The EC-FAO Partnership Programme on Information and Analysis for Sustainable Forest Management: Linking National and International Efforts in South Asia and Southeast Asia is designed to enhance country capacities to collect and analyze relevant data, to disseminate up-to-date information on forestry and to make this information more readily available for strategic decision-making. Thirteen countries in South and Southeast Asia (Bangladesh, Bhutan, Cambodia, India, Indonesia, Lao P.D.R., Malaysia, Nepal, Pakistan, the Philippines, Sri Lanka, Thailand and Viet Nam) participate in the Programme. Operating under the guidance of the Asia-Pacific Forestry Commission (APFC) Working Group on Statistics and Information, the initiative is implemented by the Food and Agriculture Organization of the United Nations (FAO) in close partnership with experts from participating countries. It draws on experience gained from similar EC-FAO efforts in Africa, and the Caribbean and Latin America and is funded by the European Commission. Cover design: Tan Lay Cheng Photo credits: Thomas Enters For copies write to: Patrick B. Durst Senior Forestry Officer FAO Regional Office for Asia and the Pacific 39 Phra Atit Road Bangkok 10200 Thailand Printed and published in Bangkok, Thailand

© FAO 2003 ISBN 974-7946-42-4

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The word “countries” appearing in the text refers to countries, territories and areas without distinction. The designations “developed” and “developing” countries are intended for statistical convenience and do not necessarily express a judgement about the stage reached by a particular country or area in the development process. The opinions expressed in the articles by contributing authors are not necessarily those of FAO.

EUROPEAN COMMISSION DIRECTORATE-GENERAL DEVELOPMENT

Information and Analysis for Sustainable Forest Management: Linking National and International Efforts in

South and Southeast Asia

EC-FAO PARTNERSHIP PROGRAMME (2000–2002) Tropical Forestry Budget Line B7-6201/1B/98/0531

PROJECT GCP/RAS/173/EC

TRAINING MANUAL ON INVENTORY OF

TREES OUTSIDE FORESTS (TOF)

by

J. K. Rawat S. Dasgupta

Rajesh Kumar Anoop Kumar

K. V. S. Chauhan

Preface An accurate inventory of forest and tree resources is essential for formulating sound strategies for forestry development. Accurate, up-to-date information on forest cover and growing stock of forests and trees are basic ingredients for planning and policy development.

Tree resources outside continuous forest areas or trees outside forests (TOF) can cover considerable areas. They occur as small woodlots and block plantations, along linear features (e.g. roads, canals) or are found scattered on farmlands, homesteads, community lands and in urban areas. Traditionally, TOF were not inventoried and as a result, quantitative information about TOF is scarce. However, interest in TOF has increased worldwide. Besides providing support to subsistence economies, these trees form a substantial source of raw materials for forest industries. In its Forest Resource Assessment 2000, the Food and Agriculture Organization (FAO) of the United Nations concluded that information on TOF remains fragmented, diffuse, sometimes empirical and often sectoral.

The Forest Survey of India (FSI) has been conducting TOF assessments since the early 1990s. Due to its expertise in this field, FSI is in the position to take the lead in Asia offering training on inventory methods of TOF to professionals. It is in this context that FSI has prepared this �Training Manual on Inventory of TOF�.

The manual introduces several aspects of TOF inventories, to provide readers with a comprehensive overview of the tools needed to conduct an inventory. It provides insights into survey methods, describes procedures for data collection, data analysis, and formats for recording data during field surveys. Case studies from various countries are used to illustrate different inventory methods. The emphasis is on practical and conventional methodologies.

The authors wish to express their sincere gratitude to FAO for giving FSI the opportunity to prepare this training manual. Secretarial assistance was provided by Ms. Pratima Saini, Mr. I. H. Rizvi and Mr. Dinesh Gupta is also acknowledged. Lastly, the authors are grateful to the Ministry of Environment & Forests (Govt. of India), New Delhi for their support and encouragement.

J. K. Rawat S. Dasgupta

Rajesh Kumar Dehradun Anoop Kumar April 15, 2002 K. V. S. Chauhan

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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.

Definitions and terminology

A fundamental starting point for a comprehensive approach to conducting inventories of TOF are clear definitions and a brief overview of the extent and state of the resource. TOF comprise tree formations ranging from single discrete trees, to systematically managed trees on private and public lands. Inventory is the process of obtaining quantitative and qualitative information about a resource (Kleinn 2000). This manual has been prepared for inventories of large areas such as districts, provinces or a country.

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Trees outside forests – the resource to be inventoried

A clear definition of TOF is required in order to guarantee consistency and comparability among data sets, and to facilitate communication. A definition should provide information for local and/or national needs and should make it easier to fulfill international commitments.

Tree: A woody perennial with a single main stem, or in the case of coppice with several stems, having a more or less definite crown. Includes: bamboos, palms and other plants meeting the above criterion.

Forest: Land with tree crown cover (or equivalent stocking level) of more than 10 percent and area of more than 0.5 hectares (ha). The trees should be able to reach a minimum height of 5 m at maturity in situ. May consist of either closed forest formations where trees of various storeys and undergrowth cover a high proportion of the ground, or open forest formations with a continuous vegetation cover, in which tree crown cover exceeds 10 percent. Young natural stands and all plantations established for forestry purposes which have yet to reach a crown density of 10 percent or tree height of 5 m are included under forest, as are areas normally forming part of the forest area which are temporarily unstocked as a result of human intervention or natural causes but which are expected to revert to forest. Includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, cleared tracts, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes stands of trees established primarily for agricultural production, for example fruit tree plantations. It also excludes trees planted in agroforestry systems.

Other wooded land:

Land with either a crown cover (or equivalent stocking level) of 5 to 10 percent of trees, able to reach a height of 5 m at maturity in situ; or a crown cover (or equivalent stocking level) of more than 10 percent of trees not able to reach a height of 5 m at maturity in situ (e.g. dwarf or stunted trees); or with shrub or bush cover of more than 10 percent.

Trees outside forests:

Trees on land not defined as forest and other wooded land. Includes: trees on land that fulfils the requirements of forest and other wooded land except that the area is less than 0.5 ha; trees able to reach a height of at least 5 m at maturity in situ where the stocking level is below 5 percent; trees not able to reach a height of 5 m at maturity in situ where the stocking level is below 20 percent; scattered trees in permanent meadows and pastures; permanent tree crops such as fruit-trees and coconuts; trees in parks and gardens, around buildings and in lines along streets, roads, railways, rivers, streams and canals; trees in shelterbelts of less than 20 m width and 0.5 ha area.

Source: FAO (2001)

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.

Classification of TOF

A classification of TOF is required to better understand the structure and composition of the resource. It facilitates uniformity in resource evaluation and comparability of inventory results. A formal classification system is particularly necessary to enable presentation on maps, where not every single tree can be depicted, for large areas.

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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.

Examples of classifications of TOF

According to the land use • Trees in urban areas • Trees associated with permanent crops • Trees associated with annual crops • Trees associated with pastures • Trees along "line features" such as

property borders, roads, railways, canals, creeks

• Tree groups (that do not comply with the area requirements of the forest definition)

• Trees on not cultivated/not managed lands (part of savannah land, mountainous regions, peatlands)

According to geometrical formation Little or no direct inter-tree interactions: • Isolated scattered trees Zoned, exhibiting a more or less clear shape: • Trees in lines • Groups of trees

Source: adapted from Kleinn (2000)

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.

Justification for inventorying TOF

Any inventory of natural resources is costly and therefore requires an objective justification, which usually embraces the economic, social and ecological role of the resource. The intended information should satisfy user needs.

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,

4

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).

Brief review of previous inventories

TOF data at the regional and global levels are scarce, although a number of countries have initiated TOF inventories. Different methodologies have been adopted according to information needs and the availability of funds. Few studies have used similar methodologies. Many studies rely on existing estimates, drawn from other surveys and interviews, which may have been conducted for quite different reasons. The quantification of products is often based on different parameters (e.g. estimates of export and import volumes, marketed output, observed or potential productivity of forests or economic value). Thus, the reliability of such results is uncertain. 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).

