Forestry Department Food and Agriculture Organization of the United Nations FRA 2000 GLOBAL FOREST COVER MAPPING FINAL REPORT.

Transcription

1 Forestry Department Food and Agriculture Organization of the United Nations FRA 2000 GLOBAL FOREST COVER MAPPING FINAL REPORT Rome, 2001 Forest Resources Assessment Programme Working Paper 50 Rome 2001

2 The Forest Resources Assessment Programme Forests are crucial for the well-being of humanity. They provide foundations for life on earth through ecological functions, by regulating the climate and water resources, and by serving as habitats for plants and animals. Forests also furnish a wide range of essential goods such as wood, food, fodder and medicines, in addition to opportunities for recreation, spiritual renewal and other services. Today, forests are under pressure from expanding human populations, which frequently leads to the conversion or degradation of forests into unsustainable forms of land use. When forests are lost or severely degraded, their capacity to function as regulators of the environment is also lost, increasing flood and erosion hazards, reducing soil fertility, and contributing to the loss of plant and animal life. As a result, the sustainable provision of goods and services from forests is jeopardized. FAO, at the request of the member nations and the world community, regularly monitors the world s forests through the Forest Resources Assessment Programme. The next report, the Global Forest Resources Assessment 2000 (FRA 2000), will review the forest situation by the end of the millennium. FRA 2000 will include country-level information based on existing forest inventory data, regional investigations of land-cover change processes, and a number of global studies focusing on the interaction between people and forests. The FRA 2000 report will be made public and distributed on the World Wide Web in the year 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 address: fra@fao.org DISCLAIMER The 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 authoritative information sources they do not reflect the official position of FAO and should not be used for official purposes. Please refer to the FAO forestry website ( for access to official information. The FRA Working Paper Series provides an important forum for the rapid release of preliminary FRA 2000 findings needed for validation and to facilitate the final development of an official quality-controlled FRA 2000 information set. Should users find any errors in the documents or have comments for improving their quality they should contact either Robert Davis or Peter Holmgren at fra@fao.org.

3 Table of Contents 1 INTRODUCTION REQUIREMENTS FOR A GLOBAL FOREST COVER MAP REVIEW OF LARGE-AREA LAND/FOREST COVER MAPPING OBJECTIVES METHODOLOGY TEMPORAL IMAGE COMPOSITING ESTIMATING PERCENT FOREST COVER WITH STRATIFICATION ADAPTING THE USGS SEASONAL LAND COVER CLASSES TO THE FAO CLASSIFICATION VALIDATION A PRAGMATIC PLAN RESULTS AND DISCUSSION SUMMARY...21 REFERENCES...23 APPENDIX 1: LOOK-UP TABLE FOR ESTABLISHING CORRESPONDENCES BETWEEN THREE EXISTING REFERENCE DATA SETS AND THE FAO FRA2000 GLOBAL LAND COVER MAP LEGEND...28 FRA WORKING PAPERS...29 Paper drafted by: Editorial production by: Zhiliang Zhu and Eric Waller, EROS Data Center Patrizia Pugliese, FAO, FRA Programme 3

4 Abbreviations AVHRR BEF BV CAS EDC EOS EZ FAO FORIS FRA GIS GOFC IGBP IR Advanced Very High Resolution Radiometer Biomass Expansion Factor Biomass of inventoried volume Chinese Academy of Sciences Eros Data Centre Earth Observation System Ecological Zone Food and Agricultural Organization of the United Nations Forest Resources Information System Forest Resources Assessment Geographic Information System Global Observation of Forest Cover International Geosphere and Biosphere Programme Infra Red Landsat MSS MRLC NDVI NGO RGB TM TREES USGS Landsat Multi-Spectral Scanner Multi-Resolution Land Characterization Normalized Difference of Vegetation Index Non-governmental organization Red, green, blue Thematic Mapper Tropical Ecosystem Environment observations by Satellites; joint project of the commission of the European Community U.S. Geological Survey 4

5 ABSTRACT Many countries periodically produce national reports on the state and change of forest resources, using statistical surveys and spatial mapping of remotely sensed data. At the global level, the Food and Agriculture Organization (FAO) of the United Nations has conducted a Forest Resources Assessment (FRA) program every 10 years since 1980, producing statistics and analysis that give a global synopsis of forest resources in the world. For the year 2000 of the FRA program (FRA2000), a global forest cover map was produced to provide spatial context to the extensive survey. The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The first two forested classes at the global scale were delineated using combinations of temporal compositing, modified mixture analysis, geographic stratification, and other classification techniques. The remaining three FAO classes were derived primarily from the USGS global land cover characteristics database (Loveland et al. 1999). Validated on the basis of existing reference data sets, the map is estimated to be 77 percent accurate for the first four classes (no reference data were available for water), and 86 percent accurate for the forest and nonforest classification. The final map will be published as an insert to the FAO FRA2000 report, and the map data are available through the EDC web page. 5

