Chapter I: Introduction

Over the last two decades, concern about the world’s forests has risen dramatically. Large forest areas have been converted to other land uses, or severely degraded. At the same time, it has been increasingly recognized that forests and trees provide for crucial economic, environmental and social needs in many countries.

The world has billions of trees that are not included in the Forest Resource Assessment definitions of "forests" and "other wooded land" (FAO 2001). Trees outside forests (TOF), trees and tree systems found on agricultural land, on meadows and grazing lands, on unproductive lands, along canals, railways, roads and in human settlements, have numerous, often essential, roles and functions. They make a critical contribution to agriculture, food security and rural household economies. They supply many products (e.g. wood for fuel and construction, fodder, fruits, bark and food) and services (e.g. biodiversity, carbon storage, habitat for wildlife, microclimate stabilization, soil and water conservation). As agroforestry systems, they serve a number of ecological and economic functions that are similar to those of forests in principle, although different in extent (Kleinn 2000). In their study on trees on farms in Kenya, Holmgren et al.(1994) point out the forest policy implications when a considerable share of wood resources is derived from non-forest lands.

The use of trees in farming systems dates back to the beginning of domestic agriculture. More recently, interest in partnerships (e.g. outgrower scheme, joint ventures) between the private and public sectors and communities and individuals for the production of goods and services outside forests has been increasing. In temperate agricultural landscapes, trees and shrubs mainly occur in the form of scattered trees, windbreaks, block and linear plantations. For centuries, farmers in India have maintained a traditional land-use system known as "sacred groves", in which a separate area with trees was set aside. Trees are also a vital component of the urban landscapes.

FAO’s definition of TOF depends on the definition of forest and other wooded land. Different definitions will affect inventory results. The extent to which the results vary with different definitions depends on factors such as the geometrical formation of trees and forest patches (Kleinn 1991), legal ownership, data precision and land-use practices.

Comprehensive classification schemes have been elaborated in agroforestry (e.g. Nair 1987; Sinclair 1999), but there is no such classification system that embraces all TOF. A key question is whether TOF constitute an area (i.e. are they geometrically defined) or whether the resource must be described in other ways, such as growing stock per unit of area on non-forest land. Both options are valid and allow map representations of the resource that are useful for different purposes (Kleinn 2000).

In classifying TOF and the land where TOF are found, one of the main confusions is that both land cover (biophysical: how much of the land is covered by tree crowns?) and land use (socio-economic: is the land used mainly for forestry?) should be taken into consideration (Kleinn 2000). Many tea gardens, coffee plantations and pastures, for example, would qualify as forest, if classified according to their tree cover but are explicitly excluded by FAO from the definition of forest.

For a large-area inventory, the classification must be practical. Two general criteria are considered useful:

the land use where TOF are found; and

the geometry of the resource.

The two criteria are not mutually exclusive but can and should be incorporated into a single classification system, with one or the other considered the principal criterion, depending on the information required.

Along with the attempt to design a TOF classification system, a major issue is the identification of variables that characterize the trees physically and functionally, enabling proper classification to meet the requirements of the particular inventory.

The TOF resources in general are independent of forest resources, and are an integral part of the non-forest landscape having ecological and economic functions of their own. Therefore, they should be taken into consideration in large-area natural resource planning.

In some countries (e.g. India, Colombia and Costa Rica), forest legislation also extends to TOF (e.g. with regards to felling permits). Most countries have policies for the (sustainable) management of forest resources. However, little attention is given to the dynamics of TOF, although information on TOF is crucial for developing management options to maintain tree cover and plan wood production (Kleinn 2000).

Besides these technical justifications, a number of international agreements and commitments (e.g. the Forest Principles of Agenda 21, the Convention on Biological Diversity and the Framework Convention on Climate Change) emphasize that an appropriate database is a prerequisite for sound management of the world’s natural resources. While these agreements and commitments generally refer to forest, the idea of sustainable management of natural resources applies to TOF as well (Kleinn 2000).

Sylvander (1981) reported on forest cover mapping including TOF for Costa Rica carried out in 1967 and 1977, as part of a forest inventory project. Using FAO (1974) guidelines five classes, based on tree cover and land use were distinguished and mapped. For all classes, the percentage of crown cover was determined, including all trees in or outside forests. The results indicated that in 1967, 23.7 percent and in 1977, 30.4 percent of tree cover was outside the class comprising large, closed forests. Sylvander’s mapping approach focused on trees and not only on forest.