TOF information based on remote sensing data

TOF information can be generated in three phases: land use classification and mapping; identification of tree-cover classes; and measurement of tree characteristics. Satellite images and aerial photos are suitable for the first two. High-resolution satellite images are likely to allow the identification of single trees (or crowns) and can be a data source for a large-area TOF inventory. 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

5

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. TOF data from other sources

TOF are partially covered by the data collection mechanisms of several sectors (e.g. agriculture, horticulture, industry, forestry, and conservation). This provides diverse data that can assist in producing large-area estimates of TOF.

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. However, secondary data can be useful as ancillary information for the planning of inventories.

Points to consider for large-area inventories

The basic prerequisite for a large-area TOF inventory is a land-use map, readily available or produced from satellite images. This base map is then used for the identification of areas where TOF are found and the type of TOF that are likely to be encountered during the inventory. On the basis of this map, the inventory can be planned.

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).

1 For more information see http://www.forst.uni-freiburg.de/TROF/dokumente/trof_english.pdf

6

7

Chapter II: Methodology for TOF inventories TOF inventory: step by step from design to implementation

In India, an inventory of TOF resources and their actual contribution to timber and fuelwood supplies was not perceived necessary prior to 1991. In some states, wood balance studies were undertaken as an important component of the social forestry projects during the 1980s. Some State Governments in India (e.g. Himachal Pradesh, Haryana, Gujarat, West Bengal, Orissa) conducted wood balance studies to estimate total wood consumption and production. At that time, data on TOF did not exist. The production of wood from TOF was either roughly estimated or outright ignored. The time available to collect data on TOF was inadequate and inventory methods were not yet developed.

The biannual forest cover inventory conducted by the FSI overlooks many TOF because of their scattered nature. The resolution of satellite images has been too low to sense the signatures of individual trees or groups of trees growing in isolation. FSI only started TOF inventories in 1991. Separate methods were developed for rural and urban TOF. The method for both rural and urban TOF will be discussed in detail below as an example of developing TOF inventories. These examples are intended to illustrate the steps necessary to design a TOF inventory method pertinent to various objectives and contexts. Steps include: clarification of TOF definitions; growing patterns or categories; sampling designs, and location selection for data collection during ground surveys.

Definition of TOF in rural areas In rural areas, TOF are found in all areas outside the legally classified forests and protected areas.

Category of trees TOF were classified into eight categories for the purpose of data collection, processing and analysis. These categories for classification were:

1. Farm forestry: trees along field bunds and in small patches of up to 0.1 ha. 2. Village woodlot: naturally growing or planted trees on community land. 3. Block plantation: compact plantations covering an area of more than 0.1 ha and not falling

in categories (i) and (ii). 4. Roadside plantation: trees planted along roads. 5. Pondside plantation: trees planted around waterbodies. 6. Railway side plantation: trees planted along railway lines. 7. Canal side plantation: trees planted along canals. 8. Others: trees not falling in any of the above categories.

Sample design In order to perform a TOF inventory, the area to be considered has to be determined. This may be a state or group of districts. If the area is fairly large, then considerable heterogeneity within a particular variable (i.e. growing stock) can be expected. Efforts were made in India to identify variable(s) on the basis of the study area, stratified into smaller homogeneous groups. TOF being planted along with agricultural crops are likely to be influenced by agro-ecological variables. In this case, the area was stratified according to agro-ecological zones (AEZ), which were demarcated by other governmental agencies. In India, districts are the basic planning and administrative units. These administrative units, which have significant influences on TOF and their resources, are considered for further stratification of these AEZs. The villages within these stratified areas are the sampling units selected, through stratified random sampling. The number

8

of sample villages surveyed in this area was decided by conducting a pilot test in 20-25 villages, randomly selected, with a 10 to 15 percent permissible error, at a 95 percent probability level.

Each of the selected villages was delineated, based on India�s revenue records. In each village, all trees of 10 cm DBH or greater were measured. The mean and variability of the growing stock/number of trees were calculated. The number of sample villages required for the detailed field inventory was determined by using the following formula

c.v. is coefficient of variation

s is standard deviation x is sample mean r is permissible error to be fixed by investigator

tα,k-1 is the value of t distribution at α level of probability and (k-1) degrees of freedom. k is the number of villages considered for pilot study.

And N = total no. of villages in the State/group of districts.

For large N, it will be equal to

After computing the appropriate sample size, the villages were assigned to the different AEZs proportionate to the rural TOF area of the same classification. In the case of a fraction, it was rounded off to the nearest integer. Selection of locations For this particular study, a list of villages for each district was made available from the District Census Studies. In other cases, this might also be an appropriate source. The sample villages in each district were then selected by using a random number table. Each village was allocated a number. Suppose there were 784 villages in the study, one would need to consider a 3-digit random number table. These tables were prepared so that each 3 digit number has an equal probability of being selected. One randomly chooses any page of the table and starts from any row or column to select a random number falling between 001 to 784. The corresponding villages are thus selected for enumeration. The selection continues from that point row/column wise to select villages, until the required number of villages is selected. A complete inventory is conducted of all the trees of 10 cm DBH and above, in the randomly selected villages.

This sampling scheme is based on the assumption that a diameter distribution can be generated with a population of only 1 000 trees. For instance, if the number of trees in a village was more than 2 000 stems, a measurement technique was applied to reduce the workload. All the characteristics (e.g. species name, DBH, category of trees) were only measured for a selected group of trees that were representative of the population.

21kα,

21kα,

rc.v.t

N11

rc.v.t

n

×+

×

=−

−

100xsc.v. where ×=

21k,

rv.ct

n

×= −α

9

Preliminary estimates on tree density were made with the help of key informants. According to these estimates, it was decided that:

(a) if the total number of trees was below 2 000, all trees were measured and recorded; (b) if the total number of trees was in the range of 2 000 to 5 000, then only alternate trees i.e.

1, 3, 5,� were measured and recorded; (c) if the total number of trees was more than 5 000 but less than 10 000, then every fourth

tree i.e. 1, 5, 9, 13,� was measured and recorded; and (d) if the total number of trees was more than 10 000, then every tenth tree i.e. 10, 20, 30,

40� was measured and recorded.

All the trees in the sample villages were counted but not all were measured. Formats for data collection and instructions for fieldwork will be discussed in Chapter III, and data processing and analysis in Chapter V.

Diameter class distributions in a village can be built up on the basis of the enumerated trees (sub-sample) and total number of trees in that village.

Inventory design and methodology for urban TOF In this particular survey, the TOF area was defined for urban centres. The classification of the urban TOF followed a similar approach to the one for rural TOF. In the Population Census of India, TOF in urban contexts were defined as follows:

(i) All places with trees designated within a Municipality, Corporation, and Cantonment Board or Notified Town Area or Village Committee Area.

(ii) All other places that satisfy the following criteria: (a) A minimum population of 5 000 individuals residing in the immediate vicinity; (b) At least 75 percent of male working population engaged in non-agricultural activities;

and (c) A population density of at least 400 persons per km2 (or 1 000 per square mile).

(iii) Places that have distinct urban characteristics (e.g. major development projects, areas of intensive industrial development, railways, and important tourist centres), although such places may or may not strictly satisfy the criteria of (a) & (b) under (ii).

In addition to the above, the areas of traditional and newly classified reserves and protected areas were excluded from this survey.

The Census classification of towns, based on population is given below: Class I: Population of 100 000 and above. Class II: Population between 50 000 to 99 999. Class III: Population between 20 000 to 49 999. Class IV: Population between 10 000 to 19 999. Class V: Population between 5 000 to 9 999. Class VI: Population less than 5 000.

Category of trees

The trees in the TOF area were classified into 9 categories for the purpose of data recording, processing and analysis. These categories were as follows:

1. Farm forestry: trees along farms and in small patches up to 0.1 ha in area. 2. Woodlot: naturally growing and planted trees on community land (e.g. parks, gardens,

institutional plantation). 3. Block plantation: patches covering areas of more than 0.1 ha and not considered in the

above categories. 4. Roadside windbreaks: trees planted along roadsides.

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5. Pond side plantation: trees planted in and around water bodies. 6. Railway windbreaks: trees planted along railway lines. 7. Canal side plantations: trees planted along canals. 8. Homestead: trees appearing in the house area and not covered in the first three categories

above. 9. Others: trees not falling in any of the above categories.