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7 1 Introduction 1.1 Requirements for a global forest cover map To reverse the trend of global climate change caused by greenhouse gases, the United Nations Framework Convention and the Kyoto Protocol have stipulated that it is necessary to take immediate policy actions to reduce the emissions. Some of important emission-cutting actions are within the forestry sector, such as sustainable forest management, forest ecosystem health, and woody plantations, which require consistent and periodic inventory and reporting to verify their status, progress, and credibility. The Food and Agriculture Organization (FAO) of the United Nations (UN) conducts a periodic global Forest Resources Assessment (FRA) program (FAO 1995). The basis and rationale of the program are provided through the UN Conference on Environment and Development held in Rio de Janeiro in June 1992, which required the observation and assessment of global forest resources as a key element in Agenda 21, the conference documentation. The FAO program has been conducted for 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 a tally and report of the most current forest inventory from each country, change analysis based on a statistical sample of Landsat image interpretations, and other modeling and analysis techniques (FAO, 1995). In addition to these components, FRA2000 was also required to produce a global forest cover map showing spatial distribution of the world's existing forests for this survey. Expert panels convened by FRA in the early stages of the program (Nyssönen and Ahti, 1996, Päivinen and Gyde, 1997) noted that a new remote sensing-based global forest cover map should be produced using concurrent satellite data to represent the geographic distribution of the forest resources surveyed under FRA2000. A number of largearea land/forest cover mapping products (discussed below) were presented and their applications were discussed. However, it was apparent that none of them met concurrent needs of FRA2000 in terms of scope (global), theme (forest cover and forest density), and timing (data from mid 1990's). These existing products demonstrated the feasibility of a FAO global forest cover mapping effort for use by FRA2000 and its constituents. Thematically, the proposed FRA2000 forest cover legend was not complex only five land cover classes were required (Table 1): closed forest, open or fragmented forest, other wooded land, other land cover, and water. This map legend is a simplified version of a more comprehensive FRA classification system used for the ground survey and reporting components in the previous and the current surveys (FAO, 1995). 7

8 Table 1. FAO FRA 2000 global land cover map legend, definitions, and representative land cover types. FRA2000 FAO Definition Representative land cover Class Closed forest Open or fragmented forest Other wooded land Land covered by trees with a canopy cover of more than 40 percent and height exceeding 5 meters. Includes natural forests and forest plantations. Land covered by trees with a canopy cover between 10 and 40 percent and height exceeding 5 meters (open forest), or mosaics of forest and non-forest land (fragmented forest). Includes natural forests and forest plantations. Land either with a 5-10 percent canopy cover of trees exceeding 5 meters height, or with a shrub or bush cover of more than 10 percent and height less than 5 meters. All other land, including grassland, agricultural land, barren land, urban areas. Tropical/subtropical moist forest Temperate broadleaf mixed forest Subtropical/temperate conifer plantation Boreal conifer forest Northern boreal/taiga open conifer or mixed forest Southern Africa woodland Tropical fragmented/degraded forest Mediterranean closed shrubland Tropical woody savanna Grassland, cropland, non-woody Other land cover wetland, desert, urban Water Inland water Inland water This legend was developed considering several factors, including source data, time and scope requirements, previous project experience, and FRA forest cover needs. In the 1990 s, owing to its coarse resolution and daily repeat cycle, the Advanced Very High Resolution Radiometer (AVHRR) sensor was perhaps the only choice for a global-scale land cover mapping effort. Constraints of the coarse resolution, plus the requirement for a quick 2-3 year delivery time for the global scope, necessitated that the classification legend should be relatively simple. Mapping forest types, in addition to canopy density, may be desirable, but at the global level, consensus and consistency are lacking as to what and how many forest types 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 forest resources exploration and conservation has led to an increased emphasis in the last two decades on land cover and forest cover mapping programs. Maturing technologies in spaceborne sensor, computer, and image processing techniques are another reason for the recently increased use of remote sensing for monitoring and assessment of forest resources (Loveland et al. 1999). While many mapping programs were intended for specific locations and in response 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 science and policy requirements (Tucker and Townshend, 2000). In the 1980 s and 1990 s, a number of studies demonstrated the feasibility and effectiveness of large-area, wall-to-wall land cover or forest cover mapping using coarse or medium resolution satellite data such as AVHRR and Landsat MSS (Multi-Spectral Scanner). Studies of Africa land cover (Tucker et al. 1985), Canadian land cover (Cihlar et al. 1996), European 8

