Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data

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1 Remote Sensing of Environment 83 (2002) Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data M.C. Hansen a, *, R.S. DeFries a,b, J.R.G. Townshend a,c, R. Sohlberg a, C. Dimiceli a, M. Carroll a a Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, MD 20742, USA b Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA c Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA Received 1 May 2001; received in revised form 21 February 2002; accepted 12 March 2002 Abstract The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multitemporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover. D 2002 Elsevier Science Inc. All rights reserved. 1. Introduction * Corresponding author. Tel.: address: mhansen@geog.umd.edu (M.C. Hansen). Tree cover mapping has grown in importance as the need to quantify global tree stocks has increased. Tree cover is an important variable for modeling of global biogeochemical cycles and climate (Sellers et al., 1997; Townshend et al., 1994). Additionally, tree cover mapping has taken on increased importance in the policy arena. Quantifying carbon stocks has been deemed a necessity in global treaties regarding release and sequestration of carbon to and from the atmosphere (IGBP, 1998). The use of tree cover mapping in assessing the condition of global ecosystems is also important (Ayensu, Claasen, Collins, et al., 1999). In order to meet the needs of the users of such data, the remote sensing community has begun to promote the benefits of the synoptic, standardized view provided by satellite data (DeFries, Hansen, Townshend, Janetos, & Loveland, 2000). One of the annual Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products is the vegetation continuous fields layers. The layers include percent bare ground, herbaceous and tree cover and, for tree cover, percent evergreen, deciduous, needleleaf and broadleaf. These maps have the potential to meet many of the needs of both the scientific and policy communities. This paper describes an improved methodology for deriving percent tree cover estimates over previous methodologies. The procedure is presented along with a global Advanced Very High Resolution Radiometer (AVHRR) application and two examples using MODIS data. Continuous fields of vegetation properties offer advantages over traditional discrete classifications. By depicting each pixel as a percent coverage, areas of heterogeneity are better represented. Discrete classes do not allow for the depiction of variability for spatially complex areas (DeFries, Field, Fung, et al., 1995). Many spatially complex areas occur because of anthropogenic land cover change. By using proportional estimates, sub-pixel cover can be mapped with the prospect of measuring change over time. Since the /02/$ - see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S (02)

2 304 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 1. Flow chart of major steps in generation of global continuous field of tree cover products for (a) prototype methodology of DeFries et al. (2000) and (b) MODIS implementation. scale of human-induced land cover change is typically finer than 250-m (Townshend & Justice, 1988), continuous fields from MODIS data may yield a usable land cover change product. 2. Procedure The approach presented in this paper for mapping continuous fields of tree cover differs from that of the initial prototype (DeFries et al., 2000). Fig. 1 outlines the prototype methodology and the improved technique presented here. The two approaches share one feature: the use of annual phenological metrics as the independent variables to predict tree cover. They differ in the following ways: n the new technique is fully automated n the new training data set is a continuous variable, not discrete class labels n the new algorithm is a regression tree as opposed to a linear mixture model modified by a land cover classification n the new approach operates globally, without per continent adjustments of the mixture model. The most important advancement is the automation of the algorithm. The prototype approach relied on a classification methodology which was partially dependent on an expert interpreter s input (Hansen, DeFries, Townshend, & Sohlberg, 2000). This step has been eliminated in the new technique. The main parts integral to the methodology are described in the following sections Annual metrics Global multi-temporal metrics capture the salient points of phenological variation by calculating annual means, maxima, minima and amplitudes of spectral information. The value of metric generation versus using a series of monthly values is that the metrics are not sensitive to time of year or the seasonal cycle and can limit the inclusion of atmospheric contamination. Fig. 2 shows monthly values for red reflectance from AVHRR data for February 1995 to January 1996 for the Amazon basin. Use of any individual month would include cloud contamination whereas the annual minimum provides a cleaner metric for viewing land cover. Fig. 3 shows another example of the utility of metrics from Central Africa. Here, the maximum annual Normalized Fig. 2. Derived minimum annual red reflectance from monthly composites of red reflectance associated with maximum monthly NDVI for (a) January 1996, (b) February 1995, (c) March 1995, (d) April 1995, (e) May 1995, (f) June 1995, (g) July 1995, (h) August 1995, (i) September 1995, (j) October 1995, (k) November 1995, (l) December (m) is derived metric. All 13 subsets have the same image enhancement applied.