Sampling design

The sampling frame for urban TOF areas was prepared by the National Sample Survey Organization (NSSO). This organization conducted a survey by the name of Urban Frame Survey (UFS). The UFS divided urban centres of a district into blocks, which are formed on the basis of 600 to 800 individuals or 120 to 160 households. These blocks covered the whole area within the geographical jurisdiction of a town (including vacant lands), with well-defined natural boundaries. Each district was divided into the six categories or town strata (as defined in TOF urban (iii) above). UFS Blocks were the sampling units defined by the NSSO. The sampling technique was based on stratified random sampling. The randomly selected blocks in each district were used for conducting a complete enumeration of the trees of 10-cm DBH and above. The number of sample blocks in the district was determined through a pilot test in randomly selected blocks with a 10 to 15 percent permissible error, at a 95 percent probability level. As with TOF in rural areas, formats for fieldwork will be discussed in Chapter III and data processing and analysis in Chapter V.

Previous TOF inventories

A number of local, regional and global inventories have been conducted, many of which differ in their approaches and purpose. Several studies have used conventional forest inventory methods while others have drawn upon estimates from small surveys and interviews. The quantification of products can be based on many different parameters, including estimates of global output, market outputs, observed/potential productivity levels or economic valuation of specific products from TOF resources. Despite these differences, the case studies show the type of information that can be obtained and the different types of techniques that can be utilized for a TOF inventory.

Haryana State, India Haryana covers a geographic area of 4.42 million ha. The TOF rural area comprises 4.26 million ha. In 1991, the population of the state was 16.47 million, of which 75.4 percent was rural. The economy of the state is predominantly agricultural. The livestock population stood at 9.14 million in 1992 (Livestock Census 1992). As there is not sufficient land for fodder production or for open grazing, livestock is putting tremendous pressure on the forest and plantation resources of Haryana.

Haryana is considered deficient in forest resources. Out of the total area, 82.8 percent (3.51 million ha) is under cultivation and 3.85 percent (0.17 million ha.) is designated as natural forest. Dominant species in these forests include Acacia spp., Anogeissus spp., Prosopis spp., Dalbergia sissoo, Ficus spp., Azadirachta indica, Shorea robusta and Bombax ceiba.

In 1991, Haryana had a total of 6 988 villages. Out of these, 219 villages were randomly selected and surveyed as per the method discussed in Chapter III. The data collected from these villages was used to estimate tree density and volume for the State of Haryana. The total volume was estimated at 10.34 million m3 and the total number of trees at 55.14 million trees.

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West Bengal State, India The geographical area of the State is 8.87 million ha, of which 7.68 million ha are outside forest areas. The forest cover is 9.42 percent and the designated forested area covers 13.38 percent of the total geographic area. West Bengal�s physiography has two natural divisions: the Himalayan North and the fertile alluvial Gangetic plain.

In West Bengal, 25 villages were randomly selected for the pilot survey. All the trees with a DBH of 10 cm and above were enumerated in the selected villages. However, in South Bengal, trees down to 5-cm diameter were also recorded. In the pilot survey, only the total number of trees were estimated and not their volume. The total number of trees in the State of West Bengal was approximately 196 million or 25.4 trees/ha.

Karnataka State, India The geographic area of Karnataka is 19.18 million ha, of which 18.75 million ha are considered to be outside forest areas. In 1991, the population of the State was 44.98 million with a rural population of 31.07 million. In 1992, the livestock population was 29.56 million and the designated forestland was 3.87 million ha constituting 20.19 percent of the total geographical area. However, the forest cover of the State, as assessed by the FSI was actually only 3.24 million ha. Of the forest cover in this survey, 2.48 million ha were characterized as �dense forest� and the remaining 0.76 million ha as �open�.

In Karnataka, 10 villages were randomly selected for the pilot survey, with one village in each district, so that all districts of the State were represented. Only the total number of trees was estimated and not the volume. The estimated total number of trees in rural areas for the whole State was 296 million trees or 15.8 trees per ha.

Kenya The Kenya�s Forestry Master Plan (KFMP) was launched in 1991. A major objective of the KFMP was to provide recommendations for the formulation of appropriate forest policies. For the preparation of the KFMP a systematic survey of the potential woody biomass contribution from TOF resources was conducted at the national level. Variables that were estimated included the standing volume, species distribution, and potential future use of the woody biomass outside actual forests.

The survey was based on a two-stage sampling design. The first stage consisted of low-altitude air photos. The second stage consisted of field measurements in a sub-sample of the photos. Interpretation of aerial photos provided a subjective classification of the woody biomass into crude cover classes. Using detailed and objective field measurements in a sub-sample of the photos, it was possible to convert the interpretations into objective volume figures, species distribution, etc. This is an example of a classical design for forest inventory that is also suitable for a TOF inventory.

In the second stage, aerial photos were taken in June 1991 with a 24 × 36 mm camera using colour film. Each photo covered approximately 700 × 500 m on the ground. The photos were taken along flight lines, 5 km apart, with a 2.5-km distance between photos along the lines, resulting in a total area coverage of 3 percent. A total of 7 587 photos, systematically distributed over the entire survey area were taken at a scale of 1:1 000, allowing the identification of single trees down to a crown diameter of about 2 m. Two hundred grid points were then placed on each photo and visually classified into 12 classes. This resulted in 1.5 million classification points, with a sub-sample of 28 000 recorded trees. Between December 1991 and June 1992, 150 of the photographed areas were ground-truthed, and interpretation points and single trees were located and measured. At each point, a sample plot was laid out and all trees were classified according to species, volume, and potential use of the wood. The same parameters were recorded for single

12

standing trees. The results of this survey were based on field measurements of 1 183 points and 878 single trees.

The volume per unit area (m3/ha), distribution of tree species and potential uses of the biomass were calculated for each sample plot. The results were then grouped according to the defined classes. Since the photos were located on a UTM-grid, it was possible to use regression analysis to test the field data for possible relationships with agro-climatic zones. It was assumed that sample plots within the same class could differ, primarily in stocking, among zones. Finally, average volumes and standard errors were calculated for each class. A similar analysis was applied on individual trees using crown diameter and shape, to estimate the volume of the tree (resulting in a volume table for biomass). The results of these calculations provided information on the composition of the woody biomass in farmlands, based exclusively on the field measurements, and class-wise volume estimates that can be applied on all interpreted data from the 7 587 photos. This survey thus converted covered areas into measurements of standing volume in a very cost-effective way.

A change study was conducted for the entire survey area, using a set of 258 systematically sampled photos of the same type as previously described. The two sets of photos from 1986 and 1992 covered exactly the same area. Changes in biomass cover, between 1986 and 1992, were analysed by dividing each photo into 48 squares. A total of 12 384 squares were interpreted. The significance of the visual changes on the photos was also ground-truthed at a district level.

No significant relationships were found between the stocking volume and agro-climatic zone for any class. Therefore, the average figure for each class was assumed to be constant over the entire survey area. Species distribution and potential biomass use indicated that eucalypts were the most important tree species and that 30 percent of the volume was classified for timber and veneer uses.

The average estimated standing volume of woody biomass outside forests was 16.4 m3/ha, of which 25 percent can be referred to as planted and the remainder as natural woodland (including riparian areas). When calculated by district, the volume ranged from 4.7 to 36.2 m3/ha. This seems to indicate that culture, education and habits (which vary among districts) are significant factors in determining the distribution of TOF. There was a strong correlation (r2 = 0.64) between population density and standing volume of TOF.

The change study revealed that the standing volume of planted trees had increased on average by about 4.7 percent annually, which is higher than population growth in Kenya. No district showed a significant decrease in volume. The amount of natural vegetation appeared to be approximately constant. The estimated standing volume of planted trees was over 40 million m3 on farmlands, which was higher than the industrial plantations under government administration.

Sri Lanka The total population of Sri Lanka in 1999 was 18.90 million with a population density of 290 persons per km2. Administratively, the country is divided into nine provinces and 25 districts. The land area of the Sri Lanka is 65 156 km2. In 1999, the country�s dense natural forest cover (canopy density of > 70 percent) represented 23.9 percent of the total land area, while the total forest cover including sparse forest (canopy density of < 70 percent) was 20 466 km2, representing about 31 percent of the land area. Shifting cultivation in the early 1990s affected around 1.2 million ha or about 18 percent of the total land area. In 1995, TOF provided 8.83 million tonnes of biofuel accounting for 90 percent of total production (9.87 million tonnes). TOF covered about 30 percent of the country�s land area and contribute about 70 percent of the total supply of industrial and construction wood (FSMP 1995).

