Forestry DepartmentFood and Agriculture Organization of the United Nations

Forest Resources Assessment Programme Working Paper 50Rome 2001

FRA 2000

GLOBAL FOREST COVERMAPPING

FINAL REPORT

Rome, 2001

The Forest Resources Assessment Programme

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

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

FAO, at the request of the member nations and the world community, regularly monitors the world’sforests through the Forest Resources Assessment Programme. The next report, the Global ForestResources Assessment 2000 (FRA 2000), will review the forest situation by the end of the millennium.FRA 2000 will include country-level information based on existing forest inventory data, regionalinvestigations of land-cover change processes, and a number of global studies focusing on theinteraction between people and forests. The FRA 2000 report will be made public and distributed onthe World Wide Web in the year 2000.

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

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

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

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

DISCLAIMERThe Forest Resources Assessment (FRA) Working Paper Series is designed to reflect the

activities and progress of the FRA Programme of FAO. Working Papers are not authoritativeinformation sources – they do not reflect the official position of FAO and should not be used for officialpurposes. Please refer to the FAO forestry website (www.fao.org/fo) for access to official information.

The FRA Working Paper Series provides an important forum for the rapid release ofpreliminary FRA 2000 findings needed for validation and to facilitate the final development of anofficial quality-controlled FRA 2000 information set. Should users find any errors in the documents orhave comments for improving their quality they should contact either Robert Davis or Peter Holmgrenat fra@fao.org.

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Table of Contents

1 INTRODUCTION .......................................................................................................................................7

1.1 REQUIREMENTS FOR A GLOBAL FOREST COVER MAP .....................................................................................71.2 REVIEW OF LARGE-AREA LAND/FOREST COVER MAPPING ............................................................................8

2 OBJECTIVES............................................................................................................................................10

3 METHODOLOGY....................................................................................................................................11

3.1 TEMPORAL IMAGE COMPOSITING ...............................................................................................................113.2 ESTIMATING PERCENT FOREST COVER WITH STRATIFICATION....................................................................123.3 ADAPTING THE USGS SEASONAL LAND COVER CLASSES TO THE FAO CLASSIFICATION ...........................143.4 VALIDATION – A PRAGMATIC PLAN............................................................................................................14

4 RESULTS AND DISCUSSION................................................................................................................16

5 SUMMARY................................................................................................................................................21

REFERENCES....................................................................................................................................................23

APPENDIX 1: LOOK-UP TABLE FOR ESTABLISHING CORRESPONDENCES BETWEEN THREEEXISTING REFERENCE DATA SETS AND THE FAO FRA2000 GLOBAL LAND COVER MAPLEGEND..............................................................................................................................................................28

FRA WORKING PAPERS ................................................................................................................................29

Paper drafted by: Zhiliang Zhu and Eric Waller, EROS Data CenterEditorial production by: Patrizia Pugliese, FAO, FRA Programme

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Abbreviations

AVHRR Advanced Very High Resolution Radiometer

BEF Biomass Expansion Factor

BV Biomass of inventoried volume

CAS Chinese Academy of Sciences

EDC

EOS

EZ

Eros Data Centre

Earth Observation System

Ecological Zone

FAO Food and Agricultural Organization of the United Nations

FORIS Forest Resources Information System

FRA Forest Resources Assessment

GIS

GOFC

IGBP

IR

Landsat MSS

MRLC

NDVI

NGO

Geographic Information System

Global Observation of Forest Cover

International Geosphere and Biosphere Programme

Infra Red

Landsat Multi-Spectral Scanner

Multi-Resolution Land Characterization

Normalized Difference of Vegetation Index

Non-governmental organization

RGB Red, green, blue

TM Thematic Mapper

TREES Tropical Ecosystem Environment observations by Satellites; joint project of the

commission of the European Community

USGS U.S. Geological Survey

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ABSTRACT

Many countries periodically produce national reports on the state and change of forestresources, using statistical surveys and spatial mapping of remotely sensed data. At the globallevel, the Food and Agriculture Organization (FAO) of the United Nations has conducted aForest Resources Assessment (FRA) program every 10 years since 1980, producing statisticsand analysis that give a global synopsis of forest resources in the world. For the year 2000 ofthe FRA program (FRA2000), a global forest cover map was produced to provide spatialcontext to the extensive survey. The forest cover map, produced at the U.S. GeologicalSurvey (USGS) EROS Data Center (EDC), has five classes: closed forest, open orfragmented forest, other wooded land, other land cover, and water. The first two forestedclasses at the global scale were delineated using combinations of temporal compositing,modified mixture analysis, geographic stratification, and other classification techniques. Theremaining three FAO classes were derived primarily from the USGS global land covercharacteristics database (Loveland et al. 1999). Validated on the basis of existing referencedata sets, the map is estimated to be 77 percent accurate for the first four classes (no referencedata were available for water), and 86 percent accurate for the forest and nonforestclassification. The final map will be published as an insert to the FAO FRA2000 report, andthe map data are available through the EDC web page.

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1 Introduction

1.1 Requirements for a global forest cover map

To reverse the trend of global climate change caused by greenhouse gases, the UnitedNations Framework Convention and the Kyoto Protocol have stipulated that it is necessary totake immediate policy actions to reduce the emissions. Some of important emission-cuttingactions are within the forestry sector, such as sustainable forest management, forestecosystem health, and woody plantations, which require consistent and periodic inventoryand reporting to verify their status, progress, and credibility. The Food and AgricultureOrganization (FAO) of the United Nations (UN) conducts a periodic global Forest ResourcesAssessment (FRA) program (FAO 1995). The basis and rationale of the program are providedthrough the UN Conference on Environment and Development held in Rio de Janeiro in June1992, which required the observation and assessment of global forest resources as a keyelement in Agenda 21, the conference documentation. The FAO program has been conductedfor every ten years since 1980, with the current FRA survey referenced for year 2000(FRA2000).

Regular FRA program components in the previous and current surveys are comprised of atally and report of the most current forest inventory from each country, change analysis basedon a statistical sample of Landsat image interpretations, and other modeling and analysistechniques (FAO, 1995). In addition to these components, FRA2000 was also required toproduce a global forest cover map showing spatial distribution of the world's existing forestsfor this survey. Expert panels convened by FRA in the early stages of the program (Nyssönenand Ahti, 1996, Päivinen and Gyde, 1997) noted that a new remote sensing-based globalforest cover map should be produced using concurrent satellite data to represent thegeographic distribution of the forest resources surveyed under FRA2000. A number of large-area land/forest cover mapping products (discussed below) were presented and theirapplications were discussed. However, it was apparent that none of them met concurrentneeds of FRA2000 in terms of scope (global), theme (forest cover and forest density), andtiming (data from mid 1990's). These existing products demonstrated the feasibility of a FAOglobal forest cover mapping effort for use by FRA2000 and its constituents.