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4 306 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 3. (a) AVHRR metrics for area in central Africa: red = maximum annual NDVI, cyan = minimum annual red reflectance; (b) AVHRR metric of mean temperature of the four warmest months from band 5; (c) continuous tree cover result; (d) high-resolution imagery, false color composite for an area in the Democratic Republic of the Congo; (e) classified high-resolution imagery: green = forest (80% canopy cover), dark maroon = woodland (50% canopy cover), light maroon = parkland (25% canopy cover), yellow = no trees (0% canopy cover); (f) derived training data by aggregating classified image to 500-m pixels.

5 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Difference Vegetation Index (NDVI) is shown with the minimum annual red reflectance metric. Minimum annual red reflectance is negatively correlated with tree cover as the combined effects of chlorophyll absorption and canopy shadowing make denser tree cover darker. Maximum annual NDVI, on the other hand, has a positive correlation with tree cover as increasing leaf area of canopies makes forests appear greener. However, for this area, woodlands of approximately 60% cover are indistinguishable from denser forests for these metrics. Another metric based on surface temperature allows for the stratification of these two areas using the regression tree. The four warmest months of the year based on surface temperature correlate with the dry season as the seasonal woodlands have senesced and evapotranspiration is lower: this allows for a clean delineation of the forest/woodland boundary. These metrics also discriminate the northern edge of the Central African rainforest as they are insensitive to the specific time of year. Metric generation will continue to develop using MODIS data as a full year of consistent data becomes available and the full global suite of metrics can be derived. The metrics to be tested will mimic those for this work shown in Table 1. Each band is ranked individually and also ordered by corresponding greenness and temperature rankings. The individual bands, NDVI and surface temperature are ranked; lowest to highest for visible and infrared bands, highest to lowest for NDVI and surface temperature. From these rankings a set of metrics is derived. The bands are also ordered according to highest and lowest corresponding NDVI and surface temperature values, and metrics are derived based on these orderings. Metrics results such as near-infrared reflectance at maximum annual NDVI, or mean NDVI of the four warmest surface temperature months are used. Table 1 shows metrics for an example using a red reflectance band Continuous training data Past training data were created by classifying and interpreting high-resolution imagery to identify homogeneous areas. These areas were then aggregated to develop a coarse resolution training data set for a discrete classification system, the modified International Geosphere Biosphere Programme s (IGBP) University of Maryland land cover legend (DeFries, Hansen, Townshend, & Sohlberg, 1998; Hansen et al., 2000). The 12 classes in this legend can be aggregated to four tree cover strata. These strata are 0 10%, 11 40%, 41 60% and % tree canopy cover. In the new approach, the high resolution classifications are aggregated to coarser scales by labeling each stratum with a mean cover value (0%, 25%, 50% and 80% for the aforementioned classes) and then averaging over the coarser output cells. In this way a continuous tree cover training data set is created. Fig. 3 shows the approach for deriving the current global training data set for an example from the Democratic Republic of the Congo. Thus, the new approach includes the use of training pixels of intermediate cover, whether they are homogeneous open woodlands or fragmented forest. This is an improvement over spectral end members, which employ only signatures characteristic of pure class types. As prior work was based on identifying core, homogeneous areas for all cover classes, a new training data set had to be assembled. The archival data sets were re-interpreted wall-to-wall, where possible, to acquire training in mixed areas. This allows for a more consistent depiction of transition areas and ecotones which are of interest to many researchers of land cover change. An important effect of the continuous training is the increased ability to automate the procedure. By having the full range of tree cover heterogeneity for training, the algorithm produces more stable results Regression tree algorithm Regression trees have previously been used with remote sensing data (DeFries et al., 1997; Michaelson, Schimel, Friedl, Davis, & Dubayah, 1994; Prince & Steininger, 1999). They offer a robust tool for handling nonlinear relationships within remotely sensed data sets. The algorithm uses a set of independent variables, in this case annual multi-temporal metrics, to recursively split a dependent variable, in this case tree cover, into subsets which maximize the reduction in the residual sum of squares. The algorithm uses only those metrics which best separate the Table 1 This table shows examples of metrics derived for the red reflectance band Ranking criteria: Each band is individually ranked and also ordered based on NDVI and surface temperature rankings Ranking of individual bands Greenest based on NDVI Warmest based on surface temperature Metric types Individual monthly values minimum, median and maximum annual red reflectance red reflectance associated with peak, median, minimum greenness red reflectance associated with peak, median and minimum surface temperature Means mean of four, six and eight darkest red reflectance monthly values mean red reflectance of four, six and eight greenest months mean red reflectance of four, six and eight warmest months Amplitudes amplitude of red reflectance for minimum, median and maximum red values amplitude of red reflectance associated with peak, median, minimum greenness amplitude of red reflectance associated with peak, median, minimum surface temperature The same metrics are calculated for other bands and NDVI. For AVHRR, bands 1 5 were used; for MODIS, bands 1 7 and surface temperature will be used.

