Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data

To facilitate estimation of the carbon sink associated with tropical forests in Cameroon, regenerating and mature forests were mapped using an unsupervised classification of AVHRR channels 1, 2 and 3. Stages of regeneration were defined using nonlinear relationships between AVHRR channel 3 radiance and basal area, estimated using data collected from 183 plots (1 ha in size) in an area south-east of the capital, Yaounde. The overall extent and patterns of regenerating forest were comparable to those generated in previous studies. Older stages of regeneration could not, however, be discriminated adequately from mature forest, suggesting that areas of tropical forest disturbance may be underestimated when mapped using AVHRR data. closed tropical forests were regenerating and that their rate of expansion million ha y 1. These regenerating forests accumulate biomass more rapidly     

Spatial and temporal patterns of gap dominance by low-canopy lianas detected using EO-1 Hyperion and Landsat Thematic Mapper

Woody lianas are critical to tropical forest dynamics because of their strong influence on forest regeneration, disturbance ecology, and biodiversity. Recent studies synthesizing plot data from the tropics indicate that lianas are increasing in both abundance and importance in tropical forests. Moreover, lianas exhibit competitive advantages over trees in elevated CO₂ environments and under strong seasonal droughts, suggesting that lianas may be poised to increase not only in abundance but also in spatial distribution in response to changing climate. We used a combination of high-resolution color-infrared videography and hyperspectral imagery from EO-1 Hyperion to map low-lying lianas in Noel Kempff Mercado National Park (NKMNP) in the Bolivian Amazon. Evergreen liana forests comprise as much as 14% of the NKMNP landscape, and low-stature liana patches occupy 1.5% of these forests. We used change vector analysis (CVA) of dry season Landsat TM and ETM+ imagery from 1986 and 2000 to determine changes in liana-dominated patches over time and to assess whether those patches were regenerating to canopy forest. The spatial distribution of liana patches showed that patches were spatially aggregated and were preferentially located in proximity to waterways. The CVA results showed that most of the dense liana patches increased in brightness and greenness and decreased in wetness over the 14 years of the change analysis, while non-liana forest patches changed less and in more random directions. Persistent liana patches increased in area by an average of 59% over the time period. In comparison, large burned areas appeared to recover completely to canopy forest in the same time period. This suggests that the dense liana patches of NKMNP represent an alternative successional pathway characterized not by tree regeneration but rather by a stalled state of low-canopy liana dominance. This research supports hypotheses that liana forests can be a persistent rather than transitional component of tropical forests, and may remain so due to competitive advantages that lianas enjoy under changing climatic conditions.     

An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration

It has been postulated that tropical forests regenerating after deforestation constitute an unmeasured terrestrial sink of atmospheric carbon, and that the strength of this sink is a function of regeneration stage. Such regeneration stages can be characterized by biophysical properties, such as leaf and wood biomass, which influence the radiance emitted and/or reflected from the forest canopy. Remotely sensed data can therefore be used to estimate these biophysical properties and thereby determine the forest regenerative stage. Studies conducted on temperate forests have related biophysical properties successfully with red and near-infrared radiance, particularly within the Normalized Difference Vegetation Index (NDVI). However, only weak correlations have generally been observed for tropical forests and it is suggested here that the relationship between forest biophysical properties and middle and thermal infrared radiance may be stronger than that between those properties and visible and near-infrared radiance.

An assessment of Landsat Thematic Mapper (TM) data revealed that radiance acquired in middle and thermal infrared wavebands contained significant information for the detection of regeneration stages in Amazonian tropical forests. It was demonstrated that tropical forest regeneration stages were most separable using middle infrared and thermal infrared wavebands and that the correlation with regeneration stage was stronger with middle infrared, thermal infrared or combinations of these wavebands than they were with visible, near infrared or combinations of these wavebands. For example, correlation coefficients increased from — 0·26 (insignificant at 95 per cent confidence level) when using the NDVI, to up to 0·93 (significant at 99 per cent confidence level) for a vegetation index containing data acquired in the middle and thermal infrared wavebands. These results point to the value of using data acquired in middle and thermal infrared wavebands for the study of tropical forests.     

Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data

The rapid environmental changes occurring in the Brazilian Amazon due to widespread deforestation have attracted the attention of the scientific community for several decades. A topic of particular interest involves the assessment of the combined impacts of selective logging and forest fires. Forest disturbances by selective logging and forest fires may vary in scale, from local to global changes, mostly related to the increase of carbon dioxide released into the atmosphere. Selective logging activities and forest fires have been reported by several studies as important agents of land-use and land-cover changes. Previous studies have focused on selective logging, but forest fires on a large scale in tropical regions have yet to be properly addressed. This study involved a more comprehensive investigation of temporal and basin-wide changes of forest disturbances by selective logging and forest fires using remotely sensed data acquired in 1992, 1996, and 1999. Landsat imagery and remote-sensing techniques for detecting burned forests and estimating forest canopy cover were applied. We also conducted rigorous ground measurements and observations to validate remote-sensing techniques and to assess canopy-cover impacts by selective logging and forest fires in three different states in the Brazilian Amazon. The results of this study showed a substantial increase in total forested areas impacted by selective logging and forest fires from approximately 11,800 to 35,600 km² in 1992 and 1999, respectively. Selective logging was responsible for 60.4% of this forest disturbance in the studied period. Approximately 33% and 7% of forest disturbances detected in the same period were due to impacts of forest fires only and selective logging and forest fires combined, respectively. Most of the degraded forests (～90%) were detected in the states of Mato Grosso and Pará. Our estimates indicated that approximately 5467, 7618, and 17437 km² were new areas of selective logging and/or forest fires in 1992, 1996, and 1999, respectively. Protected areas seemed to be very effective in constraining these types of forest degradation. Approximately 2.4% and 1.3% of the total detected selectively logged and burned forests, respectively, were geographically located within protected areas. We observed, however, an increasing trend for these anthropogenic activities to occur within the limits of protected areas from 1992 to 1999. Although forest fires impacted the least area of tropical forests in the study region, new areas of burned forests detected in 1996 and 1999 were responsible for the greatest impact on canopy cover, with an estimated canopy loss of 18.8% when compared to undisturbed forests. Selective logging and forest fires combined impacted even more those forest canopies, with an estimated canopy loss of 27.5%. Selectively logged forest only showed the least impact on canopy cover, with an estimated canopy loss of 5%. Finally, we observed that forest canopy cover impacted by selective logging activities can recover faster (up to 3 years) from impact when compared to those forests disturbed by fires (up to 5 years) in the Amazon region.     

AVHRR for monitoring global tropical deforestation

Abstract

Advanced Very High Resolution Radiometer (AVHRR) data have been used to assess the dynamics of forest transformations in three parts of the tropical belt. A large portion of the Amazon Basin has been systematically covered by Local Area Coverage (LAC) data in the 1985-1987 period. The analysis of the vegetation index and thermal data led to the identification and measurement of large areas of active deforestation. The Kalimantan/Borneo forest fires were monitored and their impact was evaluated using the Global Area Coverage (GAC) 4 km resolution data. Finally, High Resolution Picture Transmission (HRPT) data have provided preliminary information on current activities taking place at the boundary between the savanna and the forest in the Southern part of West Africa. The AVHRR approach is found to be a highly valuable means for carrying out deforestation assessments in regional and global perspectives.     

JERS-1/SAR backscatter and its relationship with biomass of regenerating forests

It has been hypothesized that regenerating tropical forests are large atmospheric carbon sinks. Accurate estimates of the location, extent and biomass of regenerating tropical forests are needed in order to quantify their contribution to global carbon budgets. Synthetic Aperture Radar (SAR) data are independent of near-constant tropical cloud cover and have proved useful for locating and mapping the extent of regenerating tropical forests. To estimate the biomass of regenerating tropical forests we need to determine the nature and strength of the relationship between radar backscatter and biomass for different types of regenerating forest. To further investigate this, two extreme forms of regenerating forest were considered; they were block-logged (clear-cut) forest in the Tapajós area of Pará State, Brazil and selectively-logged forest in Southern Cameroon. Biomass was estimated alometrically for 15 plots in Tapajós and 34 plots in Cameroon and was related to L-band backscatter derived from the JERS-1 SAR. The relationship between backscatter and biomass was strong for the Tapajós study area and weak for the Cameroonian study area. It was concluded that there is potential for the use of JERS-1/SAR to locate, map and estimate biomass for young regenerating forests following block-logging rather than selective-logging.     