9 land cover (Mücher et al. 2000) and the U.S. land cover (Loveland et al. 1991) relied on spectral and seasonal properties of land cover from AVHRR imagery for classification of general land cover patterns. Large-area forestry applications, conducted using a variety of techniques, include Amazon deforestation monitoring (Skole and Tucker, 1993), the TREES tropical 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). At the global scale, Hansen et al. (2000) classified land cover at 1 km resolution using 41 metrics as input data into a classification tree. The U.S. Geological Survey (USGS) EROS Data 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 cover complexity and land use history, the numbers of land cover classes in the database, derived primarily from their seasonal characteristics, ranged from 137 for Australia to 255 for Eurasia. The seasonal land cover classes were further summarized to a 17-class legend defined by the International Geosphere and Biosphere Programme (IGBP) (Belward, 1996). The IGBP classification legend includes five forested classes based on leaf type and longevity: evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest 1. In addition to coarse or medium resolution satellite data, 30-meter Landsat image data have been increasingly used to produce wall-towall 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 prohibitively expensive 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 be logical 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 optimize forest canopy, no direct relationship exists to permit a simple conversion of the seasonal forest cover type classes to the FAO forest canopy classes. The closed versus open forest requirement 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 entirely appropriate for an one-to-one conversion to FAO classes. A new approach was required for the 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. 9

10 2 Objectives The goal of this project was to produce a validated global forest cover map for use by the FAO Forest Resources Assessment 2000 Program. Actual forest cover, as of mid-1990's, was to be mapped with AVHRR 1-km data to indicate the geographic distribution and conditions of 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 s AVHRR 1-km image data. Derive the five-class FRA2000 forest cover map using the forest canopy density data set and 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). 10

11 3 Methodology Mapping global forest cover, even for only a limited number of classes, is a large undertaking with many challenges that are unique to the scale of the effort (Cihlar 2000). For a given class, many different forest types and physiognomy in various geographic regions of the world give rise to great variations in their spectral or seasonal properties. This requires extensive studies of regional forest distribution and the use of flexible stratification in the mapping. For a mapping area as large as a continent or the world, land cover conditions and patterns in many regions are unfamiliar to the mapping analysts. There are problems with persistent clouds, atmospheric contamination, sensor viewing angle, and solar illumination, leading to varied data quality in global image data sets. Under these circumstances, which are inherent and unique to the scale and pixel sizes of large-area mapping, many large-area land cover mapping efforts have relied on the use of temporal composite data, stratification, unsupervised classification, and interpreter skills to compensate for these scale-dependent factors. The USGS global land cover characterization project also adopted these measures, as well as the use of a flexible land cover description on the basis of vegetation seasonality as measured by a temporal series of AVHRR NDVI (normalized difference of vegetation index). While the USGS global land cover database does not differentiate open from closed forest lands, descriptions in the database for the other three required land cover types under the 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 first two classes), clearly suggested that there was a need for a flexible methodology that allowed certain interactive flexibility in deriving and correcting for the FAO classes. The methodology used for the FAO global forest cover mapping consisted of temporal compositing, estimating percent forest cover, linking percent forest cover to the USGS global land cover classes, and validation. 3.1 Temporal image compositing Source data used for the FAO forest cover mapping were drawn from AVHRR NDVI composites produced for February 1995-January 1996, the latest data year available when the project began. The temporal composites, consisting of five calibrated AVHRR bands and a NDVI band, were initially computed at EDC using the protocol of maximum NDVI value (to minimize the amount of clouds in imagery) over 10-day compositing periods (Eidenshink and Faundeen 1994). Bands 1 and 2 were further corrected for atmospheric ozone and Rayleigh scattering effects. For this study, bands 1 and 2 (red and infrared) and NDVI of the 10-day composites were processed into monthly composites (three 10-day composites for each monthly 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 NDVI algorithm 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 spectral bands. Indeed, visual examination of the source data showed that large backscatter view angles were associated with raised red and IR reflectance in forestland, leading to confusion with vigorous agriculture and added noise in open or fragmented forest regions. As a partial measure to correct for the bi-directional effect, the minimum red band-compositing rule used 11