There are different categories of TOF resources in Sri Lanka: 1) home gardens (cultivated areas around a house); 2) rubber plantations, coconut plantations, shade trees in tea-coffee and cocoa plantations, windbreaks and shelterbelts, roadside plantings, trees on farmlands (Palmyrah palm, cashew-nut and other perennial orchards); and 3) plantations in and around urban areas.

13

In response to a request from the Forest Sector Master Plan (FSMP), the Forest Management Plan Project (FORMP) used 1992 satellite images to update the nationwide forest-cover maps. In this study, tree cover included forests, forest plantations, and TOF resources with ample crown coverage (i.e. land under home gardens, rubber, and coconut). It excluded tea, other crops, and other land uses with only sparse tree cover. The resulting statistics on forests and trees cover:

! forests and forest plantations, based on FSMP 1992 satellite images; and ! home gardens, rubber, and coconut, based on 1992 satellite images.

The statistics on home gardens were estimated using the difference method. With this method, the residual TOF resources were obtained by subtracting the areas for rubber and coconut2, from the total forest area recorded.

A recent study in Sri Lanka entitled �The effect of the wood products industry on natural forests� used a method that included the collection of TOF data through participatory rural appraisals. The study, which included a survey of home garden owners and timber industry stakeholders, was conducted in seven districts with different climatic, physical and socio-economic conditions. Data collected included species and number of trees in home gardens, their age structure, the rate of cutting and the proposed planting rate to estimate the quantity of timber extracted and to assess their importance as a source of timber.

The results of this study showed that the average number of trees in a home garden is around 180 trees per ha. Furthermore, the largest number of trees (per ha) in home gardens were noted in certain districts, especially those in the wet zone, due to the large areas covered by rubber and coconut. In the dry zone, tree densities were lower (71 trees/ha) and most trees had not been planted but regenerated naturally. It was also observed that there is a trend towards planted trees for timber production. This was also confirmed recently by Ariyadasa (2002)

The FSMP (1995) estimated that home gardens covered an area of about 858 500 ha in 1992, compared to about 781 000 ha in 1983. The area under home gardens has been increasing by about 1 percent (8 000 ha) annually since the 1980s, while the rubber plantation decreased in size at an annual rate of about 0.5 percent between 1956 to 1982.

The FSMP (1995) also estimated the number of saw logs and poles that can be produced from the TOF resources based on a model forecasting the demand and supply of different wood products. The general form of the basic model was estimated in the following log-log form:

InC = a + b lnX +e

where InC is the natural log of consumption per capita, a is a constant, X is the real GDP per capita, b represents the income elasticity of demand and e is the error term. GDP and population have been taken as the main demand shifters.

In 1992, home gardens occupied 858 000 ha, rubber plantations occupied 197 000 million ha, coconut plantations occupied 300 000 million ha and tea over 189 000 million ha. The wood production from TOF resources was projected to increase from 1.86 million m3 in 1995 to 2.18 million m3 in 2020.

Bangladesh Bangladesh has a population of about 130 million, which was growing at an annual rate of 1.7 percent (1995-2000). Almost 80 percent of the people live in the rural areas. The total land area of the country is about 14.4 million ha, of which 9.25 million ha is cultivable, 2.59 million ha uncultivable and 2.56 million ha are recorded forests. Of the total recorded forests, 2.22 million ha are State forests and 0.34 million ha are private forests (village forests 0.27 million ha and tea/rubber plantations 0.07 million ha) (FMP, 1992). However, only 0.93 million ha (6.5 percent)

2 These figures were derived from the 1990 data of the Rubber Control Department and the 1992 data of the Coconut Development Authority and the Survey Department, respectively.

14

is under forest or tree cover, which is about 40 percent of the government, controlled forests. The remaining 60 percent include degraded grassland, scrub and encroached lands.

Of the total wood produced in Bangladesh, 80 percent is used as fuelwood and most of the remainder as sawntimber. It is estimated that total annual wood production is 9.5 million m3 (FMP 1992).

In Bangladesh, homestead forests are the most important source of wood, bamboo and other non-wood forest products. Because of its importance and prevalence, the term homestead forest is now used synonymously with village forests and rural forests. The terms village forests and rural forests are used in a wider sense to refer to an aggregation of scattered trees in villages (e.g. trees on homesteads, road sides, common access lands, community lands, premises of educational institutions and places of worship). TOF resources in Bangladesh thus refer to:

1. trees grown in multi-tiered gardens of a homestead; 2. woodlots or block plantations; 3. agro-forestry plantations; and 4. strip plantations along road and highways, railways, district council and feeder roads.

Prior to 1980/81, little was known about TOF resources (and in particular homestead forests) in Bangladesh. Since then, many local-level studies and a few national studies have been conducted to estimate the area of village and homestead forests, their growing stock of wood and bamboo, and average annual production of materials. The first three homestead/village forest national surveys conducted in 1981 and 1992 used similar methodologies.

In the 1981 study for bamboo, a sample of 6 000 households was randomly selected from among the 267 selected villages. In the other two studies in 1981 and 1992, 6 675 households were selected. The households in the selected villages constituted the final stage of sampling. They were selected randomly from the Union Parishad list in the Holding Tax Register. The selected households were classified into four landholding classes as given below:

Class � I Less than 0.2 ha Class � II 0.2 to 1.0 ha Class � III 1.0 to 3.0 ha Class � IV More than 3.0 ha

Almost all households in Bangladesh, except in the coastal and low-lying areas, cultivate bamboo. The estimated quantity of bamboo use was about 10.2 culms/capita/annum.

The latest national survey was conducted for the preparation of the Forestry Master Plan (FMP 1992). Total growing stock of homestead and village forests was estimated to be 55 million m3. About 76 percent of the stems were found on homesteads occupied by landless, marginal and small landholders. Only 24 percent of the stems were found on areas owned by medium and large landholders.

The fuelwood component of the growing stock was estimated to be about 40 million m3 (73 percent of the total growing stock). Sawlogs made up 15 million m3 (27 percent). The per capita consumption of bamboo was estimated at 6 culms/year against the availability of 4.3 culms for mature and 7 culms for immature bamboo, per capita per year.

15

CHAPTER III: Format for collection of field data In the first step, all variables deemed necessary for meeting the objectives of the inventory have to be identified. Then, the data collection formats should be designed for the selected variables. In this chapter a format is discussed as an example to see how the design and application of a format can help to collect the desired data on TOF resources, for further analysis.

TOF data collection in rural India (an example)

In this particular example, the organization of the fieldwork was based on the Instructions for Field Inventory used in India. Field teams were formed consisting of five members, with one crew leader. The crew leader was provided with the list of sample villages to be inventoried along with a set of 1:50 000 maps indicating the location of villages. After reaching the sample village, the team determined the village boundary. For this purpose, the maps of the revenue department were referred to, and the help of village authorities sought. It was necessary to select a reference (starting) point, such as a prominent and permanent feature (e.g. temple, community hall). The location of reference point and its description was recorded in the �Village Description Form� (see Annex I, TOFR-1, points 8 and 9) for further ground-truthing and cross-checking of data collection methods, at a later time.

After determining the reference point, the entire village was divided into smaller units with the help of a compass. Each cardinal section (i.e. wedge of the compass) was selected so that it could be completed in one working day. Data collection commenced from due north and proceeded in a clockwise direction. The enumerated trees were clearly marked with chalk along the boundary of the wedge to avoid duplication and omission of trees and to facilitate the resumption of data collection on the next day. DBH was measured for selected trees, with the help of a calliper tool. Measurements and code numbers were recorded on the field forms by the crew leader. Instructions were read carefully before filling in field forms, so that all field teams understood the measures and codes.

Data collection formats for rural TOF Three forms were used for data collection of rural TOF resources (see also Annex I):

(a) TOFR -1 Village Description Form (VDF) This form provides information on the reference point, the number of cardinal wedges, the size of each wedge and the total number of trees enumerated in each wedge.

(b) TOFR -2 Village Tree Enumeration Form (VTEF) This form provides information on all trees enumerated, their diameter, species and category.