Thematically, the proposed FRA2000 forest cover legend was not complex – only five landcover classes were required (Table 1): closed forest, open or fragmented forest, other woodedland, other land cover, and water. This map legend is a simplified version of a morecomprehensive FRA classification system used for the ground survey and reportingcomponents in the previous and the current surveys (FAO, 1995).

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Table 1. FAO FRA 2000 global land cover map legend, definitions, and representative landcover types.FRA2000Class FAO Definition Representative land cover

Closed forest

Land covered by trees with a canopy coverof more than 40 percent and heightexceeding 5 meters. Includes naturalforests and forest plantations.

− Tropical/subtropical moist forest− Temperate broadleaf mixed forest− Subtropical/temperate conifer

plantation− Boreal conifer forest

Open orfragmentedforest

Land covered by trees with a canopy coverbetween 10 and 40 percent and heightexceeding 5 meters (open forest), ormosaics of forest and non-forest land(fragmented forest). Includes naturalforests and forest plantations.

− Northern boreal/taiga open coniferor mixed forest

− Southern Africa woodland− Tropical fragmented/degraded

forest

Other woodedland

Land either with a 5-10 percent canopycover of trees exceeding 5 meters height,or with a shrub or bush cover of more than10 percent and height less than 5 meters.

− Mediterranean closed shrubland− Tropical woody savanna

Other land cover All other land, including grassland,agricultural land, barren land, urban areas.

− Grassland, cropland, non-woodywetland, desert, urban

Water Inland water − Inland water

This legend was developed considering several factors, including source data, time and scoperequirements, previous project experience, and FRA forest cover needs. In the 1990’s, owingto its coarse resolution and daily repeat cycle, the Advanced Very High ResolutionRadiometer (AVHRR) sensor was perhaps the only choice for a global-scale land covermapping effort. Constraints of the coarse resolution, plus the requirement for a quick 2-3 yeardelivery time for the global scope, necessitated that the classification legend should berelatively simple. Mapping forest types, in addition to canopy density, may be desirable, butat the global level, consensus and consistency are lacking as to what and how many foresttypes should, and can be mapped within the scope of this project.

1.2 Review of large-area land/forest cover mapping

The public and institutional awareness of the scientific and policy issues surrounding forestresources exploration and conservation has led to an increased emphasis in the last twodecades on land cover and forest cover mapping programs. Maturing technologies in space-borne sensor, computer, and image processing techniques are another reason for the recentlyincreased use of remote sensing for monitoring and assessment of forest resources (Lovelandet al. 1999). While many mapping programs were intended for specific locations and inresponse to specific local or regional needs, there are large-area (large countries, continents,global) land/forest cover mapping programs designed to provide data to meet global scienceand policy requirements (Tucker and Townshend, 2000).

In the 1980’s and 1990’s, a number of studies demonstrated the feasibility and effectivenessof large-area, wall-to-wall land cover or forest cover mapping using coarse or mediumresolution satellite data such as AVHRR and Landsat MSS (Multi-Spectral Scanner). Studiesof Africa land cover (Tucker et al. 1985), Canadian land cover (Cihlar et al. 1996), European

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land cover (Mücher et al. 2000) and the U.S. land cover (Loveland et al. 1991) relied onspectral and seasonal properties of land cover from AVHRR imagery for classification ofgeneral land cover patterns. Large-area forestry applications, conducted using a variety oftechniques, include Amazon deforestation monitoring (Skole and Tucker, 1993), the TREEStropical forest mapping project (Malingreau et al. 1995), mapping of the U.S. forest types(Zhu and Evans, 1994) and classification of European forest ecosystems (Roy et al. 1997). Atthe global scale, Hansen et al. (2000) classified land cover at 1 km resolution using 41metrics as input data into a classification tree. The U.S. Geological Survey (USGS) EROSData Center (EDC) has developed a flexible, multi-theme land cover characteristics database(Loveland et al. 1999) for use by a broad range of applications. Depending on land covercomplexity and land use history, the numbers of land cover classes in the database, derivedprimarily from their seasonal characteristics, ranged from 137 for Australia to 255 forEurasia. The seasonal land cover classes were further summarized to a 17-class legenddefined by the International Geosphere and Biosphere Programme (IGBP) (Belward, 1996).The IGBP classification legend includes five forested classes based on leaf type andlongevity: evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest,deciduous needleleaf forest, and mixed forest1. In addition to coarse or medium resolutionsatellite data, 30-meter Landsat image data have been increasingly used to produce wall-to-wall land cover maps in large areas, (e.g., China (Liu and Buheaosier, 2000), U.S.(Vogelmann et al. 1998)). However, presently these mapping efforts remain prohibitivelyexpensive in terms of time and effort to be applied to the global level.

Following the successful completion of the USGS global land cover effort, it would belogical to consider using the USGS/IGBP results for the FAO FRA2000 mapping needs.However, because the USGS seasonal land cover database was not intended to optimizeforest canopy, no direct relationship exists to permit a simple conversion of the seasonalforest cover type classes to the FAO forest canopy classes. The closed versus open forestrequirement in the FAO classification implied that forested classes in the USGS database,described on the basis of vegetation seasonality, productivity, and leaf type, were not entirelyappropriate for an one-to-one conversion to FAO classes. A new approach was required forthe FAO mapping effort.

1 Remaining 17 IGBP classes are: closed shrubland, open shrubland, woody savanna, savanna, grassland,wetland, cropland, urban, cropland and natural vegetation mosaic, barren, permanent snow and ice, water.

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2 Objectives

The goal of this project was to produce a validated global forest cover map for use by theFAO Forest Resources Assessment 2000 Program. Actual forest cover, as of mid-1990's, wasto be mapped with AVHRR 1-km data to indicate the geographic distribution and conditionsof global forest resources. Results from this study would also be useful to other applications,chiefly global change research, and climate, ecological and forest modeling efforts.Specifically, the project had the following objectives:

• Model and develop a global forest canopy cover density data set using mid-1990’sAVHRR 1-km image data.

• Derive the five-class FRA2000 forest cover map using the forest canopy density data setand the USGS global land cover database.