12 for this study was found to better preserve pixels near nadir or in fore-scatter direction and maintain the integrity of patterns of forest lands. There are also geometric concerns associated with the fore-scatter view angles selected by the minimum red compositing, but these 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 (red and infrared) and NDVI band for each of five major land masses: Africa, Australia and tropical Asia 2, Eurasia (Europe and temperate/subtropical Asia), North America, and South America. Lack of good quality image data prevented several important tropical island chains in the Pacific, including Hawaii, Micronesia, and Polynesia, from being mapped. For each major land mass, initial image data preparation and subsequent mapping were conducted using an equal-area Interrupted Goode Homolosine projection (Steinwand 1994). Finished map products were re-projected to two map projections: Lambert equal-area projection for all continental 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 surface components (Smith et al., 1990, Wessman et al. 1996), or endmembers, such as green vegetation, soil, and shade. Mixture analysis provides a means to estimate amount of endmembers within target pixels, and results serve as one additional layer of information about land cover (Zhu and Evans, 1994). It is generally understood that, in relatively small study areas and with sufficient spectral information, unique and representative endmembers can be identified to produce reasonable results. However, spectral mixture analysis is nevertheless important to large-area land cover mapping conducted with coarse-resolution satellite imagery as the primary data set, since coarse pixels (e.g. 1-km) are more likely than fine resolution (e.g. 30-m) to contain mixed land cover types. Several recent large-area land cover studies approached the problem of quantifying mixed pixels with different algorithms. For instance, Defries et al (2000) used a multivariate, multitemporal regression model to estimate 8-km global land cover mixtures. For the tropical forest TREES project, a spatial regression estimator was used to improve coarse-resolution area estimates of deforestation on the basis of a fine-resolution sample (Mayaux and Lambin 1995). These algorithms reduced the need for a time-consuming refinement process in classification (Cihlar 2000) and proved the key element in achieving overall objectives of these studies. For the objective of estimating fraction of forest cover in 1-km pixels, we adopted a methodology that combined linear mixture modeling with scaling of NDVI and the visible band based on pixel positions along the infrared band (Figure 1). Development of this methodology resulted from our observations that it would be impractical to obtain a sufficiently large sample of high-resolution reference data set in the global framework required to run a TM-guided approach. Moreover, a simple use of the mixture analysis usually would not result in satisfactory results for forest cover, particularly over a large mapping 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 factors such 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. 12

13 and solar illumination problems as well as open woodland with varying soil background would complicate the analysis even further. These issues necessitated the use of geographic stratification and flexible modeling. In the combined model, pixels were modeled depending on their relative positions along the infrared 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 could be best scaled with their NDVI values because NDVI was considered insensitive to illumination variance (Holben et al. 1986). It may be noted in the red-ir space that, for green vegetation, the distribution of NDVI values varies between the diagonal line (0 value) and the boundary near Y-axis (NDVI as 1). It was found that NDVI values varied between 0.3 and 0.8 from open woodland to closed forest in the low IR range. Scaling was flexibly set between different forest cover types. Pixels with high IR reflectance were treated in a two-band unmixing method, using the three endmembers, as shown in Figure 1: A as forest (usually healthy deciduous forest), B as agriculture (bright green), and C as non-vegetated land. Linear combinations of the three land cover 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 green vegetation, which represented conifer or mixed forests, fragmented forest (forest mixed with agriculture), open woodland, shrubland and grassland, a linear scaling of AVHRR red band was applied. Previous studies have shown that, with careful applications of stratification to both land cover and physiography, forest cover density was highly correlated to red band reflectance in this spectral range (Yang and Prince, 1997). Even with the three methods in the mixture analysis model, significant regional variations in climate, topography, and forest types dictated that geographic stratification be used to ensure that the same canopy definitions be mapped across varying regional conditions. For example, dry miombo woodland in southern Africa would display different spectral properties from that of fragmented tropical moist forest in the Congo Basin, even if they had a similar range of canopy openness. Geographic stratification allowed treating regional variations separately by resetting threshold or endmember values flexibly to fit with vegetation conditions being mapped. Geographic stratification for each continent was based on digitized lines following combinations of ecoregions, physiography, vegetation types, and imagery conditions. The three methods in the combined model for forest canopy mapping were applied to each monthly AVHRR composite. To provide the least atmospherically affected results, final percent forest cover for a continent was determined over the course of the year (February 1995-January 1996) on the basis of maximum monthly forest cover value achieved, regardless of the method chosen. The maximum forest compositing was compared to average forest canopy cover over the course of the year; areas of high ratio were examined to prevent overestimating from anomalous data. The two FAO forest classes: closed forest and open or fragmented forest were then derived on the basis of FAO definitions: percent and percent. 13

14 3.3 Adapting the USGS seasonal land cover classes to the FAO classification After the first two FAO classes (closed forest, open or fragmented forest) were mapped using the mixture analysis model described above, the existing USGS global land cover database was 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 database were built on characteristics of vegetation seasonality, productivity and leaf types determined in terms of 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 length of the temporal period helped separate successively decreasing vegetation primary production, ranging from forested land to open woodland, shrub and grass, and sparse land cover (Reed et al. 1994). Additionally, seasonal variations were investigated to partially support identification of agricultural land uses (e.g., single versus multiple cropping). At the most 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 cover complexity and land use history. To derive the three FAO classes, the USGS seasonal land cover classes were used as the baseline data on a continent-by-continent basis. The refinement methods to fit USGS classes to 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 careful study of all available evidence. Loveland et al. (1999) describes the overall approach in detail. The other wooded land class consisted primarily of open or closed shrub land cover in tropical and subtropical regions as well as low density tree cover in northern boreal zones near 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 data sets, such as ecoregions and digital elevation models. Spectral reclustering, as well as userdefined polygon splits, was also used. In areas of disagreement, the initially defined first two FAO classes took priority. The three FAO classes were merged with the first two forest classes for each continent, and all continents were combined into a single global forest cover map. 3.4 Validation a pragmatic plan Assessing accuracy for results of large-area land cover mapping is a necessary yet difficult task largely because of potentially high costs in terms of both resources committed and time. Moreover, as the mapping area is large and land cover in coarse-resolution pixels is highly variable, requirements on sampling and data collection may be more stringent than small, less heterogeneous mapping areas. As Cihlar (2000) noted, these constraints limit accuracy assessment for large-area land cover to a matter of compromise between what is desired and what is available. For this study, an independent, global-scale, sampling-based reference data set was not available. Instead, the validation plan was centered around a highresolution interpreted data set used for validating the IGBP global land cover, and two national land cover maps: China and the U.S. 14