6 308 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 4. Example of tree cover mapping methodology. (a) Scatter of km global tree cover training data where the feature space is minimum annual red reflectance on the y-axis and minimum annual near-infrared reflectance on the x-axis with derived NDVI from these two values also used; (b) node partitions and node numbers derived from the pruned regression tree; (c) mean node estimates resulting from the regression tree; (d) per node stepwise regression estimates; (e) per node median adjustment results. In addition to slightly improving the root mean square error estimates, the last two steps in (d) and (e) create a more continuous result and improve depictions in extreme low and high cover nodes. Refer to Fig. 5 to see the actual tree structure. tree strata. In this way, unlike unsupervised classifiers, metrics that provide no discriminatory information are ignored. For example, the individual months of Fig. 2 may not be used at all, since the derived index of minimum red reflectance best depicts tree cover information. All input metrics are analyzed across digital number values and right and left splits are examined. The split that produces the greatest reduction in the residual sum of squares, or deviance, is used to divide the data and the process begins again for the two newly created subsets. The regression tree algorithm takes the following form: D ¼ D s D t D u where s represents the parent node, and t and u are the splits from s. The deviance for nodes is calculated from the equation: D i ¼ X ðy i u ½jŠ Þ 2 casesðjþ for all j cases of y and the mean value of those cases, u. Our implementation of the regression tree algorithm is performed as follows. Two samples of training pixels are taken from the training data set. One is used to grow the regression tree and one to prune it. Pruning is required because tree algorithms are very robust and delineate even

7 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 5. Tree structure from Fig. 4, which employs 1999 minimum red and near-infrared reflectances and derived NDVI for 8-km Pathfinder AVHRR data. Training data are resampled from the high-resolution classifications to the 8-km grid. Ellipses represent nonterminal nodes; rectangles, terminal nodes. Inside nodes are mean tree cover estimates based on 50% sample used to grow tree. Splitting rules are shown under nonterminal nodes. Terminal node numbers match those in Fig. 4b. individual pixels isolated in spectral space. By having a setaside of training data, a more generalized tree can be generated. This generalization is achieved by passing the second sample of data down the initial tree. As the data cascade down the tree, the overall sum of squares begins to level out and eventually begins to increase. This indicates an overfitting of the initial tree. For this work, pruning is performed not where the sum of squares begins to increase, but where additional nodes represent a reduction of less than 0.01% of the overall sum of squares for the data. The end result is an easily interpreted hierarchy of splits, which, when followed, allow for a ready biophysical interpretation of the relationship between vegetation cover and satellite signal. An additional step is the fitting of a linear regression model to the data in each node. The regression tree output yields a mean cover value based on training pixels present in each node. However, the predicted values can be improved by running a linear model using the independent variables to predict tree cover for each node. This is done by using a stepwise regression procedure per node in order to use the combination of image data which best explains tree cover variation. This step represents a fine-tuning of the result to produce a more continuous product and does not greatly change the regression tree results. For example, from Fig. 3, the regression tree might use the temperature metric to separate the forest from the woodlands. Then metrics such as maximum annual NDVI would be used in the stepwise regression phase to improve the mean node estimates. Many nodes at the extremes of tree cover extent have skewed data distributions. While the regression tree yields suitable splits in these instances, the use of the mean value in assigning a cover value may reduce values at the high cover end and increase values for extremely low cover Table 2 Node statistics for example tree in Figs. 4 and 5 Node Training mean Standard deviation Median Number of pixels

8 310 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 6. (a) Percent tree cover map automatically generated using global 1-km AVHRR data from data and (b) subset of preliminary linear endmember mixture model approach for an area of New York state; (c) same area for new approach; (d) preliminary approach for an area in Mato Grosso state, Brazil; (e) same area for new approach.