Selective logging changes forest phenology in the Brazilian Amazon: Evidence from MODIS image time series analysis

We present a large-scale study of the relationships between selective logging and forest phenology in the Brazilian Amazon. Time-series analysis of MODIS satellite data of selectively logged forests in Mato Grosso, Brazil, shows that relatively low levels (5-10%) of canopy damage cause significant and long-lasting (more than 3 years) changes in forest phenology. Partial clearing slows forest green-up in the dry season, progressively dries the canopy, and induces overall seasonal deficits in canopy moisture and greenness. Given large and increasing geographic extent of selective logging throughout Amazonia, this phenological disturbance has a potential to impact carbon and water fluxes, nutrient dynamics, and other functional processes in these forests.     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Mapping of Siberian forest landscapes along the Yenisey transect with AVHRR

In this paper NOAA AVHRR data acquired at the Sukachev Institute of Forest in Siberia, Russia is evaluated for forest management mapping applications. First a classification of the entire 1000km 2 3000km transect was performed, but was found to be too general to be of value. More useful interpretation procedures require a landscape-ecological approach. This means that computer classification should be made separately for segments of territory based ecologically distinct regions. This segmentation of the transect into ecological regions was found to improve the level of detail available in the classification. Using this approach AVHRR data were found to be adequate for small scale mapping at the level of vegetation types or plant formations. A limited study using AVHRR data for classification of mountainous regions showed that AVHRR-derived maps were more detailed than existing landscape maps. AVHRR derived classifications also compared favourably to larger scale forest management maps of softwood and hardwood forests. Current forest management in Siberia relies on very small-scale inventory maps. Thus, there is a potential role for AVHRR (or Terra) data for northern Siberian forest monitoring. The southern forests of the Yenisey meridian (below the 57th parallel ) are less uniform due to considerable human activity, and NOAA/AVHRR data will play a subordinate role in its monitoring.     

Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis

We combined a detailed field study of forest canopy damage with calibrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) reflectance data and texture analysis to assess the sensitivity of basic broadband optical remote sensing to selective logging in Amazonia. Our field study encompassed measurements of ground damage and canopy gap fractions along a chronosequence of postharvest regrowth of 0.5–3.5 years. We found that canopy damage and regrowth rates varied according to the logging method used, either conventional logging or reduced impact logging. Areas used to stage felled trees prior to transport, log decks, had the largest gap fractions immediately following cutting. Log decks were quickly colonized by early successional plant species, resulting in significant gap fraction decreases within 1.5 years after site abandonment. Although log decks were the most obvious damage areas on the ground and in satellite imagery, they accounted for only 1–2% of the total harvested area of the blocks studied. Other forest damage features such as tree-fall gaps, skid trails, and roads were difficult to recognize in Landsat reflectance data or through textural analysis. These landscape features could be only crudely resolved in the most intensively logged forests and within about 0.5 years following harvest. We found that forest damage within any of the landscape strata (decks, roads, skids, tree falls) could not be resolved with Landsat reflectance or texture data when the canopy gap fraction was <50%. The basic Landsat ETM+ imagery lacks the resolution of forest structural features required for quantitative studies of logging damage. Landsat textural analyses may be useful for broad delineation of logged forests, but detailed ecological and biogeochemical studies will probably need to rely on other remote sensing approaches. Until spatial gradients of canopy damage and regrowth resulting from selective logging operations in tropical forests in the Amazon region are resolved, the impacts of this land use on a continental scale will remain poorly understood.     

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI_af) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004.     

Forest regeneration on abandoned clearances in central Amazonia

A time-series of Landsat and SPOT sensor data was used to approximate the age of tropical forests regenerating on abandoned agricultural land north of Manaus, Brazil, and to estimate the period of active land use prior to abandonment. Based on field data, two distinct regeneration pathways, dominated by the pioneer genera Cecropia and Vismia, respectively, were described, with the former regenerating on the least intensively used sites. Forests of mixed species composition and lacking numerical dominance by pioneer species were also observed. Transformed Divergence Analysis of Landsat Thematic Mapper (TM) radiance data revealed that forests of varying age and following different regeneration pathways were best discriminated using mid infrared (1.55-1.74 w m) wavelengths. As rates of carbon sequestration by forests vary with age and regeneration pathway, the potential exists for refining spatial estimates of the carbon balance of tropical forests regenerating on abandoned agricultural lands.     