15 The IGBP validation data set (Scepan, 1999) contained 312 interpreted sample sites from a randomly selected Landsat-5 Thematic Mapper (TM) sample, acquired in early 1990 s and stratified by the IGBP land cover classes (Belward, 1996). The sample sites were 1 square kilometer in size in the Landsat scenes, and were interpreted by a team of regional land cover experts (Scepan, 1999). The Landsat sample was stratified by class for 16 of the 17-class IGBP legend; water was not sampled. To provide a knowledge of accuracy for the FAO global forest cover map, the IGBP validation data set was re-coded from the 16-class IGBP legend to the first 4 FAO classes, and then to forest or nonforest (Appendix 1). This re-coded data set was overlaid on the global FAO map to derive a set of accuracy statistics including overall accuracy, and user s and producer s accuracy (Stehman and Czaplewski, 1998) for the first four classes in the FAO legend. The accuracy assessment was conducted for each continent rather than landmasses used as separate mapping areas, since the IGBP validation data set was coded for continents. Two special cases in the overall validation effort were the two existing, wall-to-wall, land cover maps produced with 30-meter Landsat-5 TM data acquired in the early 1990 s. For China, the Chinese Academy of Sciences (CAS) developed a wall-to-wall land cover classification 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 in Appendix 1. Because of the issue of mismatch between the existing data sets and the full FAO map legend (4 classes, excluding water), we derived accuracy numbers based on only forest/nonforest reclassification. The China and U.S. land cover maps were resampled to 1 square 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 and producer s, were derived from the overlay. All three reference data sets had different mapping techniques, and their reclassifications to the 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 at different levels (global vs. China and U.S.). It may be noted that, while other land cover maps existed, only the two data sets from China and U.S were used. This is because 1) the data sets covered sufficiently large areas, 2) dates of image data were close (early 90 s), and 3) primary image data were the 30-meter Landsat, following the convention of using finer resolution data to validate products from coarser source data (e.g., Kloditz et al. 1998). 15

16 4 Results and discussion The 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 the 1990 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 unique data set was to provide a quantification of forested land in various regions of the world, and to help define closed forest from open or fragmented forest, and open or fragmented forest from non-forested land. This global map may potentially be useful to other applications. As a quantified output, the data set may be used in large-scale (continental or global) climate models to test for different forest cover scenarios, or in other large-area biogeochemical, ecological, conservation models that require both quantitative and categorical forest cover descriptions. This data set, however, is not validated and is not required for the FRA2000 program. The global forest cover map will be published as a poster map, in a global Robinson projection, by the FAO FRA2000 program to illustrate distribution and conditions of global forest resources surveyed under the 10-year program. The digital data set will be made available, similar to other USGS large-area land cover products, on the EDC webpage 3. The forest cover data set is a new land cover layer in the USGS global land cover characteristics database and complements other thematic layers in the database in holistically describing the global land cover. Area estimates are derived by classes and by major landmasses (Table 2). Although Antarctica was not contained in the input data, it is included in the table and on the global map as other land cover (barren, ice and snow). Affecting the area estimates in a relatively small 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 the lack of quality image data consistent with the rest of the world. Compared to the missing islands, a few other factors inherent to the AVHRR source data probably exerted far greater impact on precision of the area estimates. These factors include image quality problems such as atmospheric contamination and view/illumination geometry, misclassification errors, issues related to spatial scale including resolution and area distortion. It is important to recognize 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 local significance