9 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) nodes. A simple solution to this is to adjust the final node values by adding the median minus the mean for each node. Again, this represents a subtle adjustment to the final product, but experiments with the procedure show that it slightly improves overall root mean square errors and high and low end cover estimates. Fig. 4 shows a graphic representation of the procedure. This example uses actual inputs, but is a simplified illustration to aid understanding of the procedure. Three input metrics, minimum annual red and near infrared reflectances and derived NDVI from 1999 AVHRR data, are used as the independent variables. The training data are from the global training set aggregated to 8-km resolution. The 50% sample used to grow the tree created a 2954 node tree when perfectly fit to the scatter in Fig. 4a. Using the other 50% of data to prune and find the 0.01% cutoff threshold, an 18- node tree is derived as shown in Figs. 4b and 5. The overall mean of the training data is 14.2% tree cover as can be seen in the root node in Fig. 5. Using this estimate for all pixels yields a root mean square error (rmse) of 17.73%. The mean estimates from the 18 nodes reduce the rmse to 3.43%. The next steps of stepwise regression and median adjustment lower this value to 3.35% and 3.31%, respectively. Thus, the most significant predictor is the original pruned tree itself, while the subsequent steps create a more continuous and slightly improved result. The tree structure and associated node statistics are informative since trees allow for meaningful interpretation from a biophysical perspective. The first three splits in the tree use red reflectance, indicating the importance of this metric in tree mapping. The combined effects of chlorophyll absorption and canopy shadowing in the visible red wavelengths are most significant among these variables in discriminating dense tree cover. Node 5 is an example of a low tree cover node which could be associated with burns as it has both very low red reflectance and NDVI. Table 2 shows statistics for each node. Note that the mean node values are slightly different than those of the tree in Fig. 5, because the tree is originally defined using a 50% sample whereas the Table 2 statistics include all pixels. In this table, nodes with great variability represent inseparable signatures. Increasing the feature space by adding metrics might be required in this instance to enhance separability. An arc of increased inseparability is seen across the feature space for nodes 1, 2, 3, 6, 7, 8, 9 and 10. This type of information is useful, especially for change detection studies because it allows for an assignment of confidence which can be employed to measure change. For instance, given two successive time periods and similar tree structures, only pixels which started and ended in the high confidence zones above and below this low confidence arc would be labeled as changed pixels. Only node 6 exhibits a significant degree of skewing. The mean and median are fully 10% apart. This node represents a bimodal distribution which is inseparable and best estimated by adjusting node values using the median. 3. Results 3.1. AVHRR global prototype using MODIS algorithm The initial attempt to use the regression tree was performed using the AVHRR 1-km data set processed at the EROS Data Center under the guidance of the IGBP (Eidenshink & Faudeen, 1994). Metrics describing the phenological variation of vegetation were derived for the year dating February 1995 to January This test employed 144 metrics, many derivative of those used in the land cover classification of Hansen et al. (2000). Table 1 shows an outline of the metrics used. At 1-km resolution, the training data consists of nearly 6 million pixels, and a systematic sampling of roughly every fifth training pixel was taken to drive the analysis. The final product and improved information content in the algorithm can be seen in Fig. 6. A much more detailed, sharper depiction is shown for subsets centered on the Hudson River valley, United States and the upper Xingu River valley, Brazil as compared to the initial methodology. The previous methodology using endmembers in a linear model tends to overestimate forest cover at the high end. This is due to the small dynamic range of dense tree cover ( f >40%) for many metrics, such as the red reflectance metric shown in Fig. 4. The linear model tends to flatten tree cover variability, which is captured in the regression tree approach. The initial regression tree mean cover values for 189,092 pixels yielded an rmse of 9.28 compared to the training data. After applying the regression models to each node, the rmse was reduced to 9.06% tree cover. The final scaling using the median adjustment also resulted in an rmse of 9.06%. Comparison of the training values to results for both methodologies are listed in Table 3. The average rmse values indicate a more robust result across all strata with the new algorithm Conterminous United States 250-m tree cover map from 2000 summer and fall maximum NDVI composites To test the procedure further and to examine the robustness of the MODIS data, a preliminary United States tree Table 3 Comparison of global continuous training pixel values with results from two approaches depicting tree cover, the linear mixture approach of DeFries et al. (2000) and the regression tree approach planned for use with MODIS data Tree cover strata Linear mixture model + classification (%) average rmse overall rmse Regression tree algorithm (%)

10 312 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 7. (a) Continuous tree cover training at 250-m resolution used to create test map. (b) Test product of tree cover for the conterminous United States from two maximum NDVI composites from data between June 10 and July 27, 2000 and between October 7 and October 31, 2000.