• Validate the global forest cover map using independent reference data sets.

• Conduct cartographic design of a paper map for FRA2000 (not presented in the paper).

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3 Methodology

Mapping global forest cover, even for only a limited number of classes, is a large undertakingwith many challenges that are unique to the scale of the effort (Cihlar 2000). For a givenclass, many different forest types and physiognomy in various geographic regions of theworld give rise to great variations in their spectral or seasonal properties. This requiresextensive studies of regional forest distribution and the use of flexible stratification in themapping. For a mapping area as large as a continent or the world, land cover conditions andpatterns in many regions are unfamiliar to the mapping analysts. There are problems withpersistent clouds, atmospheric contamination, sensor viewing angle, and solar illumination,leading to varied data quality in global image data sets. Under these circumstances, which areinherent and unique to the scale and pixel sizes of large-area mapping, many large-area landcover mapping efforts have relied on the use of temporal composite data, stratification,unsupervised classification, and interpreter skills to compensate for these scale-dependentfactors. The USGS global land cover characterization project also adopted these measures, aswell as the use of a flexible land cover description on the basis of vegetation seasonality asmeasured by a temporal series of AVHRR NDVI (normalized difference of vegetationindex). While the USGS global land cover database does not differentiate open from closedforest lands, descriptions in the database for the other three required land cover types underthe FAO legend – other wooded land, other land cover, and water – were compatible.

Still, the requirements for forest canopy density mapping (the first step leading to the firsttwo classes), clearly suggested that there was a need for a flexible methodology that allowedcertain interactive flexibility in deriving and correcting for the FAO classes. Themethodology used for the FAO global forest cover mapping consisted of temporalcompositing, estimating percent forest cover, linking percent forest cover to the USGS globalland cover classes, and validation.

3.1 Temporal image compositing

Source data used for the FAO forest cover mapping were drawn from AVHRR NDVIcomposites produced for February 1995-January 1996, the latest data year available when theproject began. The temporal composites, consisting of five calibrated AVHRR bands and aNDVI band, were initially computed at EDC using the protocol of maximum NDVI value (tominimize the amount of clouds in imagery) over 10-day compositing periods (Eidenshink andFaundeen 1994). Bands 1 and 2 were further corrected for atmospheric ozone and Rayleighscattering effects. For this study, bands 1 and 2 (red and infrared) and NDVI of the 10-daycomposites were processed into monthly composites (three 10-day composites for eachmonthly composite), using a rule of minimum band 1 (red band) value (Waller and Zhu,1999). Studies have shown that, in the temporal compositing process, the maximum NDVIalgorithm tends to retain large off-nadir pixels in the backscatter direction (Cihlar et al. 1994,Qi et al 1991), resulting in varying pixel sizes and additional noise introduced into spectralbands. Indeed, visual examination of the source data showed that large backscatter viewangles were associated with raised red and IR reflectance in forestland, leading to confusionwith vigorous agriculture and added noise in open or fragmented forest regions. As a partialmeasure to correct for the bi-directional effect, the minimum red band-compositing rule used

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for this study was found to better preserve pixels near nadir or in fore-scatter direction andmaintain the integrity of patterns of forest lands. There are also geometric concernsassociated with the fore-scatter view angles selected by the minimum red compositing, butthese are thought to be of lesser importance than the reflectance concerns (Waller and Zhu,1999).

The final image data consisted of 12 monthly composites of the first two AVHRR bands (redand infrared) and NDVI band for each of five major land masses: Africa, Australia andtropical Asia2, Eurasia (Europe and temperate/subtropical Asia), North America, and SouthAmerica. Lack of good quality image data prevented several important tropical island chainsin the Pacific, including Hawaii, Micronesia, and Polynesia, from being mapped. For eachmajor land mass, initial image data preparation and subsequent mapping were conductedusing an equal-area Interrupted Goode Homolosine projection (Steinwand 1994). Finishedmap products were re-projected to two map projections: Lambert equal-area projection for allcontinental masses and a global Robinson projection for global presentation.

3.2 Estimating percent forest cover with stratification

The concept of spectral mixture analysis (SMA) quantifies pixels as fractions of basic surfacecomponents (Smith et al., 1990, Wessman et al. 1996), or “endmembers,” such as greenvegetation, soil, and shade. Mixture analysis provides a means to estimate amount ofendmembers within target pixels, and results serve as one additional layer of informationabout land cover (Zhu and Evans, 1994). It is generally understood that, in relatively smallstudy areas and with sufficient spectral information, unique and representative endmemberscan be identified to produce reasonable results. However, spectral mixture analysis isnevertheless important to large-area land cover mapping conducted with coarse-resolutionsatellite imagery as the primary data set, since coarse pixels (e.g. 1-km) are more likely thanfine resolution (e.g. 30-m) to contain mixed land cover types. Several recent large-area landcover studies approached the problem of quantifying mixed pixels with different algorithms.For instance, Defries et al (2000) used a multivariate, multitemporal regression model toestimate 8-km global land cover mixtures. For the tropical forest TREES project, a spatialregression estimator was used to improve coarse-resolution area estimates of deforestation onthe basis of a fine-resolution sample (Mayaux and Lambin 1995). These algorithms reducedthe need for a time-consuming refinement process in classification (Cihlar 2000) and provedthe key element in achieving overall objectives of these studies.

For the objective of estimating fraction of forest cover in 1-km pixels, we adopted amethodology that combined linear mixture modeling with scaling of NDVI and the visibleband based on pixel positions along the infrared band (Figure 1). Development of thismethodology resulted from our observations that it would be impractical to obtain asufficiently large sample of high-resolution reference data set in the global frameworkrequired to run a TM-guided approach. Moreover, a simple use of the mixture analysisusually would not result in satisfactory results for forest cover, particularly over a largemapping area, since endmember fractions might not directly correspond to forest fractions(Roberts et al., 1993). In spectral space, forest is often a mixture of vegetation and shade(traditional endmembers) and its spectral position can vary according to a variety of factorssuch as leaf type, structure, productivity, or topography (Waller and Zhu, 1999). View angle

2 Grouping of tropical Asia with Australia was primarily for convenient image processing and mapping.

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and solar illumination problems as well as open woodland with varying soil backgroundwould complicate the analysis even further. These issues necessitated the use of geographicstratification and flexible modeling.