(c) TOFR -3 District Tree Form (DTF) This form provides detailed information on the sampled villages in the district, referring to the geographical area and number of trees falling in different categories.

Organization of field work for inventorying urban TOF

Field teams were formed similarly to the rural TOF teams. The crew leader was provided with a list of the sample blocks to be inventoried, along with UFS maps indicating the particulars of the block. Instead of working on cardinal wedges of a compass, daily sampling units were defined as city blocks, starting in the northwest corner of the block. As a reference point a prominent and permanent feature of the block was chosen. The location of reference point and its description were recorded in the �Town Description Form� (see Annex II). Enumeration of trees commenced from the northwest corner of the block and proceeded in clockwise direction. As with the rural data collection, the enumerated trees were marked to avoid duplication or omission.

16

All the trees in the selected blocks were enumerated. If the house owner of a particular house was not available at the time of survey, then the field team collected data by interviewing neighbours and/or key informants.

Data collection formats for urban TOF Three forms were used for data collection of urban TOF resources (see also Annex II):

(a) TOFU -1 Town Description Form (TDF) This form provides information on the reference point.

(b) TOFU -2 Town Tree Enumeration Form (TTEF) This form provides information on the trees enumerated, including their diameter and species.

(c) TOFU -3 District Tree Form (DTF) This form provides detailed information on the sampled blocks in the district, including the geographical area and number of trees in the different categories.

17

CHAPTER IV: Volume tables and equations The main objective of a TOF inventory is to estimate the growing stock, which is essential for sound management, planning and policy formulation. For management purposes, the user is often concerned with determining the volume of a large number of trees of distinct dimensions. To do this requires volume tables. Volume tables are based on diameter and/or height and/or tree form. They are often derived via volume equations that are statistically sound and based of regression analysis.

Volume tables

A volume table of a particular species is defined as a table showing the average volume of trees, logs or sawntimber for one or more given characteristics such as DBH alone or DBH and height or DBH, height and form or taper. The main objective of volume tables is to facilitate estimating the average volume of standing trees for which the diameter and height are known. The volume is based on the actual volume measurements of some 40 to 50 felled or standing trees of all diameter classes. It is based on the assumption that individuals of the same species, with the same DBH, or DBH and height have, on average, the same volume (given identical growing conditions). Thus, volume tables can be classified into three categories according to the number of independent variables (one to three) i.e. DBH, height and/or taper.

Volume equations

The development of a volume table requires volume equations for the species in question. There are three types of volume equations based on the number of variables and objectives. Each type is formulated by means of regression analysis:

(i) Local volume equation: Local volume equations are applicable for a small forest or land area and are based on only one variable, i.e. DBH. The basic assumption is that trees of a given species, at a given location, with the same DBH, will have the same height and form. This assumption is only valid as long as site conditions are homogenous.

(ii) Regional volume equation: This type of equation is normally based on two variables (e.g. DBH and height) and covers a larger geographical area. Regional volume equations are standard volume equations with limited application. Care needs to be taken that the trees measured, for the formulation of this equation, are truly representative of the variation encountered in the region.

(iii) General or standard volume equation: This is an even broader equation and covers the full distribution of the species. It is normally based on two variables such as DBH and height.

A local volume equation can be easily prepared from a standard or regional volume equation by analysing the DBH/height relationship of the species for the given location.

Preparation of volume tables

There are two methods available to generate volume tables namely, the destructive and the non-destructive method.

18

Destructive method In this method, 40-50 individuals of a particular species, representing all diameter classes of interest are selected randomly and felled. Each tree is cut into appropriate lengths of logs, generally between 2-3 m. The volume of each log is calculated individually, using suitable formulae, generally Huber�s formula for parabolides, cylindrical, conical or Newton�s formula for neiloidic form. The volume of each individual log is added to obtain the total volume of the tree.

Non-destructive method This is similar to the destructive method but the trees are not felled. Diameters are measured at different heights by climbing the trees. Tree height is estimated with the help of e.g. an altimeter, a clinometer, a cruiser stick or a relaskop. The volume is then calculated using the same formulae as above. Volume tables can be prepared for particular species, on the basis of these calculations.

Data collection for preparation of volume tables

The trees to be measured are selected randomly. The DBH is measured twice to the nearest millimeter, perpendicular to each other, over bark. Both the height and the diameter are recorded in a table. After the measurements have been taken, the trees are felled. The first log is marked at 2.74 m (with the DBH in the centre) and the DBH taken as the girth. The rest of the bole is divided into sections of 3 m length and mid girth of each section is recorded at the middle of the log. The last section is allowed to vary between 2 to 4 meters. The lowest diameter limit fixed for the measurement is generally 5 cm. Similar measurements are carried out for all the branches.

Ground level

Clear bole and height of the tree and DBH measurement at 1.37 m from ground

Total height

Clear bole DBH

1.37 m

19

Volume computation

The volume of individual logs is calculated using Huber�s formula, which considers the cross sectional areas of the log at mid-point as a circle. Multiplied with the length of the log it provides the volume.

V = sm x l V = volume (m3) sm = the sectional area at the middle (m2) l = the length of the log or height of the log (m)

Earlier methods of developing volume tables involved large-scale data collection for different diameter and height classes. However, the present trend is to use multiple regression methods in which basal area, girth or DBH along with height or a form factor is taken into consideration. Although these methods have certain inherent limitations, they provide a high degree of correlation and statistically acceptable relationships.

General volume equations General volume equations (GVEs), i.e. regression functions in volume, diameter and height are selected for each species. The GVEs are obtained from randomly selected tree data by applying multiple regression methods. The following regression equations are generally used:

(i) V = a + bD2H (ii) V = a + bD + cD2H (iii) V = a + bD2 + c(D2H) 2 (iv) V = a + bD + cD2H + d(D2H) 2 (v) V = a + bD + cH + dD2H (vi) V = a + bD + cD2+ dD2H (vii) loge V = a + b loge D + c loge H (viii) V/D2H = a + bD2H (ix) V/D2H = a + bD2H + c/D2H

Where, V = volume under bark (m3) D = diameter at breast height (1.37 m) over bark (m)

(Unless otherwise specified) H = height of tree (m) a is the intercept and b, c & d are regression coefficients

The best fit regression equation is used to estimate the volume of trees. Local volume equations Local volume equations (LVEs) are developed with only one independent variable, i.e. diameter (D). The following types of regression equations are used to obtain LVEs:

(i) V = a + bD2 (ii) V = a + bD + cD2 (iii) V = a + bD + cD2 + dD3 (iv) V = a + b√D + c D2 (v) √V = a + bD (vi) √V = a + bD + c√D (vii) V/D2 = a + b/D2 (viii) V/D2 = a + b/D + c/D2 (ix) V/D2 = a + b/D2 + c/D + dD (x) loge V = a + loge D

20

Where, V = volume under bark (m3) D = diameter at breast height (1.37 m) over bark (m)

(Unless otherwise specified) a is the intercept and b, c & d are regression coefficients.

The best fit regression equation is used to estimate the volume of trees.

On the basis of developed volume equations volume tables can be prepared. An example of a volume table for some species is given below:

Local volume table for Jalgaon district SL. NO. Name of species Diameter class (in cm)

10-20 20-30 30-40 40-50 50-60 60-70 70 + 1 Acacia arabica 0.110 0.410 0.900 1.590 2.450 3.460 4.620 2 Acacia catechu 0.084 0.319 0.671 1.141 1.729 2.433 3.256 3 Acacia leucophloea 0.065 0.278 0.639 1.149 1.807 2.614 3.569 4 Albizzia spp. 0.100 0.540 0.980 1.420 2.380 3.530 5.060 5 Azadirachta indica 0.067 0.219 0.500 0.910 1.448 2.116 2.913 6 Dalbergia latifolia 0.088 0.350 0.784 1.392 2.173 3.128 4.255 7 Dalbergia sissoo 0.088 0.350 0.784 1.392 2.173 3.128 4.255 8 Mangifera indica 0.100 0.530 0.970 1.590 2.550 3.450 5.060 9 Melia azadirachta 0.085 0.298 0.618 1.044 1.577 2.217 2.963 10 Mitragyna parviflora 0.065 0.294 0.725 1.357 2.192 3.228 4.465 11 Pongamia pinnata 0.065 0.278 0.639 1.149 1.807 2.614 3.569 12 Syzygium cumini 0.075 0.268 0.585 1.027 1.593 2.284 3.099 13 Tamarindus indica 0.067 0.219 0.500 0.910 1.448 2.116 2.913 14 Leucanea leucocephala 0.065 0.278 0.639 1.149 1.807 2.614 3.569 15 Other spp. 0.065 0.278 0.639 1.149 1.807 2.614 3.569

Note: 1. Volume figures are in m3. 2. Volume figures are estimated using the mid-point of the diameter classes.

