover area and the ecological zone under consideration. For developing the function only multi-date data were used. The best fitting curve had the form of a logistic function.

The model has proved to be a valid and flexible tool with which to estimate deforestation at a global level and to relate forest cover changes with demographic and ecological variables. Given the scarcity of existing reliable multi-date observations, the model serves a very useful purpose in global forest resources assessment.

Estimation procedure

The FORIS database, in conjunction with the model, served: (i) to adjust forest cover data of the subnational units to the standard reference year 1990; and (ii) to produce estimates of the forest cover area change over the period 1981 to 1990. For these purposes the most recent forest inventory observation of a national/subnational unit is used as baseline, and forest cover area in 1980 and 1990 (standardized results) computed according to one of the following options:

i) Reliable multi-date inventories available: in this case the existing multi-date information is used to calibrate the general model with the local parameters and the resulting model is then used to compute the standardized results. This is the optimum case;

ii) Reliable single-date inventory available: in this case the standardized results are computed using the general model;

iii)No reliable inventory available: in this case estimates of baseline forest cover area are extracted from calibrated vegetation maps existing in the Project*s GIS and are then used as input to the modelling procedure described for option (ii).

For each option, procedures were developed for integrating the FORIS and GIS data to compute the model parameters for each subnational unit and to provide forest cover areas 1980 and 1990 as standardized outputs.

The standard estimates for the forest cover state and change at subnational level are aggregated at national, regional and global levels. Keeping in view the law of propagation of errors, the global estimates are expected to be more precise than the subregional; and the subregional estimates more precise than the national and subnational ones.

In the present system of assessment the up-dating of results is an almost continuous process. Both the FORIS database and deforestation model parameters can be updated as and when additional country data become available. The addition of further records and fields in FORIS will improve the reliability of model parameters as well as precision of estimates for the country, which in turn will improve the precision of the estimates at regional and global levels.

State of forest biomass and assessment of change

Biomass of forests has become very relevant to studies related to global change. The biomass of forests provides estimates of the carbon pools in vegetation (about 50 per cent of biomass is carbon), and consequently the potential amount of carbon dioxide that can be added to the atmosphere when the forest is cleared and/or burned. Biomass is also a useful variable for comparing structural and functional attributes of forest ecosystems across a wide range of environmental conditions.

Not all tree biomass for domestic use originates from forests; significant quantities are obtained from nonforest lands such as woodlots, windbreaks and other line formations, home gardens, etc. It is recognised that these sources should be assessed in the future, but it was beyond the present scope of the assessment.

To estimate the biomass density of forests, use was made of existing volume over bark estimates (VOB) in the FORIS database which is converted to biomass density with the help of a biomass estimation function and further “expanding” this value to take into account the biomass of the other above ground components.

Research results based on actual inventory data across the tropics, show that for tropical broadleaved forests biomass expansion factors are significantly related to stemwood biomass (SB) according to the following model:

BEF = exp{3.213 - 0.506*ln(SB)}

for SB < 190 t/ha

= 1.74 for SB ³ 190 t/ha
The following function for estimating biomass from volume information was used 

BD (t/ha) = VOB * WD * BEF


    BD = biomass density

    VOB = volume over bark of all trees to 10 cm minimum diameter (m3/ha)

    WD = average wood density (t/m3); values obtained from FRA 1990 guidelines

    BEF = biomass expansion factor

No model is available at present for calculating biomass expansion factors for coniferous forests, because of a general lack of data for this type of analysis. Therefore the estimates presented in the Project guidelines were used.

This approach is of unknown reliability, because much of the VOB data were estimated generally through extrapolation from existing local and international forest inventories. However, this method has the advantage of being pan-tropic and can therefore be used with area estimates for 1990 to produce an assessment of the total biomass of tropical forests.

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