17 Table 2. Area estimates (rounded to thousands square kilometers except for global percent) of FAO 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), North America (NA), and South America (SA). The area estimates are derived from continental Lambert equal-area projections. FRA2000 Classes Global Global percent AF AP EA NA SA Closed forest 28, ,774 1,919 10,800 7,149 6,231 Open or fragmented forest 15, , ,442 2,329 2,879 Other wooded land 11, , , ,197 Other land cover 89, ,267 7,244 31,779 14,031 5,194 Water 3, , This 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 characteristics of broad regional forests in the world, rather than from any remote sensing studies. There exists a certain correspondence between forest canopy cover and satellite image data, as shown in this mapping exercise. However, such correlation between satellite spectral data and percent forest cover is not unique and variations are great in different geographic regions and vegetation types. Forest lands of different canopy cover are mapped based firstly on ecological knowledge of these forests. For example, northern boreal forests in subpolar zones or parts of the miombo woodland in Africa are known to have canopy densities below 40 percent. The use of models and geographic stratification only provided refinements to the canopy openness. Our strategy generally assumes that forests tend to occupy a specific region in spectral space and that regionally specific compositing and modeling will ensure the separation of forest and non-forest. Unfortunately, even with an optimal strategy for enhancing discrimination, there are unavoidable ambiguities; these range from spectral ambiguities in land cover, to ambiguities resulting from atmospheric, illumination, and view angle effects. The former can include land cover that is physically similar to forest but doesn t meet the forest height definition (e.g. Mediterranean shrubland and heathland of Scotland). Other land cover may appear similar to forest due to a mixed pixel effect. Non-woody wetlands, and other vegetation/water mixes, vegetation/dark soil mixes, and vegetation/burn mixes can have the spectral appearance of forest. Similarly, cloud shadow, low sun angles, and topography are factors that can cause non-forest vegetation to look forested. However, most atmospheric and view angle effects have the tendency to make forests brighter, and thus more like non-forests; these include atmospheric effects (haze or subpixel clouds) that are especially prevalent in the tropics. Even year long compositing can fail to remove some of these problems, especially over some cloud forests. Similarly, backscatter viewing geometry can make forests look unforested, but compositing reduces these problems. Extreme cases, such as eastern North America, can be ameliorated with stratification, although view angle-specific corrections might be preferred. As noted before, the global IGBP validation data set (primarily TM interpretation sites) was used as an independent reference data set to assess the accuracy of the global FAO forest cover map. To accomplish this goal, it was necessary to approximate the FAO classes with 17

18 the IGBP legend. Table 3 shows the results of the accuracy assessment. The accuracy and associated standard error estimates were derived based on simple random sampling (Stehman and Czaplewski, 1998). Table 3. The error matrix, user s and producer s percent accuracy estimates using the IGBP global validation data set. The IGBP legend was re-labeled to approximate the FAO legend (the first four classes). The overall accuracy and standard error estimates are 76.92% and 0.24%, respectively. IGBP Global Validation Dataset FAO legend Row total User's Standard error Column total Producer's Standard error Not surprisingly, FAO classes 2 and 3 (open or fragmented forest, other wooded land) have low accuracy than class 1 (closed forest) and class 4 (other land cover). While closed forest and other land cover (e.g., cropland, grassland) are relatively easy to define, the exact boundaries in terms of canopy cover between open or fragmented forest and either the closed forest or other land cover are spectrally highly variable. Particularly at the lower forest canopy cover, the amount of open ground with bright soil tends to contribute to misclassification between open or fragmented forest and other land cover such as barren or shifting agriculture. Similarly for the other wooded land (class 3, primarily woody savanna and 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 of incompatibility between the IGBP legend (basis of the IGBP global validation data set) and the FAO legend (see Appendix 1). For example, IGBP class 14 (cropland and natural vegetation mosaic) was re-coded to other land cover (FAO class 4), even though the original class clearly represented the situation where patchy forest land or shrub land could be present. 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 also calculated 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 the continents varied between 13 (Australia and New Zealand) and 82 (North America). This variation largely reflected the diversity of IGBP land cover in the original random stratified sampling method (Scepan, 1999). Because of limited sample sizes for the continents, perclass accuracy and standard error estimates would be imprecise, and therefore were not calculated. Only global and continental overall accuracy estimates are given in Table 4. 18

19 Table 4. Sample size and overall accuracy of the first four FAO classes and forest/nonforest aggregations for global and six mapped continents: Africa (AF), Australia and New Zealand (AU), Asia (AS), Europe (EU), North America (NA), and South America (SA). Global AF AU AS EU NA SA Sample size Classes Standard error Forest/nonforest Standard error Notwithstanding the factor of limited sample sizes, accuracy estimates at the continental level clearly show that land cover homogeneity is an important factor in determining map accuracy. For forest and nonforest land cover classification, the FRA2000 map is most accurate in South America, largely due to the continuous, unbroken tropical moist forest in Amazon Basin; and it is least accurate in Africa, as Congo Basin rain forest is smaller than that of South America, and the large areas of miombo woodland in southern Africa are spatially and temporally highly variable. Reasonable accuracy results in the northern hemisphere perhaps reflect the fact that land cover is relatively established and knowledge of land cover in the regions can be easily obtained. For Australia and tropical Asia, the high accuracy may be the result of the small sample size (13 sample sites). Of course, regional land 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 capture the regional variations. China and the U.S. are two special cases (Tables 5 and 6) in the validation because of the availability of wall-to-wall land cover mapping with 30-meter Landsat data in the two countries. Perceptively, the all-pixel comparisons should be statistically more rigorous than sampling approaches for the same mapping areas. However, any gain in statistical precision may be discounted by the facts that the fine-resolution land cover maps have their own mapping errors 4, and that the two countries probably represent the best case scenarios in the mapping 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 a mismatch with the FAO legend, despite the use of cross-walk to make these reference data sets as comparable as possible with the FAO legend. The legend mismatches should be included 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., with the FRA2000 forest canopy map displayed as red and MRLC-derived forest canopy map displayed as green and blue. The MRLC forest canopy map was derived by recoding the three forested classes (see Appendix 1 for MRLC classes) to forest and the rest of the classes to nonforest, and then aggregating 30-meter pixels to corresponding 1 km pixels. Areas of agreement between the two maps are shown in shades of gray (from black to white depending on 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) and cyan (vise versa). The eastern U.S. woody wetlands (which should have been included in the MRLC 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. 19