In the combined model, pixels were modeled depending on their relative positions along theinfrared band (Y-axis). Pixels with low IR reflectance contained new forests in burned areas,woody wetlands, and other dark land cover such as shadow or water, and these pixels couldbe best scaled with their NDVI values because NDVI was considered insensitive toillumination variance (Holben et al. 1986). It may be noted in the red-IR space that, for greenvegetation, the distribution of NDVI values varies between the diagonal line (0 value) and theboundary near Y-axis (NDVI as 1). It was found that NDVI values varied between 0.3 and0.8 from open woodland to closed forest in the low IR range. Scaling was flexibly setbetween different forest cover types.

Pixels with high IR reflectance were treated in a two-band unmixing method, using the threeendmembers, as shown in Figure 1: A as forest (usually healthy deciduous forest), B asagriculture (bright green), and C as non-vegetated land. Linear combinations of the three landcover types were then solved for individual fractions with the least-square fit technique(Smith et al. 1990, Roberts et al. 1993). For pixels with mid-range IR reflectance for greenvegetation, which represented conifer or mixed forests, fragmented forest (forest mixed withagriculture), open woodland, shrubland and grassland, a linear scaling of AVHRR red bandwas applied. Previous studies have shown that, with careful applications of stratification toboth land cover and physiography, forest cover density was highly correlated to red bandreflectance in this spectral range (Yang and Prince, 1997).

Even with the three methods in the mixture analysis model, significant regional variations inclimate, topography, and forest types dictated that geographic stratification be used to ensurethat the same canopy definitions be mapped across varying regional conditions. For example,dry miombo woodland in southern Africa would display different spectral properties fromthat of fragmented tropical moist forest in the Congo Basin, even if they had a similar rangeof canopy openness. Geographic stratification allowed treating regional variations separatelyby resetting threshold or endmember values flexibly to fit with vegetation conditions beingmapped. Geographic stratification for each continent was based on digitized lines followingcombinations of ecoregions, physiography, vegetation types, and imagery conditions.

The three methods in the combined model for forest canopy mapping were applied to eachmonthly AVHRR composite. To provide the least atmospherically affected results, finalpercent forest cover for a continent was determined over the course of the year (February1995-January 1996) on the basis of maximum monthly forest cover value achieved,regardless of the method chosen. The maximum forest compositing was compared to averageforest canopy cover over the course of the year; areas of high ratio were examined to preventoverestimating from anomalous data. The two FAO forest classes: closed forest and open orfragmented forest were then derived on the basis of FAO definitions: 40-100 percent and 10-40 percent.

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3.3 Adapting the USGS seasonal land cover classes to the FAOclassification

After the first two FAO classes (closed forest, open or fragmented forest) were mapped usingthe mixture analysis model described above, the existing USGS global land cover databasewas adapted to derive the next three classes: other wooded land, other land cover, and water(Table 1). Vegetation classification and descriptions in the USGS global land cover databasewere built on characteristics of vegetation seasonality, productivity and leaf types determined interms of 1992-93 AVHRR NDVI and other attributes such as general topography and climate(Loveland et al. 1999, Reed et al. 1994). The magnitude of integrated NDVI over the lengthof the temporal period helped separate successively decreasing vegetation primaryproduction, ranging from forested land to open woodland, shrub and grass, and sparse landcover (Reed et al. 1994). Additionally, seasonal variations were investigated to partiallysupport identification of agricultural land uses (e.g., single versus multiple cropping). At themost basic level of the database is the flexible classification of seasonal land cover regions,with the number of classes at this level varying among continents as a function of land covercomplexity and land use history.

To derive the three FAO classes, the USGS seasonal land cover classes were used as thebaseline data on a continent-by-continent basis. The refinement methods to fit USGS classesto FAO definitions were similar to the methods used in producing these USGS classes,namely that refinements depended on local conditions of land cover and relied on a carefulstudy of all available evidence. Loveland et al. (1999) describes the overall approach indetail. The “other wooded land” class consisted primarily of open or closed shrub land coverin tropical and subtropical regions as well as low density tree cover in northern boreal zonesnear the polar regions. The “other land cover” class contained mostly barren land, grassland,and cropland from the USGS database. Class merges and splits were aided by ancillary datasets, such as ecoregions and digital elevation models. Spectral reclustering, as well as user-defined polygon splits, was also used. In areas of disagreement, the initially defined first twoFAO classes took priority. The three FAO classes were merged with the first two forestclasses for each continent, and all continents were combined into a single global forest covermap.

3.4 Validation – a pragmatic plan

Assessing accuracy for results of large-area land cover mapping is a necessary yet difficulttask largely because of potentially high costs – in terms of both resources committed andtime. Moreover, as the mapping area is large and land cover in coarse-resolution pixels ishighly variable, requirements on sampling and data collection may be more stringent thansmall, less heterogeneous mapping areas. As Cihlar (2000) noted, these constraints limitaccuracy assessment for large-area land cover to a matter of compromise between what isdesired and what is available. For this study, an independent, global-scale, sampling-basedreference data set was not available. Instead, the validation plan was centered around a high-resolution interpreted data set used for validating the IGBP global land cover, and twonational land cover maps: China and the U.S.

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The IGBP validation data set (Scepan, 1999) contained 312 interpreted sample sites from arandomly selected Landsat-5 Thematic Mapper (TM) sample, acquired in early 1990’s andstratified by the IGBP land cover classes (Belward, 1996). The sample sites were 1 squarekilometer in size in the Landsat scenes, and were interpreted by a team of regional land coverexperts (Scepan, 1999). The Landsat sample was stratified by class for 16 of the 17-classIGBP legend; water was not sampled. To provide a knowledge of accuracy for the FAOglobal forest cover map, the IGBP validation data set was re-coded from the 16-class IGBPlegend to the first 4 FAO classes, and then to forest or nonforest (Appendix 1). This re-codeddata set was overlaid on the global FAO map to derive a set of accuracy statistics includingoverall accuracy, and user’s and producer’s accuracy (Stehman and Czaplewski, 1998) for thefirst four classes in the FAO legend. The accuracy assessment was conducted for eachcontinent rather than landmasses used as separate mapping areas, since the IGBP validationdata set was coded for continents.