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CHAPTER V: Data processing and analysis

Data collection methods and estimation procedures depend on the sampling design used and the purpose of the survey. Data processing and analysis are also linked to the adopted survey design. In the survey example used earlier in this manual, stratified random sampling was used for estimating the number of stems and volume for TOF in rural areas.

The sampling design adopted considered the village as the survey unit and the agro-ecological zone was treated as a stratum of the population (i.e. the state). In this survey, it was necessary to generate an estimate for each stratum (i.e. agro-ecological zone and then from stratum to state). Since the sampling units (i.e. villages) were of different sizes, their TOF areas varied. The TOF area of each stratum was thus known and therefore it was possible to use a ratio estimation procedure for the parameters studied for each stratum and state.

For urban areas, the survey design used stratified random sampling and town areas were divided into six strata, based on the population, as used by the census authorities and the sample unit adopted by Urban Frame Survey (UFS) as blocks. These blocks were randomly selected in each strata and district, as the population under survey.

Estimation procedure for rural TOF

To estimate number of trees and growing stock, the ratio estimate is applied using the following formula.

Let n = number of sample villages in the stratum/state N = total number of villages in the stratum/state

xi = area of ith sample village yi = volume/no. of trees for the ith village

∑=

=n

1i

i

nx x = Average area of village in the sample

∑=

=n

1i

i

ny y = Average volume/no. of trees in the sample

∑=

=N

1i

i

Nx

X = Average area of village in the population (stratum/state)

∑=

=N

1i

i

Ny

Y = Average volume/no. of trees in the population (stratum/state)

Then the mean volume/no. of trees per unit area for the population (stratum/state) say, R is given by

XY R =

∑=

=N

1iix A = Total area of all villages in the population (stratum/state)

22

The estimate of R is the sample ratio.

xy

X

Y R� n

1ii

n

1ii

==

∑

∑

=

=

The estimate of total volume/no. of trees say, T� in the population is given by the formula

xyA R�A

xyAx T� ×=×==

Estimated variance of R� is given by the formula

( )

+−

−×

−= ∑∑∑

===

n

1i

2i

2n

1iii

n

1i

2i2 xR�xyR�2y

1n1

x1)n(Nn-N R�V�

When N is large, then

( )

+−

−= ∑∑∑

===

n

1i

2i

2n

1iii

n

1i

2i2 xR�xyR�2y

x1)n(n1 R�V�

Estimated variance of T� is given by

( ) ( )R�V�A T�V� 2 ×=

S.E. of ( )R�V� R� = S.E.% ( ) 100R�

S.E. R� ×=

S.E. of ( )T�V� T� = S.E.% ( ) 100T�

S.E. T� ×=

Data processing

Data processing consists of two steps namely, data preparation and electronic processing.

Data preparation The inventory data are collected by the field crew in the requisite forms (see Chapter III). The forms are precoded so that the data can be transferred directly to the data files.

The forms are prepared for processing as follows: 1. The field forms are received by the Data Processing Unit (DPU) and proper documentation

is done for future reference. 2. Data coding is done in the field forms, if omitted for some reason earlier. 3. The field forms are checked with the listing supplied by the Field Operation Unit (FOU) for

inconsistencies and coding mistakes. 4. Abstract of field forms of TOF area is mentioned in the register at DPU under the headings

of: district geographical area, TOF area, name of village, Sl. No. of sample village, geographical area of sample village, mapsheet No., total No. of trees enumerated etc.

5. Finally, data are corrected for input into the computer.

23

Electronic data processing Data processing involves the following steps:

1. Data entry: after consistency checking, data are entered in FoxPro/Excel/Access for each tree in the data sheet, with its species, diameter and category code. Data are entered village by village, with identification particulars.

2. Listings are taken out for the data entered in the computer and checked to ensure complete loading and proper sequence. Necessary corrections are incorporated in the data entered.

3. Species and diameter class-wise volume tables are prepared (see Chapter IV). In the case used as an example in this manual, the local volume equations were developed by FSI. The volume table thus prepared is used to calculate the volume of each tree.

4. Compilation of data: data entry of all the villages, which has already been done for a particular stratum are grouped in one file for further processing.

5. Classification of species: the species occurring more than 1 percent, which are required for calculating the number of stems and growing stock, are classified according to their category and diameter class. Species with lower frequencies are grouped under other species and classified in the same manner. This classification is done with the help of a routine in FoxPro/Excel/Access.

6. Category-wise tables: after selecting the main species including other species, tables for enumerated trees are prepared in Excel and filled in the following sample format for further processing.

Category: Distribution of enumerated trees by species and diameter class

Diameter class Sl. No.

Species 10-20 20-30 30-40 40-50 50-60 60-70 70+

Total

Total

7. Calculation of growth stock tables: The growth stock tables are calculated per species and diameter class, for each category, by multiplying the respective cells of the volume table, (see step 3 and category table above).

The number of trees and growing stock are adopted for all the villages of the stratum, i.e. agro-ecological zone. The following formula of the ratio estimator is used to estimate the number of trees and growing stock for the stratum:

Estimated number of trees =

Enumerated trees Average area Total TOF = of villages × area of the No. of villages surveyed surveyed agro-ecological zone Where,

Area of villages surveyed Average area of villages = Number of villages TOF area of district is calculated according to formula: TOF area = Total geographical area of district � forest area � urban area Total TOF(R) Area for stratum = total of all such districts falling in the stratum

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After that, the estimate for the population is generated with the help of stratum estimates. The formats of reporting tables are given below:

Table 1. Distribution of estimated total no. of stems by species and diameter class Diameter class Sl.

No. Species

10-20 20-30 30-40 40-50 50-60 60-70 70+ Total % of

trees Stem/ha

Total % of trees Stem/ha

Table 2. Distribution of estimated volume by species and diameter class

Diameter class Sl. No.

Species 10-20 20-30 30-40 40-50 50-60 60-70 70+

Total % of vol. Vol/ha

Total % of vol. Vol/ha

Table 3. Distribution of estimated total no. of stems by category and diameter class

Diameter class Sl. No.

Category 10-20 20-30 30-40 40-50 50-60 60-70 70+

Total % of stems

Stem/ha

1 2 3 4 5 6 7 8 Total percent of

stems

Stem/ha

Table 4. Distribution of estimated volume by category and diameter class Diameter class Sl.

No. Category

10-20 20-30 30-40 40-50 50-60 60-70 70+ Total % of

vol. Vol./ha

1 2 3 4 5 6 7 8 Total % of vol. Vol./ha

25

Table 5. Distribution of estimated total no. of stems by species and category Species Category Sl.

No. 1 2 3 4 5 6 7 8 Total % of

trees Stem/ha

Total % of trees Stem/ha

Table 6. Distribution of estimated volume by species and category

Species Category Sl. No. 1 2 3 4 5 6 7 8

Total % of vol. Vol/ha

Total % of vol. Vol/ha

In all the tables above, the estimated number of trees and volume, the values in each diameter class or category is obtained by adding corresponding values obtained during processing. After this, the percentage and per hectare figures are calculated. Similar calculations are done in case of urban TOF taking class of town as strata, block as sample unit and district as population. Using the above mentioned formula in estimation procedure, the standard error is calculated for the estimates of number of stems and volume of TOF area of the state.