20 shrubland, but considered forested by the U.S. Forest Service) accounted for most of the FRA2000 overestimation (red shades). Conversely, the FRA2000 underestimation (cyan) occurs primarily in three situations: 1) the Great Basin (Nevada and Utah) dry woodland and shrubland, 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, the differences in Figure 3 probably represent typical errors or variations in the FRA2000 global forest canopy density map. Table 5. Area estimates (rounded to thousand km 2 ) and rate of forest cover for China and the conterminous U.S. FAO estimates are derived from 1-km AVHRR data, CAS and MRLC area estimates are derived from 30-meter Landsat data. Forest area Conterminous China estimates (x 1,000 U.S. km 2 ) FAO CAS FAO MRLC Total mapped 9,474 9,496 7,943 8,080 Total forest 1,770 1,766 2,591 2,480 % forest cover Table 6. Estimates comparing the FAO map to reference maps of China (CAS) and the conterminous U.S. (MRLC). Overall agreement for forest/nonforest classification is and percent, respectively for China and the U.S. Forest/non forest China USA classification Producer s User s Producer s User s Total forest Total nonforest Certainly, the ultimate judgement of a map s overall quality lies with its usefulness and effectiveness in meeting requirements of a variety of applications. User feedback can be an opportunity to examine potential areas of map error, whereas sensitivity of applications using the map as input may be a more objective indicator of the map s accuracy. However, these holistic approaches are long term and difficult to measure in absolute numbers; traditional accuracy assessment is still necessary to provide a more immediate assessment to both producer and user. In this study, the use of existing reference data sets for accuracy assessment may not be the ideal approach, but it was a practical decision to obtain an accuracy estimation given time and budget constraints. 20

21 5 Summary Mapping global land or forest cover, even for a limited number of classes, is a difficult endeavor, as spectral data do not follow a single set of rules for pre-defined classes and problems associated with atmosphere and view/illumination geometry in large areas complicate matters still further. In addition, fine-scale land cover conditions, such as canopy density or forest fragmentation, often dictate spectral response to the sensor of coarse spatial resolution. For these problems, which are unique to large-area land cover mapping, this project relied on geographic stratification, mixture modeling, and other measures employed by 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 the validation effort produced for the FAO FRA2000 Program showed that this integrated approach worked reasonably well for the objectives of this study. The FAO FRA2000 forest cover map, as validated by using the IGBP global validation data set, 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 forest cover map has an overall accuracy of 86 percent with a standard error of 2 percent. The relatively low per-class accuracy for open or fragmented forest may be partially attributed to the mixture model s difficulty to faithfully and consistently map low canopy cover density mixed with large amounts of other land cover background such as bright soil and agriculture. On the other hand, the incompatibility of the existing reference information used in the validation probably had an adverse effect on accuracy estimates for the map. The reality that assessing accuracy for any large-area land cover map is necessary but costly led to the compromise in this study between cost and precision. To provide a sense of overall quality of the mapping effort to users, suitable existing reference data were used for three geographic areas: global, China, and the U.S. As a result, our validation is only relevant at these levels and reliability at scales finer than sub-continental is unknown. Experiences learned from this project indicate that several key factors were responsible for the overall quality and accuracy of the global product; these factors include 1) knowledge and experience of analysts and availability of reference information, 2) quality of input AVHRR imagery, and 3) the flexible use of compositing, stratification, scaling, and modeling rules to account for data and vegetation variations. The modified spectral mixture analysis was necessary to produce an estimated percent forest canopy cover data set, based on which the first two classes of the FAO forest cover map were summarized. The other three classes were refined and adopted from the USGS global land cover database even though there is a 2-3 year difference of dates of input data. For the definitions of the three FAO classes and at the global scale, it may be safely rationalized that any changes between the three classes can be insignificant. While the global forest cover map was developed primarily for use by the FAO FRA2000 program, there are potentially other uses for both digital maps. Particularly for global change studies, the two maps represent the distribution and conditions of large forest cover areas in the world as of 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 global land cover characterization database, the forest cover mapping effort enriches the diverse content of the database and makes it more useful to a wider range of applications. 21