In Kenya, Holmgren et al. (1994) inventoried tree resources on farms using a two-phase sampling design; aerial photos were used in the first phase and field measurements in the second. They found this design suitable for the inventory of scattered trees on farms. Combining their results with other sources of information, the authors concluded that only one-third of the woody biomass in Kenya was found within traditional forests.

A study in Haryana State in India, an intensively cultivated state with about 3.8 percent of its area classified as forestland but only about 2 percent under actual forest cover (FSI 1999), showed that farm forestry (trees along farm bunds and in small patches of up to 0.1 ha) accounted for 41.2 percent of the total growing stock of wood. Multiple tree rows along roads and canals accounted for 13 and 9.6 percent, respectively; village woodlots for 24 percent; and block plantations of less than 0.1 ha for 10.6 percent (FSI 1999).

Land use and land cover are important factors for TOF inventory and therefore, the classification rules should be formulated in such a way that these factors are suitably considered. Since sources of information for land use and land cover are different, and in some cases segregation between land use and land cover is difficult, some other method may need to be adopted for classification purposes. The geometric resolution of an image allows the determination of crown cover, tree density and spatial arrangement of trees (or crowns). Other important attributes (e.g. species, stem, DBH, crown width) are more reliably observed in the field. Non-biophysical variables such as ownership and type of tree management can also be observed in the field.

High-resolution satellite images can provide information at multiple scales. Even single trees can be sensed. However, high-resolution images are expensive. Multi-spectral LISS III data, with a resolution of 23.5 m ´ 23.5 m, can provide information on vegetation cover. Techniques are available that help to differentiate between land predominantly covered with trees and agricultural land, if the areas covered with trees are at least 1 ha in size. The monochrome IRS PAN data, with a resolution of 5.8 m ´ 5.8 m, can be used to identify land cover on areas as small as 0.1 ha. If LISS III and PAN images are combined, then TOF resources can be appropriately stratified, on the basis of geometrical tree formations. Based on the spatial stratification, an appropriate sampling design can be determined for field surveys in each stratum.

The Tropical Agricultural Research and Higher Education Center (CATIE), in collaboration with several other agencies has been implementing the TROF Project in Costa Rica, Honduras and Guatemala¹ to evaluate various aspects of TOF assessment, using remotely sensed data. Similarly, the Forest Survey of India (FSI) has commenced a project, in collaboration with the National Remote Sensing Agency (NRSA), to develop a standardized methodology for TOF assessment using satellite images and geographic information systems.

Information on the area of permanent pastures is found in land-use statistics, however, data on tree components are scattered. Guevara, Laborde and Sánchez (1998) reported mean numbers of trees per hectare in 45 selected pastures in Los Tuxtlas, Mexico. Van Leeuwen and Hofstede (1995) counted trees on farms in the Atlantic Zone of Costa Rica. They found it impossible to assess the average number of dispersed trees on pasture land through interviews, as the farmers usually underestimated the number of trees they owned.

Existing data alone are unlikely to provide a complete and consistent picture of TOF for large areas, as they come from a variety of sources and studies, usually conducted in a limited number of smaller and not necessarily representative areas. Howev er, secondary data can be useful as ancillary information for the planning of inventories.

The choice of variables for which data are to be collected depends upon the objectives of the inventory. Land use, land cover and tenure may be important issues. The choice of appropriate sampling design is also crucial. It should be practical and ensure the desired results with the specified reliability, at a minimum cost and/or with the maximum reliability at a given cost, given the most effective use of the resources available.

Efficient inventory designs integrate the use of aerial photos or high-resolution satellite images with field plots. The role of high-resolution satellite images is to identify TOF and TOF configurations, support mapping and spatial analysis and assist in fieldwork planning. Detailed field sampling is required to provide information on species composition, tree dimensions, management practices and ownership. The spatial distribution (e.g. scattered individual trees, trees in lines, trees in blocks) necessitates an adjusted design of sample plots, combining fixed-area plots and line samples, as carried out in ecological surveys and forest inventories.

All agencies with an interest in TOF should be contacted before a TOF assessment is initiated to appraise the possibility of generating a comprehensive resource database (which includes TOF).