Data processing tools

In a typical census, a large amount of data has to be entered, but the data structure and statistical tabulations are relatively simple, comprising first-level tabulations and second- and third-level cross-tabulations. In contrast, sample surveys require far less data input (10 percent or, more generally, 5 percent or less), but the statistical operations, such as the estimation of population totals and their variance are complex. Computers have now become an indispensable tool for data processing in the public and private sector. Almost invariably, survey data are now processed on personal computers (PC). For example, the data of 48 Demographic and Health Surveys, conducted in different countries of the world during 1985 and 1992, were all processed on PCs, using an integrated software package especially developed for these surveys. The first sets of tabulations was completed within two weeks of the receipt of data. Large-scale surveys are conducted by government organizations, industry, political organizations and large private-sector enterprises. They generally have the same objective in selecting sampling designs, which is to minimize the variance of survey estimates under the constraints of time and cost. As a consequence, most large-scale surveys are characterized by sampling designs with varying degrees of complexity. They often include features like stratification, disproportionate sampling, multiple stages of sample selection, clustering, PROC SURVEYSELECT (PPS) sampling and similar procedures. They often use different estimators like ratio and regression estimators. Most standard statistical software packages such as SAS, SPSS, S-Plus and SysStat assume that the data were obtained from a simple random sample in which the observations are independent and identically distributed and selected with equal probability. When the data have been collected using a complex sampling design, variance estimates of survey statistics desired

26

under simple random sampling assumptions generally underestimate the true variance, which results in artificially lower confidence intervals. Therefore, when using a standard statistical package, care should be taken and limitations of the software be covered by utilizing appropriate weights, as per sampling design using different commands. Generally, integrated software packages are developed for data processing of these large surveys and standard statistical packages are used for data analysis purposes.

27

ANNEX I: Examples of field forms and instructions for their completion for rural TOF

TOFR-1 Village Description Form

1. State and code

2. District and code

3. Mapsheet no.

4. Name of the village

5. Area of the village

6. Date of commencement of survey

7. Date of completion of survey

8. Conspicuous feature selected as the centre for starting the survey

9. Description of the centre and approach to this point

10. Number of wedges into which the area of village has been divided (give size

of wedges in degrees

11. Wedge-wise summary of enumeration’s S.No. Wedge No. Date of survey Total No. of trees

Compassing done by Tree enumeration done by Name of Crew Crew leader Dated Signature

28

TOFR – 2 VILLAGE TREES ENUMERATION FORM

State District Sample Vill. Geographical area of the

sample vill. (ha.) Total No. of Trees

1 2 3 4 5

Species name Code DBH

(cm) Age

(years) Category of plantation/

trees

Area* in ha

Species name Code DBH (cm)

Age (years)

Category of plantation/

trees

Area* in ha.

6 7 8 9 10 11 12 13 14 15 16 17

*Mention area in case of farm forestry, block plantations and village wood lots (patches only). Date………………………………. Page No………………………. Sign of Crew Leader………………………… Total No. of Pages………………….. Name of Crew Leader………………………

29

TOFR-3 DISTRICT TREE FORM

(ABSTRACT OF ENUMERATION IN SAMPLE VILLAGES) State name &

code District name &

code Total number of villages in the district

No. of sample villages in the

district

Total area of the villages in

the district (km2)

Name of Sample village

Geographical area of the

sample village (ha)

Category of the sample

village

No. of trees in the sample

village

1 2 3 4 5 6 7 8 9

Name of Trees in the Sample Village According to Category of the Plantation/Trees

Categories of tree/plantation Diameter class

Farm Forestry

Village woodlot

Block plantation

Road side plantation

Ponds Railway lines Canals Others Total

10 11 12 13 14 15 16 17 18 19 10-20 cm

20-30 cm

30-40 cm

40+ cm

Date………………………….. Total No. of Pages……………….. Sign of Crew Leader…………………

30

Instructions for completing the above forms

1. TOFR - 1 Village Description Form (VDF) 1. State and code To be filled according to sample list 2. District and code To be filled according to sample list 3. Mapsheet and code To be filled according to toposheet 4. Name of the village Name of selected village 5. Area of the village To be filled according to revenue records 6. Date of commencement of survey Self-explanatory 7. Date of completion of survey Self-explanatory 8. Conspicuous feature selected as Name of the centre viz. temple, school, etc.

the centre for starting the survey 9. Description of the centre and Self explanatory

approach to this point 10. Number of wedges into Appropriate number of wedge is to be given after

which the area of village has assessing the work load. been divided (give size of angle in degrees)

11. Wedge-wise summary of Self-explanatory enumerations S.No. Wedge no. Date of survey Total no. of trees

Signature of Dated: Crew Leader

Compassing done by Tree enumeration done by Name of Crew Leader

(Diagram of village should be attached)

2. TOFR -2 Village Tree Enumeration Form (VTEF) Col. 1: State

State name and code will be same as item 1 of TOFR-1. Col. 2: District

District name and code will be same as item 2 of TOFR-1. Col. 3: Sample village

Sample village name and code will be same as item 4 of TOFR-1. Col. 4: Geographical area of the sample village

The geographical area of the sample village nearest to hectare may be given. This will be same as item 5 of TOFR-1.

Col. 5: Total number of trees Total number of trees enumerated in the sample village. This number will tally with the total number of species coded in columns 6 and 12.

Col. 6: Species name Species name. A list of species names along with code adopted in India has been prepared and is followed. Species codes for other countries will have to be developed.

Col. 7: Species code In this column the species code will be given. Its botanical/local name is given in Col. 6.

Col. 8: Diameter at breast height (over bark) (cm) In this column the DBH over bark will be recorded to the nearest centimetre. If there is a flare at breast height, the diameter is measured immediately above and below the flare and the average is recorded. In case of buttressed and large-sized trees, take the girth and convert it to diameter by multiplying with 7/22 or 0.318.

31

In case there is forking of a tree below its breast height, diameter of each forked stem will be measured at breast height (above forking) and recorded separately, as if for two or more trees.

Col. 9: Age in years Age is to be recorded in completed years by enquiry method.

Col. 10: Category of plantation/trees This column will be filled in one digit code as given below: Category of plantation/tree Code Farm forestry 1 Village woodlots 2 Block plantations 3 Road side plantations 4 Ponds 5 Railway lines 6 Canals 7 Others 8

Col. 11:Area in hectare Note: Area is to be mentioned in case of farm forestry, block plantations and village wood lots (patches only).

Col. 12 to 17: In these columns, information on other trees will be recorded in the same manner as recorded in columns 6-11.

3. TOFR-3: District Tree Form (DTF) Col. 1: State

Name of State and its code will be filled from the sample list. Col. 2: District

Name of District and their code will be filled from the sample list. Col. 3: Number of villages in the district

Total number of villages in the district will be given from the sample list. Col. 4: Number of sample villages in the district

Number of sample villages selected in the district for inventory will be given from the sample list.

Col. 5: Total area of the villages (in km2) The total area of the village (in km2) will be given to the nearest km2 from the village record.

Col. 6: Sample village A list of total villages in the district will have to be prepared giving them appropriate codes. In this column, Sl.No. of sample village selected will be given.

Col. 7: Geographical area of the sample village (ha.) The geographical area of the sample village will be given to the nearest ha.

Col. 8: Category of the sample village The category of the sample village will be given in code as given below: Code Category of the sample village 1 Hilly, irrigated and within 5 km of forests 2 Plain, irrigated and within 5 km of forests 3 Hilly, irrigated and more than 5 km from forests 4 Plain, irrigated and more than 5 km from forests 5 Hilly, unirrigated and within 5 km of forests 6 Plain, unirrigated and within 5 km of forests 7 Hilly, unirrigated and more than 5 km from forests 8 Plain, unirrigated and more than 5 km from forests

32

If sample village has both plain and hilly areas, its category will be decided on the basis of area. If the area of the plain is more than the hilly area in the village, it will be classified as �plain�. Similarly, irrigation status will be decided in case village has both irrigated and unirrigated areas.

Col. 9: Number of trees in the sample village The total number of trees according to the following categories of plantation/tree will be recorded in column 9. Dead trees having utility less than 70 percent and all trees less than 10 cm diameter will not be recorded.

Col. 11:Farm Forestry Number of trees in a sample village under Farm Forestry will be recorded in different diameter classes.

Col. 12:Village woodlot Number of trees in a sample village under Village woodlot will be recorded in different diameter classes.

Col. 13:Block plantations Number of trees in a sample village under Block plantations will be recorded in different diameter classes.

Col. 14:Roadside plantations Number of trees in a sample village under Roadside plantations will be recorded in different diameter classes.

Col. 15:Ponds Number of trees in a sample village around Ponds will be recorded in different diameter classes.

Col 16:Railway lines Number of trees in a sample village near Railway lines will be recorded in different diameter classes.