Two special cases in the overall validation effort were the two existing, wall-to-wall, landcover maps produced with 30-meter Landsat-5 TM data acquired in the early 1990’s. ForChina, the Chinese Academy of Sciences (CAS) developed a wall-to-wall land coverclassification using Landsat-5 data in early 1990’s (Liu and Buheaosier, 2000). Similarly,early 1990’s Landsat 5 data were used to produce a conterminous U.S. land cover map(Vogelmann et al. 1998) by the Multi-Resolution Land Characterization (MRLC)Consortium. Reclassifications of the two land-cover maps to the FAO legend are given inAppendix 1. Because of the issue of mismatch between the existing data sets and the fullFAO map legend (4 classes, excluding water), we derived accuracy numbers based on onlyforest/nonforest reclassification. The China and U.S. land cover maps were resampled to 1square km pixel size and overlaid to the FAO global forest cover map in the same locations.Again, the commonly used accuracy parameters, such as the overall accuracy, and user’s andproducer’s, were derived from the overlay.

All three reference data sets had different mapping techniques, and their reclassifications tothe FAO legend were approximate. Still the utility of the three existing data sets is that,together, the three data sets provide users an indication of consistency of thematic accuracy atdifferent levels (global vs. China and U.S.). It may be noted that, while other land covermaps existed, only the two data sets from China and U.S were used. This is because 1) thedata sets covered sufficiently large areas, 2) dates of image data were close (early 90’s), and3) primary image data were the 30-meter Landsat, following the convention of using finerresolution data to validate products from coarser source data (e.g., Kloditz et al. 1998).

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4 Results and discussionThe result of the mapping project is a global forest cover map with the FRA2000 legend(figure 2), which portrays the spatial distribution of forested lands in the world as of mid the1990’s. The modeled forest canopy density (fraction of square km) is an additional data set,an intermediate product as the result of our methodology. The primary function of this uniquedata set was to provide a quantification of forested land in various regions of the world, andto help define closed forest from open or fragmented forest, and open or fragmented forestfrom non-forested land. This global map may potentially be useful to other applications. As aquantified output, the data set may be used in large-scale (continental or global) climatemodels to test for different forest cover scenarios, or in other large-area biogeochemical,ecological, conservation models that require both quantitative and categorical forest coverdescriptions. This data set, however, is not validated and is not required for the FRA2000program.

The global forest cover map will be published as a poster map, in a global Robinsonprojection, by the FAO FRA2000 program to illustrate distribution and conditions of globalforest resources surveyed under the 10-year program. The digital data set will be madeavailable, similar to other USGS large-area land cover products, on the EDC webpage3. Theforest cover data set is a new land cover layer in the USGS global land cover characteristicsdatabase and complements other thematic layers in the database in holistically describing theglobal land cover.

Area estimates are derived by classes and by major landmasses (Table 2). AlthoughAntarctica was not contained in the input data, it is included in the table and on the globalmap as “other land cover” (barren, ice and snow). Affecting the area estimates in a relativelysmall way are small islands in the world and several tropical island chains (Hawaii,Micronesia and Polynesia). These islands were not included in the mapping effort due to thelack of quality image data consistent with the rest of the world. Compared to the missingislands, a few other factors inherent to the AVHRR source data probably exerted far greaterimpact on precision of the area estimates. These factors include image quality problems suchas atmospheric contamination and view/illumination geometry, misclassification errors,issues related to spatial scale including resolution and area distortion. It is important torecognize that the area estimates in Table 2 (and mapped forest cover patterns) are imprecise,and they should be treated in the context of continental and global scales, instead of localsignificance.

3 Http://edc.usgs.gov

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Table 2. Area estimates (rounded to thousands square kilometers except for global percent) ofFAO FRA2000 classes by global total and major landmasses. The five landmasses are Africa(AF), Australia and tropical Asia (AP), Europe and temperate/subtropical Asia (EA), NorthAmerica (NA), and South America (SA). The area estimates are derived from continentalLambert equal-area projections.

FRA2000 Classes Global Globalpercent AF AP EA NA SA

Closed forest 28,875 19.36 2,774 1,919 10,800 7,149 6,231Open or fragmented forest 15,465 10.37 3,511 899 5,442 2,329 2,879Other wooded land 11,805 7.91 5,152 748 2,433 687 3,197Other land cover 89,7254 60.16 18,267 7,244 31,779 14,031 5,194Water 3,283 2.20 298 54 1,839 830 261

4This figure includes total area of Antarctica given by the National Geographic world atlas, sixth edition.

The definitions of the FAO forest classes were developed based on ecological characteristicsof broad regional forests in the world, rather than from any remote sensing studies. Thereexists a certain correspondence between forest canopy cover and satellite image data, asshown in this mapping exercise. However, such correlation between satellite spectral data andpercent forest cover is not unique and variations are great in different geographic regions andvegetation types. Forest lands of different canopy cover are mapped based firstly onecological knowledge of these forests. For example, northern boreal forests in subpolar zonesor parts of the miombo woodland in Africa are known to have canopy densities below 40percent. The use of models and geographic stratification only provided refinements to thecanopy openness.

Our strategy generally assumes that forests tend to occupy a specific region in spectral spaceand that regionally specific compositing and modeling will ensure the separation of forest andnon-forest. Unfortunately, even with an optimal strategy for enhancing discrimination, thereare unavoidable ambiguities; these range from spectral ambiguities in land cover, toambiguities resulting from atmospheric, illumination, and view angle effects. The former caninclude land cover that is physically similar to forest but doesn’t meet the forest heightdefinition (e.g. Mediterranean shrubland and heathland of Scotland). Other land cover mayappear similar to forest due to a mixed pixel effect. Non-woody wetlands, and othervegetation/water mixes, vegetation/dark soil mixes, and vegetation/burn mixes can have thespectral appearance of forest.

Similarly, cloud shadow, low sun angles, and topography are factors that can cause non-forestvegetation to look forested. However, most atmospheric and view angle effects have thetendency to make forests brighter, and thus more like non-forests; these include atmosphericeffects (haze or subpixel clouds) that are especially prevalent in the tropics. Even year longcompositing can fail to remove some of these problems, especially over some cloud forests.Similarly, backscatter viewing geometry can make forests look unforested, but compositingreduces these problems. Extreme cases, such as eastern North America, can be amelioratedwith stratification, although view angle-specific corrections might be preferred.

As noted before, the global IGBP validation data set (primarily TM interpretation sites) wasused as an independent reference data set to assess the accuracy of the global FAO forestcover map. To accomplish this goal, it was necessary to approximate the FAO classes with

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the IGBP legend. Table 3 shows the results of the accuracy assessment. The accuracy andassociated standard error estimates were derived based on simple random sampling (Stehmanand Czaplewski, 1998).