22 Improvements in satellite sensor and imagery will be potentially the most important contribution to future global forest cover mapping. Accuracy, content, and timelines of global forest cover mapping can be significantly improved by using EOS (Earth Observation System of the National Aeronautical and Space Administration) era sensors such as MODIS and Landsat 7 that have better atmospheric, radiometric, and geometric qualities. Programs such as GOFC (Global Observation of Forest Cover) have been designed to take advantage of new sensors and their much improved mapping capabilities. 22

23 References Belward, A.S. (editor), The IGBP-DIS global 1 km land cover data set (DISCover): proposal and implementation plans. IGBP-DIS Working Paper No. 13, IGBP-DIS Office, Toulouse, France, 61 pp. Cihlar, J., Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing, 21(6&7): Cihlar, J., Manak, D. & Voisin, N., AVHRR bi-directional reflectance effects and compositing. Remote Sensing of Environment, 48: Cihlar, J., Ly, H. & Xiao, Q., Land cover classification with AVHRR multichannel composites in northern environments. Remote Sensing of Environment, 58: DeFries, R.S., Hansen, M. & Townshend, J.R.G., Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. International Journal of Remote Sensing, 21: Eidenshink, J.E. & Faundeen J.L., The 1 km AVHRR global land data set: first stages in implementation. International Journal of Remote Sensing, 15(17): FAO Forest resources assessment 1990 global synthesis. FAO Forestry Paper 124, Rome, Italy. 46 pp. Hansen, M.C., Defries, R.S., Townshend, J.R.G. & Sohlberg, R., Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6&7): Holben, B., Kimes, D. & Fraser, R.S., Directional reflectance response in red and near-ir bands for three cover types and varying atmospheric conditions. Remote Sensing of Environment, 19: Klöditz, C., van Boxtel, A., Carfagna, E. & van Deursen, W., Estimating the accuracy of coarse scale classification using high scale information. Photogrammetric Engineering and Remote Sensing, 64(2): Liu, J., Buheaosier Study on spatial-temporal features of modern land-use change in China: using remote Sensing techniques. Quaternary Sciences (Chinese language), 20(3): Loveland, T.R., Merchant, J.W., Ohlen, D.O. & Brown, J.F Development of a landcover characteristics database for the conterminous U.S. Photogrammetric Engineering and Remote Sensing, 57: Loveland, T.R., Zhu, Z., Ohlen, D.O., Brown, J.F., Reed, B.C. & Yang, L., An analysis of the global land cover characterization process. Photogrammetric Engineering and Remote Sensing, 65(9):

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25 Steinwand, D.R Mapping raster imagery to the Interrupted Goode Homolosine projection. International Journal of Remote Sensing, 15(17): Tucker, C.J., Townshend, J.R.G. & Goff, T.E., African land-cover classification using satellite data. Science, 227: Tucker, C.J. & Townshend, J.R.G Strategies for monitoring tropical deforestation using satellite data. International Journal of Remote Sensing, 21(6&7): Vogelmann, J.E., Sohl, T.L., Campbell, P.E. & Shaw, D.M., Regional land cover characterization using Landsat Thematic Mapper data and ancillary data sources. Environmental Monitoring and Assessment, 51: Waller, E. & Zhu, Z, Estimating subpixel forest cover with 1-km satellite data. Proceedings of the Pecora 14 Conference, Demonstrating the Value of Satellite Imagery, Denver, CO, pp Wessman, C.A., Bateson, C.A. & Benning, T.L Detecting fire and grazing patterns in tallgrass prairie using spectral mixture analysis. Ecological Applications, 7(2): Wilbanks, T.J. & Kates, R.W Global change in local places: how scale matters. Climate Change, 43: Yang, J. & Prince S.D A theoretical assessment of the relation between woody canopy cover and red reflectance. Remote Sensing of Environment, 59(3): Zhu, Z. & Evans, D.L., U.S. forest type groups and predicted percent forest cover from AVHRR data. Photogrammetric Engineering and Remote Sensing, 60(5):

26 AVHRR band 2 (infrared) A 100 End-member unmixing 0 AVHRR band 1 (red) reflectance Figure 1. Illustration of the overall model for forest canopy cover estimation. The solid lines show approximate boundaries of three techniques separated by their relative positions along the infrared band in the red-infrared spectral space. Three endmembers (A, B, C) in the endmember unmixing technique are discussed in the text. Dotted lines illustrate percent forest canopy cover isolines. 26

27 Figure 2. Global forest cover map produced for the Food and Agriculture Organization 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 the conterminous U.S. The MRLC map was derived by recoding the three forested classes (see Appendix 1 for MRLC classes) to forest and the rest of the classes to nonforest, and then aggregating 30-meter pixels to corresponding 1 km pixels. Areas of agreement by the two maps are in shades from black to white depending on estimated canopy density. Areas of red show the FRA2000 map as more forested and the MRLC map as less forested, whereas shades of cyan indicate the MRLC map as more forested and the FRA2000 map as less forested. 27