Global land cover classification at 1 km spatial resolution using a classification tree approach

This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.     

Observations on the relationship between SIR-C radar backscatter and the biomass of regenerating tropical forests

SIR-C SAR data were related to the above ground biomass of regenerating tropical forests in Amazonia, Brazil. C- and L- band SAR data in the conventional polarization configurations showed no significant relationship with forest biomass, which were estimated in the field to range from 63.8-141.1 tha -1. However, the strength of the relationships was increased through the use of backscatter ratios and stratification of the forests by dominant species. These results support the view that backscatter ratios enhance the relationship between radar backscatter and biomass, perhaps beyond some quoted radar saturation levels, by reducing the effect of differences due to forest type. They also demonstrate that an ability to differentiate between forests of different species composition, and canopy geometry, increases the strength of the relationship between the SAR backscatter and biomass.     

Subpixel forest cover in central Africa from multisensor, multitemporal data

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions.     

Exploring the utility of NOAA AVHRR middle infrared reflectance to monitor the impacts of ENSO-induced drought stress on Sabah rainforests

The ever-wet tropics are under threat from ENSO events and there is a need for a monitoring system to analyse and describe their responses to such events. This letter explores the relative value of using NOAA AVHRR middle infrared (MIR) reflectance data and NDVI data for the monitoring of ENSO-related drought stress of a tropical forest ecosystem in Sabah, Malaysia. Relationships between rainfall and MIR reflectance were examined. Correlation coefficients are generally large and significant (at 0.1 level) while those between rainfall and NDVI were small and insignificant. This letter concludes that there is potential in using MIR reflectance for monitoring the effects of ENSO-induced drought stress on these forests and this has a bearing on how NOAA AVHRR data may be used to further our knowledge on the impacts of ENSO events on tropical forest environments.     

Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models

In this paper, we present a methodology to map classes of degraded forest in the Eastern Amazon. Forest degradation field data, available in the literature, and 1-m resolution IKONOS image were linked with fraction images (vegetation, nonphotosynthetic vegetation (NPV), soil and shade) derived from spectral mixture models applied to a Satellite Pour L'observation de la Terre (SPOT) 4 multispectral image. The forest degradation map was produced in two steps. First, we investigated the relationship between ground (i.e., field and IKONOS data) and satellite scales by analyzing statistics and performing visual analyses of the field classes in terms of fraction values. This procedure allowed us to define four classes of forest at the SPOT 4 image scale, which included: intact forest; logged forest (recent and older logged forests in the field); degraded forest (heavily burned, heavily logged and burned forests in the field); and regeneration (old heavily logged and old heavily burned forest in the field). Next, we used a decision tree classifier (DTC) to define a set of rules to separate the forest classes using the fraction images. We classified 35% of the forest area (2097.3 km²) as intact forest. Logged forest accounted for 56% of the forest area and 9% of the forest area was classified as degraded forest. The resultant forest degradation map showed good agreement (86% overall accuracy) with areas of degraded forest visually interpreted from two IKONOS images. In addition, high correlation (R²=0.97) was observed between the total live aboveground biomass of degraded forest classes (defined at the field scale) and the NPV fraction image. The NPV fraction also improved our ability to mapping of old selectively logged forests.     

A technique for extrapolating and validating forest cover across large regions Calibrating AVHRR data with TM data

Abstract

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High Resolution Radiometer (AVHRR) pixels covering the same location. Regression analysis was used to develop an empirical relationship between AVHRR spectral signatures and forest cover. The regression equation developed from data from the single county calibration area in southern Illinois was then applied to the entire AVHRR scene, which covered all or parts of ten states, to produce a regional map of forest cover. This map was used to derive estimates of forest cover, within a geographical information system (GIS), for each of the 428 counties located within the boundaries of the original AVHRR scene. The validity of the overall regional map was tested by comparing the AVHRR/TM-derived estimates of county forest cover with independent estimates of county forest cover developed by the U.S. Forest Service (USFS). The overall correlation coefficient of the AVHRR/TM and USFS county forest cover estimates was r=0-89 (n=428 counties). Not surpris0ingly, some individual states and the areas nearer to the southern Illinois calibration centre had higher correlation coefficients. Absolute estimates of forest cover percentages were also significantly well predicted. With the future inclusion of multiple calibration centres representing a number of physiographic regions, the method shows promise for predicting continental and global estimates of forest cover.