Table 3. The error matrix, user’s and producer’s percent accuracy estimates using theIGBP global validation data set. The IGBP legend was re-labeled to approximate theFAO legend (the first four classes). The overall accuracy and standard error estimatesare 76.92% and 0.24%, respectively.

IGBP Global ValidationDataset

FAO legend 1 2 3 4

Rowtotal User's Standard

error

1 65 2 3 8 78 83.33 0.422 13 9 3 17 42 21.43 0.643 1 2 6 10 19 31.58 1.104 3 8 2 160 173 92.49 0.20

Column total 82 21 14 195 312Producer's 79.27 42.86 42.86 82.05Standard error 0.45 1.10 1.37 0.28

Not surprisingly, FAO classes 2 and 3 (open or fragmented forest, other wooded land) havelow accuracy than class 1 (closed forest) and class 4 (other land cover). While closed forestand other land cover (e.g., cropland, grassland) are relatively easy to define, the exactboundaries in terms of canopy cover between open or fragmented forest and either the closedforest or other land cover are spectrally highly variable. Particularly at the lower forestcanopy cover, the amount of open ground with bright soil tends to contribute tomisclassification between open or fragmented forest and other land cover such as barren orshifting agriculture. Similarly for the other wooded land (class 3, primarily woody savannaand shrubland), where this type of woody cover ends and other land cover types (e.g.,grassland, forestland or barren) begin is highly subjective. There is also the factor ofincompatibility between the IGBP legend (basis of the IGBP global validation data set) andthe FAO legend (see Appendix 1). For example, IGBP class 14 (cropland and naturalvegetation mosaic) was re-coded to other land cover (FAO class 4), even though the originalclass clearly represented the situation where patchy forest land or shrub land could bepresent. The compromise of recoding is a potential source for artificially low accuracy,particularly for FAO classes 2 and 3.

Accuracy estimates, for the first four classes and for forest/nonforest aggregations, were alsocalculated for each of six continents: Africa (AF), Australia and New Zealand (AU), Asia(AS), Europe (EU), North America (NA), and South America (SA). Sample sizes for thecontinents varied between 13 (Australia and New Zealand) and 82 (North America). Thisvariation largely reflected the diversity of IGBP land cover in the original random stratifiedsampling method (Scepan, 1999). Because of limited sample sizes for the continents, per-class accuracy and standard error estimates would be imprecise, and therefore were notcalculated. Only global and continental overall accuracy estimates are given in Table 4.

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Table 4. Sample size and overall accuracy of the first four FAO classes andforest/nonforest aggregations for global and six mapped continents: Africa (AF),Australia and New Zealand (AU), Asia (AS), Europe (EU), North America (NA), andSouth America (SA).

Global AF AU AS EU NA SASample size 312 69 13 68 43 82 37Classes 1-4 76.92 68.12 92.31 82.35 72.09 79.27 78.38Standard error 0.24 0.57 0.77 0.47 0.69 0.45 0.69Forest/nonforest 85.58 76.47 92.31 85.29 86.05 86.59 97.30Standard error 0.20 0.51 0.77 0.44 0.54 0.37 0.26

Notwithstanding the factor of limited sample sizes, accuracy estimates at the continental levelclearly show that land cover homogeneity is an important factor in determining mapaccuracy. For forest and nonforest land cover classification, the FRA2000 map is mostaccurate in South America, largely due to the continuous, unbroken tropical moist forest inAmazon Basin; and it is least accurate in Africa, as Congo Basin rain forest is smaller thanthat of South America, and the large areas of miombo woodland in southern Africa arespatially and temporally highly variable. Reasonable accuracy results in the northernhemisphere perhaps reflect the fact that land cover is relatively established and knowledge ofland cover in the regions can be easily obtained. For Australia and tropical Asia, the highaccuracy may be the result of the small sample size (13 sample sites). Of course, regionalland cover within the continents, and associated map accuracy, are highly variable spatially.An ideal and more complete assessment would be to obtain sufficient sample sizes to capturethe regional variations.

China and the U.S. are two special cases (Tables 5 and 6) in the validation because of theavailability of wall-to-wall land cover mapping with 30-meter Landsat data in the twocountries. Perceptively, the all-pixel comparisons should be statistically more rigorous thansampling approaches for the same mapping areas. However, any gain in statistical precisionmay be discounted by the facts that the fine-resolution land cover maps have their ownmapping errors4, and that the two countries probably represent the best case scenarios in themapping with ample ancillary information and researcher familiarities. On the other hand,none of the three reference data sets were developed for the mapping effort, resulting in amismatch with the FAO legend, despite the use of “cross-walk” to make these reference datasets as comparable as possible with the FAO legend. The legend mismatches should beincluded in consideration of overall quality of the FRA2000 map.

Figure 3 is a RGB (red, green, blue) color composite image for the conterminous U.S., withthe FRA2000 forest canopy map displayed as red and MRLC-derived forest canopy mapdisplayed as green and blue. The MRLC forest canopy map was derived by recoding the threeforested classes (see Appendix 1 for MRLC classes) to forest and the rest of the classes tononforest, and then aggregating 30-meter pixels to corresponding 1 km pixels. Areas ofagreement between the two maps are shown in shades of gray (from black to white dependingon canopy density value estimated). Areas of disagreement are shown in colors of red(canopy density estimates of the FRA2000 map are greater than that of the MRLC map) andcyan (vise versa). The eastern U.S. woody wetlands (which should have been included in theMRLC map as forested land cover) and the western chaparral (classified by MRLC as 4 Accuracy assessment for either map product is not complete at this time.

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shrubland, but considered forested by the U.S. Forest Service) accounted for most of theFRA2000 overestimation (red shades). Conversely, the FRA2000 underestimation (cyan)occurs primarily in three situations: 1) the Great Basin (Nevada and Utah) dry woodland andshrubland, 2) patchy or riparian woodland in the Midwest agriculture region, and 3)southeastern U.S. fragmented forest lands. While the U.S. MRLC is a special case, thedifferences in Figure 3 probably represent typical errors or variations in the FRA2000 globalforest canopy density map.

Table 5. Area estimates (rounded to thousand km2) and rate of forest cover for China and theconterminous U.S. FAO estimates are derived from 1-km AVHRR data, CAS and MRLC areaestimates are derived from 30-meter Landsat data.

China ConterminousU.S.