11 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) cover map was made using two maximum NDVI composites from available summer and fall data for the year The high-resolution training data resampled to the 250-m MODIS cell size resulted in over 20 million training pixels for the contiguous United States alone. The 250-m training data are shown in Fig. 7. A 1% sample of these sites was randomly taken. The 250-m bands were chosen to be included in the MODIS sensor as Townshend and Justice (1988) found this to be the resolution necessary to depict human-induced land cover change. It is clear from much of the MODIS 250-m raw imagery that this was a useful choice. When viewing raw swaths, many forest clearings and other features associated with human activity are plainly visible. However, when comparing the raw inputs to a maximum NDVI composite, it is clear that a lot of this information is lost. Fig. 8 shows NDVI data from the MODIS 250-m bands. The raw swath has a great amount of detail present, which is lost or blurred in the autumn composited image used to make the country-wide product. Small clearings and water courses in the Congaree bottomland hardwood forest, which appears as the bright fork shape in the center of the images, are plainly visible in the L1B data, but not in the composite. This composite is not an official MODIS product (Huete et al., 2002, this issue), but a simple test to observe the quality of a traditional procedure. It is possible that the blurring is related to geolocation errors or the inclusion of extreme view angle values, which may be easily corrected. However, it is apparent that compositing issues are critical to maximizing the usefulness of MODIS data. In past work, the AVHRR sensor s resolution of 1.1 km did not allow for the depiction of such detail and the effects of compositing, while well-characterized by many, (Cihlar, Manak, & D Iorio, 1994; Holben, 1986; Moody & Strahler, 1994), did not appear to result in such a potential dramatic loss of information. That is because the original resolution and sensor characteristics of the AVHRR captured an image which was too coarse to view many of the features which are visible with MODIS. Compositing is now of increased importance, as blurring of the data can preclude the usefulness of the data in change detection studies North America 500-m tree cover and leaf type products The operational MODIS algorithm was implemented on 4 months of 500-m data (Julian days for 2000 and of 2001) for North America. This is the resolution of the official MODIS continuous cover products. The time periods used capture some seasonality, but are not sufficient temporally to derive useful metrics. A consistently processed year of data for metric generation was not available at the time of this study. However, the results of this preliminary product reveal the robustness of the MODIS data. The data were compiled into 40-day composites and the training data binned to the 500-m MODIS Integerized Sinusoidal grid. The 500-m data were sampled in a similar Fig. 8. (a) Maximum NDVI composite from October 2000 composite of tiled MODIS 250-m data for an area in South Carolina. Columbia is at left, center of the image. (b) NDVI derived from raw level 1B data for October 12, 2000 level 1B 250.

12 314 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 9. Preliminary 500-m MODIS percent tree cover map for North America.

13 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 10. Preliminary 500-m MODIS percent tree leaf type for North America.

14 316 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 11. (a) Per state thresholds at which the area estimate of the 500-m tree cover map matches United States Forest Service estimates. This value is found per state by starting at the highest percent tree cover values in the 500-m map and calculating area totals as the tree cover threshold is lowered. For the 500-m map, the area of tree cover greater than or equal to the threshold value shown yields the same area as estimated by the USFS. (b) Application of weighted mean threshold (35% tree cover) which yields an areal match with the Forest Service data for the lower 48 United States. Gray is tree cover greater than or equal to 35%; black is less than 35%.

15 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) Fig. 12. Regional comparisons of threshold matches between 500-m continuous tree cover map and United States Forest Service estimates.