Col. 17:Canals Number of trees in a sample village around Canal-side will be recorded in different diameter classes.

Col. 18:Others Number of trees in a sample village under category others (trees not falling in any of the above categories) will be recorded in different diameter classes.

33

ANNEX II: Examples of field forms and instructions for their completion for urban TOF

TOFU-1 Town Description Form

1. State and code

2. District and code

3. Name of the town & class

4. Area & Population of the town

5. Selected UFS blocks no., IV unit no. & ward no. in the town

(i)

(ii)

(iii)

6. Conspicuous feature selected as the starting point for the survey

7. Description of the starting point and approach to this point

8. Maps of UFS attached (Y/N)

Date of commencement of survey Date of completion of survey Tree enumeration done by

Dated Name of the Crew leader…………… Signature………………………………

34

TOFU – 2 TOWN TREES ENUMERATION FORM

State District Sample UFS Block Geographical area of the

sample Block (ha) Total No. of Trees

1 2 3 4 5

Species name Code DBH

(cm) Age

(years) Category of plantation/

trees

Area* (ha)

Species name Code DBH (cm)

Age (years)

Category of plantation/

trees

Area* in ha.

6 7 8 9 10 11 12 13 14 15 16 17

*Mention area in case of farm forestry, block plantations and woodlots (patches only). Date………………………………. Page No………………………. Sign of Crew Leader………………………… Total No. of pages………………….. Name of Crew Leader………………………

35

TOFU-3 DISTRICT TREE FORM

(ABSTRACT OF ENUMERATION IN SAMPLE TOWN)

State District No. of towns in the district

No. of UFS Blocks in the

district

Total area of the town in the

district (km2)

Sample UFS Block

Geographical area of the sample

block (ha.)

Category of the sample block

1 2 3 4 5 6 7 8

Name of Trees in the Sample Village According to Category of the Plantation/Trees

Farm Forestry

Village woodlot

Block plantation

Road side plantation

Ponds Railway lines Canals Homesteads Others Total

9 10 11 12 13 14 15 16 17 18

Date………………………….. Page No.……………….. Sign of Crew Leader………………….……… Total No. of Pages………………. Name of Crew Leader………………………

36

Instructions for completing the above forms

1. TOFU - 1 Town Description Form (TDF) The information necessary for filling in this form is similar to TOFR-1. Please refer to the description for TOFR-1

2. TOFU - 2 Town Tree Enumeration Form (TTEF) Col. 1: State It will be copied from item 1 of TOFU-1. Col. 2: District

It will be copied from item 2 of TOFU-1. Col. 3: Sample UFS block

It will be copied from item 7 of TOFU-1 Col. 4: Geographical area of the sample block

The geographical area of the sample block nearest to hectare may be given with the help of GPS. In absence of GPS it should be given after measuring length and width of the block.

Col. 5 to Col. 9: These columns are completed in the same way as in TOFR 2 Col. 10: Category of plantation or trees

This column is completed by using of the codes given below: Category of plantation or tree Code Farm forestry 1 Woodlots 2 Block plantations 3 Road-side plantations 4 Ponds-side plantation 5 Railway-lines plantation 6 Canal-side plantation 7 Homestead 8 Others 9

Col. 11: Area in hectare Note: Area to be mentioned in case of farm forestry, block plantations and wood lots (patches only).

Cols. 12 to 17: In this column, information in respect of other species will be recorded in the same manner as recorded in column 6-11.

3. TOFU - 3 District Tree Form (DTF) Col. 1: State

Name of State and code. Col. 2: District

Name of District and code. Col. 3: Number of Towns in the district

Total number of towns in the district. Col. 4: Number of sample block in the district

Number of sample blocks selected in the district for inventory. Col. 5: Total area of the Town (in km2)

The total area of the town (in km2) to the nearest km2 from the town authority or latest census handbook.

37

Col. 6: Sample block Selected sample block number from sample list.

Col. 7: Geographical area of the sample block (ha) The geographical area of the sample block to the nearest ha with the help of GPS. In absence of GPS the block needs to measured manually.

Col. 8: Category of the sample block The different categories of the sample block receive different codes

Category of the sample block Slum area (Code 1): A slum area refers to an agglomeration of densely inhabited, poorly built and/or dilapidated structures, often irregularly or asymmetrically constructed in unhygienic surroundings. Residential area (Code 2): Area used predominantly for residential purposes. Bazaar area (Code 3): Area consisting primarily of markets and shops. Restricted/prohibited area (Code 4): Areas occupied by the armed and/or police forces are declared restricted/prohibited areas. Before the UFS is to be carried necessary permission from competent authority needs to be obtained. Factory/industrial area (Code 5): Area where factories are mostly located. Other residential area (Code 6): Whenever it is not possible to distinguish an area into falling into any one of the above mentioned types (it may happen that the area is a combination of two or more types of areas mentioned above), it may be treated as �Other Residential Area�

Col. 9: Farm forestry

Number of trees in a sample block under farm forestry by diameter class. Col. 10: Woodlot

Number of trees in a sample block under woodlot by diameter class. Col. 11: Block plantations

Number of trees in a sample block under block plantations by diameter class. Col. 12: Roadside plantations

Number of trees in a sample block along roads by diameter class. Col. 13: Ponds

Number of trees in a sample block around ponds by diameter class. Col 14: Railway lines

Number of trees in a sample block along railway lines by diameter class. Col. 15: Canals

Number of trees in a sample block along canals by diameter class. Col. 16: Homestead

Number of trees in a sample block in homesteads by diameter class. Col. 17: Others

Number of trees in a sample block under category others (trees not falling in any of the above categories) by diameter class.

Col. 18: Total Total number of trees in a sample block under all categories of plantations by diameter class.

38

39

Bibliography Ariyadasa, K.P. 2002. Assessment of tree resources in the home gardens of Sri Lanka. EC-FAO

Partnership Programme (2000-2002). Food and Agriculture Organization of the United Nations, Bangkok.

FAO. 1974. Manual of forest inventory with special reference to mixed tropical forests. Food and Agriculture Organization of the United Nations, Rome.

FAO. 2001. Global forest resources assessment 2000. Main report. Food and Agriculture Organization of the United Nations, Rome.

FMP. 1992. Forestry master plan. Asian Development Bank, Manila. FSI. 1999. State of Forest Report 1999, Forest Survey of India, Dehradun, India. FSMP. 1995. Forestry sector master plan of Sri Lanka. Ministry of Lands, Agriculture and

Forestry. Colombo. Guevara, S., Laborde, J. & Sánchez, G. 1998. Are isolated remnant trees in pastures a fragmented

canopy? Selbyana, 19(1): 34-43. Holmgren, P., Masakha, E.J. & Sjöholm, H. 1994. Not all African land is being degraded: a recent

survey of trees on farms in Kenya reveals rapidly increasing forest resources. Ambio, 23(7): 390-395.

Kleinn, C. 1991. Zum Waldbegriff in Forstlichen Großrauminventuren (On forest definitions in large-area forest inventories). Allgemeine Forst und Jagdzeitung, 162: 201-210. (English summary).

Kleinn, C. 2000. On large-scale inventory and assessment of trees outside forests. Unasylva 200: 3-10.

Lund, G. 1996. Generalities and necessary tangents: considerations for designing agroforestry inventories. In R.K. Kohli & K.S. Arya, eds. Proceedings of the IUFRO-DNAES International Meeting on Resource Inventory Techniques to Support Agroforestry and Environment Activities, Chandigarh, India, 1-3 October 1996, p. 1-12. Chandigarh, India, HKT Publications.

Nair, P.K.R. 1987. Agroforestry systems inventory. Agroforestry Systems, 5: 301-317. Sinclair, F. 1999. A general classification of agroforestry practice. Agroforestry System. 46: 161-

180. Sylvander, R.B. 1981. Bosques del país y su distribución por provincias. San José, Costa Rica,

Universidad Estatal a Distancia. Van Leeuwen, A.C.J. & Hofstede, A.M. 1995. Forests, trees and farming in the Atlantic Zone of

Costa Rica. An evaluation of the current and future integration of trees and forests in farming systems in the Atlantic Zone of Costa Rica. Technical Series, Technical Report No. 257. Turrialba, Costa Rica, Tropical Agriculture Research and Higher Education Center (CATIE).