Forest areaestimates (x 1,000

km2) FAO CAS FAO MRLCTotal mapped 9,474 9,496 7,943 8,080Total forest 1,770 1,766 2,591 2,480% forest cover 18.68 18.60 32.62 30.70

Table 6. Estimates comparing the FAO map to reference maps of China (CAS) and theconterminous U.S. (MRLC). Overall agreement for forest/nonforest classification is 86.12 and83.01 percent, respectively for China and the U.S.

China USAForest/non forestclassification Producer’s User’s Producer’s User’sTotal forest 62.89 62.72 75.36 73.11Total nonforest 91.44 91.50 86.69 87.99

Certainly, the ultimate judgement of a map’s overall quality lies with its usefulness andeffectiveness in meeting requirements of a variety of applications. User feedback can be anopportunity to examine potential areas of map error, whereas sensitivity of applications usingthe map as input may be a more objective indicator of the map’s accuracy. However, theseholistic approaches are long term and difficult to measure in absolute numbers; traditionalaccuracy assessment is still necessary to provide a more immediate assessment to bothproducer and user. In this study, the use of existing reference data sets for accuracyassessment may not be the ideal approach, but it was a practical decision to obtain anaccuracy estimation given time and budget constraints.

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5 SummaryMapping global land or forest cover, even for a limited number of classes, is a difficultendeavor, as spectral data do not follow a single set of rules for pre-defined classes andproblems associated with atmosphere and view/illumination geometry in large areascomplicate matters still further. In addition, fine-scale land cover conditions, such as canopydensity or forest fragmentation, often dictate spectral response to the sensor of coarse spatialresolution. For these problems, which are unique to large-area land cover mapping, thisproject relied on geographic stratification, mixture modeling, and other measures employedby other similar studies (e.g., Cihlar et al. 1996, Loveland et al. 1999, Malingreau et al. 1995,Roy et al. 1997, Zhu and Evans, 1994). The two global-scale maps and results of thevalidation effort produced for the FAO FRA2000 Program showed that this integratedapproach worked reasonably well for the objectives of this study.

The FAO FRA2000 forest cover map, as validated by using the IGBP global validation dataset, has an overall accuracy at 77 percent (standard error 2.4 percent) for the first four classes.Using the same validation data set, the forest and nonforest classification of the global forestcover map has an overall accuracy of 86 percent with a standard error of 2 percent. Therelatively low per-class accuracy for open or fragmented forest may be partially attributed tothe mixture model’s difficulty to faithfully and consistently map low canopy cover densitymixed with large amounts of other land cover “background” such as bright soil andagriculture. On the other hand, the incompatibility of the existing reference information usedin the validation probably had an adverse effect on accuracy estimates for the map. Thereality that assessing accuracy for any large-area land cover map is necessary but costly led tothe compromise in this study between cost and precision. To provide a sense of overallquality of the mapping effort to users, suitable existing reference data were used for threegeographic areas: global, China, and the U.S. As a result, our validation is only relevant atthese levels and reliability at scales finer than sub-continental is unknown.

Experiences learned from this project indicate that several key factors were responsible forthe overall quality and accuracy of the global product; these factors include 1) knowledge andexperience of analysts and availability of reference information, 2) quality of input AVHRRimagery, and 3) the flexible use of compositing, stratification, scaling, and modeling rules toaccount for data and vegetation variations. The modified spectral mixture analysis wasnecessary to produce an estimated percent forest canopy cover data set, based on which thefirst two classes of the FAO forest cover map were summarized. The other three classes wererefined and adopted from the USGS global land cover database even though there is a 2-3year difference of dates of input data. For the definitions of the three FAO classes and at theglobal scale, it may be safely rationalized that any changes between the three classes can beinsignificant.

While the global forest cover map was developed primarily for use by the FAO FRA2000program, there are potentially other uses for both digital maps. Particularly for global changestudies, the two maps represent the distribution and conditions of large forest cover areas inthe world as of 1995. The maps may also be valuable inputs into other climatic, ecological,and forest models that require large-area forest cover data. As an update to the USGS globalland cover characterization database, the forest cover mapping effort enriches the diversecontent of the database and makes it more useful to a wider range of applications.

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Improvements in satellite sensor and imagery will be potentially the most importantcontribution to future global forest cover mapping. Accuracy, content, and timelines of globalforest cover mapping can be significantly improved by using EOS (Earth Observation Systemof the National Aeronautical and Space Administration) era sensors such as MODIS andLandsat 7 that have better atmospheric, radiometric, and geometric qualities. Programs suchas GOFC (Global Observation of Forest Cover) have been designed to take advantage of newsensors and their much improved mapping capabilities.

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proposal and implementation plans. IGBP-DIS Working Paper No. 13, IGBP-DIS Office, Toulouse, France, 61 pp.

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Eidenshink, J.E. & Faundeen J.L., 1994. The 1 km AVHRR global land data set: first stages in implementation. International Journal of Remote Sensing, 15(17): 3443-3462.

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Hansen, M.C., Defries, R.S., Townshend, J.R.G. & Sohlberg, R., 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6&7): 1331-1364.

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Malingreau, J.P., Achard, F., D’Souza, G., Stibig, H.J., D’Souza, J., Estreguil, C. & Eva, H. 1995. The AVHRR for global tropical forest monitoring: the lessons of the TREES project. Remote Sensing Reviews, 12:29-40.

Mayaux, P. & Lambin, E.F. 1995. Estimation of tropical forest area from coarse spatial resolution data: a two-step correction function for proportional errors due to spatial aggregation. Remote Sensing of Environment, 53:1-15.

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Steinwand, D.R. 1994. Mapping raster imagery to the Interrupted Goode Homolosine projection. International Journal of Remote Sensing, 15(17): 3463-3471.

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Figure 1. Illustration of the overall model for forestcanopy cover estimation. The solid lines showapproximate boundaries of three techniques separatedby their relative positions along the infrared band inthe red-infrared spectral space. Three endmembers(A, B, C) in the endmember unmixing technique arediscussed in the text. Dotted lines illustrate percentforest canopy cover isolines.

A

End-memberunmixing

100

0

AVHRR band 1 (red) reflectance

AV

HR

Rba

nd2

(infr

ared

)

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Figure 2. Global forest cover map produced for the Food and AgricultureOrganization’s Forest resources Assessment 2000 Program.