16 318 M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) fashion to the 1-km AVHRR by taking every tenth pixel to reduce data volumes. A final tree of 90 nodes was created from the 24 input channels (bands 1 7 and NDVI for three 40-day composites). The initial node estimates yielded an rmse for the 82,082 training pixels of 11.07% tree cover which was reduced to 10.32% and 9.93% after regression and median refinements. The result is shown in Fig. 9. The same procedure was followed for tree leaf type, resulting in a map of percent needleleaf and broadleaf tree cover. For training sites with greater than 10% tree cover, the percent contribution of broadleaf tree cover was used as training. This yielded 48,105 training pixels. The procedure was followed as before and the percent needleleaf calculated by taking the difference of the percent total tree cover less the product of the percent broadleaf and percent tree cover. The result is shown in Fig. 10. The subsets in both Figs. 9 and 10 show the increased detail available with MODIS compared to AVHRR. 4. Evaluation of preliminary 500-m tree cover for lower 48 United States The 500-m tree cover map was compared to United States Forest Service (USFS) statistics for the lower 48 United States (Powell, Faulkner, Darr, Zhu, & MacCleery, 1992). Beginning with the densest forest stratum and lowering the continuous field threshold, a cutoff can be found for which the forest area estimate of the USFS can be matched. Fig. 11 shows for each state which continuous field threshold yields an equivalent areal estimate. A mean weighted by USFS state area estimates was derived, which results in a match for total forest area for the lower 48 states. A threshold of 35% results in a total of 2.35 million km 2 compared to the USFS estimate of 2.42 million km 2. The Forest Service definition of forest is land at least 10% stocked by trees of any size (Powell et al., 1992), but also includes areas formerly with tree cover with plans to be afforested. Fig. 11 also shows the resulting forest/ nonforest map after applying this threshold to the continuous field map. States in Fig. 11a with thresholds below and above this cutoff will, respectively, under- and overestimate the USFS figures. There are many regional differences in terms of which threshold best matches the USFS state areas totals. Fig. 12 shows these findings. For example, the intermountain west, centered on desert southwest states, has the lowest matching thresholds of any region. A clear reason for this is the inclusion of shorter stands of woody cover as forest in the USFS forest definition. Pygmy pinyon forests, chaparral and shorter oak scrub are labeled forest in the USFS definition (Powell et al., 1992). The continuous field implementation uses a definition of tree as any woody plant in excess of 5 m in height. Much of the moisture limited woody cover found in the western United States does not meet this definition. A continuous training data set for short woody vegetation is being developed to augment the tree cover layer. The corn belt is not a traditional regional subset like the other regions, but is included here due to the consistently low threshold found for the dominant corn producing states. This could be the result of an increased fragmentation of forest in this area and a confusion in spectral space between crops and sub-pixel forest which is biased toward crops. The rest of the Midwest and Great Plains states have great consistency in a threshold of at or near 36%. As one trends east the thresholds increase with the highest matching thresholds being the heavily forested south and northeast. These results show that the algorithm is producing consistent results which compare well with the USFS statistical database. Such results should be repeatable and allow for developing thresholds of change detection for monitoring purposes. This would help augment the laborintensive approach to forest area estimation employed by the USFS. However, calculating area totals can be complicated by fragmentation, as a pixel with half of its area in 100% tree cover will yield the same cover area estimate as a uniform, homogeneous 50% woodland pixel. Fragmentation could be developed as an ancillary layer in improving area estimates at the sub-pixel level. 5. Conclusion The new procedure for depicting a continuous field of tree cover is an improvement over the prototype approach. The main advance is that the algorithm is fully automated. All of the products here were generated using the new technique and do not include an interpreter s input. The continuous field training data have been critical to this advance by containing signatures across a wide range of spatial and spectral mixtures. The algorithm is made more stable in this way as signatures are not derived from only core cover exemplar sites. The regression tree algorithm is an advance as well, in that it can handle the nonlinear relationships present in a global sample of tree cover. Present work for the 500-m MODIS continuous field layers includes creating the annual metrics and producing global tree cover, leaf type and leaf longevity layers. The examples shown here indicate that MODIS data will be a substantial improvement over AVHRR in mapping tree cover. The spatial detail present in MODIS imagery is unprecedented for satellites of this kind. However, preserving the finest spatial detail within the compositing process might require new approaches. Acknowledgements This research was funded by the National Aeronautics and Space Administration under contract NAS596060, grant NAG59339, and the Earth Science Information Partnership (ESIP) program under grant NCC5300.

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