Figure 3. Comparison of FRA2000 forest canopy density (displayed in red in the RGB composite)with MRLC derived forest canopy density (displayed in green and blue in the RGB composite) in theconterminous U.S. The MRLC map was derived by recoding the three forested classes (see Appendix1 for MRLC classes) to forest and the rest of the classes to nonforest, and then aggregating 30-meterpixels to corresponding 1 km pixels. Areas of agreement by the two maps are in shades from black towhite depending on estimated canopy density. Areas of red show the FRA2000 map as more forestedand the MRLC map as less forested, whereas shades of cyan indicate the MRLC map as more forestedand the FRA2000 map as less forested.

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Appendix 1: Look-up table for establishing correspondencesbetween three existing reference data sets and the FAOFRA2000 global land cover map legend

FAO IGBP China CAS U.S. MRLCClosed forest 1. Evergreen needleleaf

forest5

2. Evergreen broadleafforest

3. Deciduous needleleafforest

4. Deciduous broadleafforest

5. Mixed forest

1. Forest (natural or man-made)

41. Deciduous forest42. Evergreen forest43. Mixed forest91. Woody wetlands

Open orfragmentedforest

8. Woody savanna6 3. Open forest or woodland

Otherwooded land

6. Closed shrubland 2. Shrub4. Other wooded land

51. Shrubland

Other landcover

7. Open shrubland9. Savanna10. Grassland11. Wetland12. Cropland13. Urban14. Cropland / natural

vegetation mosaic15. Barren16. Permanent ice and

snow.

Classes 5-30 (barren, urbanand rural settlements,agriculture, grassland classes)

12. Perennial ice/snow21. Low intensity residential22. High intensity residential23. Commercial/industrial/transportation31. Bare rock/sand/clay32. Quarries/strip mine/ gravelpits33. Transitional61. Orchard/vineyard/other71. Grassland/herbaceous81. Pasture/hay82. Row crops83. Small grains84. Fallow85. Urban/recreational grass92. Emergent herbaceouswetlands

5 All five IGBP forest classes are defined as having a canopy cover greater than 60% (see Belward, 1996).6 Forest canopy cover for this class is between 30-60%.

FRA Working Papers0. How to write a FRA Working Paper (10 pp. – E)1. FRA 2000 Terms and Definitions (18 pp. - E/F/S/P)2. FRA 2000 Guidelines for assessments in tropical and sub-tropical countries (43 pp. - E/F/S/P)3. The status of the forest resources assessment in the South-Asian sub-region and the country capacity building needs.

Proceedings of the GCP/RAS/162/JPN regional workshop held in Dehradun, India, 8-12 June 1998 (186 pp. - E)4. Volume/Biomass Special Study: georeferenced forest volume data for Latin America (93 pp. - E)5. Volume/Biomass Special Study: georeferenced forest volume data for Asia and Tropical Oceania (102 pp. - E)6. Country Maps for the Forestry Department website (21 pp. - E)7. Forest Resources Information System (FORIS) – Concepts and Status Report (20 pp. E)8. Remote Sensing and Forest Monitoring in FRA 2000 and beyond (22 pp. - E)9. Volume/Biomass special Study: Georeferenced Forest Volume Data for Tropical Africa (97 pp. – E)10. Memorias del Taller sobre el Programa de Evaluación de los Recursos Forestales en once Países Latinoamericanos

(194 pp. - S)11. Non-wood forest Products study for Mexico, Cuba and South America (draft for comments) (82 pp. – E)12. Annotated bibliography on Forest cover change – Nepal (59 pp. – E)13. Annotated bibliography on Forest cover change – Guatemala (66 pp. – E)14. Forest Resources of Bhutan - Country Report (80 pp. – E)15. Forest Resources of Bangladesh – Country Report (93 pp. – E)16. Forest Resources of Nepal – Country Report (78 pp. - E)17. Forest Resources of Sri Lanka – Country Report (77 pp. – E)18. Forest plantation resource in developing countries (75 pp. – E)19. Global forest cover map (14 pp. – E)20. A concept and strategy for ecological zoning for the global FRA 2000 (23 pp. – E)21. Planning and information needs assessment for forest fires component (32 pp. – E)22. Evaluación de los productos forestales no madereros en América Central (102 pp. – S)23. Forest resources documentation, archiving and research for the Global FRA 2000 (77 pp. – E)24. Maintenance of Country Texts on the FAO Forestry Department Website (25 pp. – E)25. Field documentation of forest cover changes for the Global FRA 2000 (40 pp. – E)26. FRA 2000 Global Ecological Zones Mapping Workshop Report Cambridge, 28-30 July 1999 (53 pp. –E)27. Tropical Deforestation Literature:Geographical and Historical Patterns in the Availability of Information and the

Analysis of Causes (17 pp. – E)28. Global Forest Survey – Concept Paper (30 pp. - E)29. Forest cover mapping and monitoring with NOAA-AVHRR and other coarse spatial resolution sensors (42 pp. - E)30. Web Page Editorial Guidelines (22 pp. – E)31. Assessing state & change in Global Forest Cover: 2000 and beyond (15 pp. – E)32. Rationale & methodology for Global Forest Survey (60 pp. – E)33. On definitions of forest and forest change (13 pp.- E)34. Bibliografía comentada. Cambios en la cobertura forestal: Nicaragua (51 pp. – S)35. Bibliografía comentada. Cambios en la cobertura forestal: México (35 pp. – S)36. Bibliografía comentada. Cambios en la cobertura forestal: Costa Rica (55 pp. – S)37. Bibliografía comentada. Cambios en la cobertura forestal: El Salvador (35 pp. – S)38. Bibliografia comentada. Cambios en la cobertura forestal: Ecuador (47 pp. – S)39. Bibliografia comentada. Cambios en la cobertura forestal: Venezuela (32 pp. – S)40. Annotated bibliography. Forest Cover Change: Belize (36 pp. – E)41. Bibliografia comentada. Cambios en la cobertura forestal: Panamà (32 pp. – S)42. Proceedings of the FAO Expert consultation to review the FRA 2000 methodology for Regional and Global Forest

Change Assessment (54 pp. – E)43 Bibliografia comentada. Cambios en la cobertura forestal: Colombia (32 pp. – S)44 Bibliografia comentada. Cambios en la cobertura forestal: Honduras (42 pp. – S)45 Proceedings of the South-Asian Regional Workshop on Planning, Database & Networking for Sustainable Forest

Management (254 pp. – E)46 Under preparation47 Proceedings of the Regional workshop on forestry information services, Stellenbosch, South Africa, 12-17 february

200148 Under preparation49 Under preparation

Please send a message to fra@fao.org for electronic copies or download fromwww.fao.org/forestry/fo/fra/index.jsp (under Publications)