Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.     

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.     

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.     

National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier

Decision tree classifiers have received much recent attention, particularly with regards to land cover classifications at continental to global scales. Despite their many benefits and general flexibility, the use of decision trees with high spatial resolution data has not yet been fully explored. In support of the National Park Service (NPS) Vegetation Mapping Program (VMP), we have examined the feasibility of using a commercially available decision tree classifier with multitemporal satellite data from the Enhanced Thematic Mapper-Plus (ETM+) instrument to map 11 land cover types at the Delaware Water Gap National Recreation Area near Milford, PA. Ensemble techniques such as boosting and consensus filtering of the training data were used to improve both the quality of the input training data as well as the final products.Using land cover classes as specified by the National Vegetation Classification Standard at the Formation level, the final land cover map has an overall accuracy of 82% (κ=0.80) when tested against a validation data set acquired on the ground (n=195). This same accuracy is 99.5% when considering only forest vs. nonforest classes. Usage of ETM+ scenes acquired at multiple dates improves the accuracy over the use of a single date, particularly for the different forest types. These results demonstrate the potential applicability and usability of such an approach to the entire National Park system, and to high spatial resolution land cover and forest mapping applications in general.     

Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from Advanced Very High Resolution Radiometer (AVHRR) data with 1 km spatial resolution using a six-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat Thematic Mapper (TM) and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FPAR maps. Further, the main source of disagreement in LAI and FPAR maps can be attributed to specific biome classes that are characterized by a continuum of fractional cover and canopy structure.     

Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover

Annual forest cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deforestation data from the PRODES (Amazon Deforestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national forest cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES forest and 2000 to 2005 deforestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact forest class. Total scaled MODIS change area within the PRODES historical deforestation and forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all forest change dynamics, including the cover dynamics of intact humid forest, regrowth, plantations, and cerrado tree cover assemblages. Results also indicate that operational MODIS-only forest cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.     

Estimating stand structural details using nearest neighbor analyses to link ground data, forest cover maps, and Landsat imagery

Forest information over a landscape is often represented as a spatial mosaic of polygons, separated by differences in species composition, height, age, crown closure, productivity, and other variables. These polygons are commonly delineated on medium-scale photography (e.g., 1:15,000) by a photo-interpreter familiar with the inventory area, and displayed and stored in a Geographic Information System (GIS) layer as a forest cover map. Forest cover maps are used for multiple purposes including timber and habitat supply analyses, and carbon inventories, at a regional or management unit level, and for parks planning, operational planning, and selection of stands for many purposes at a local level. Attribute data for each polygon commonly include the variables used to delineate the polygon, and other variables that can be measured or estimated using these medium-scale photographs. Additional measures that can only be obtained via expensive ground measures or possibly on high resolution photographs (e.g., volume per unit area, biomass components per unit area, tree-list of species and diameters) are available only for a sample of polygons, or may have been gathered independently using a sample survey over the land area. Improved linkages over a variety of data sources may help to support landscape level analyses. This study presents an approach to combine information from a systematic (grid) ground survey, forest cover (polygon) data, and Landsat Thematic Mapper (TM) imagery using variable-space nearest neighbor methods to estimate (i) mean ground-measured attributes for each polygon, in particular, volume per ha (m³/ha), stems per ha, and quadratic mean diameter for each polygon; and (ii) variation of these ground attributes within polygons. The approach was initially evaluated using Monte Carlo simulations with known measures of these attributes. Nearest neighbor methods were then applied to an approximate 5000 ha area (about 1000 polygons) of high productivity, mountainous forests located near the Pacific Coast of British Columbia, Canada. Based on the simulation results, the use of Landsat pixel reflectances to estimate volume per ha, average tree size (i.e., quadratic mean diameter), and stems per ha did not show great promise in improving estimates for each polygon over using forest cover data alone. However, in application, the use of remotely sensed data provided estimates of within-polygon variability. At the same time, the estimated means of these three imputed variables over the entire study area were very similar to the representative sample estimates using the ground data only. Extensions to other variables such as ranges of diameters and numbers of snags may also be possible providing useful data for habitat and forest growth analysis.     

Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Remotely sensed images and processing techniques are a primary tool for mapping changes in tropical forest types important to biodiversity and environmental assessment. Detailed land cover data are lacking for most wet tropical areas that present special challenges for data collection. For this study, we utilize decision tree (DT) classifiers to map 32 land cover types of varying ecological and economic importance over an 8000 km² study area and biological corridor in Costa Rica. We assess multivariate QUEST DTs with unbiased classification rules and linear discriminant node models for integrated vegetation mapping and change detection. Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees. A set of 35 variables from SRTM-DEM terrain variables, WorldClim grids, and Landsat TM bands were assessed.

Of the techniques examined, QUEST trees were most accurate by integrating a set of 12 spectral and geospatial predictor variables for image subsets with an overall cross-validation accuracy of 93% ± 3.3%. Accuracy with spectral variables alone was low (69% ± 3.3%). A random selection of training and test set pixels for the entire landscape yielded lower classification accuracy (81%) demonstrating a positive effect of image subsets on accuracy. A post-classification change comparison between 1986 and 2001 reveals that two lowland forest types of differing tree species composition are vulnerable to agricultural conversion. Tree plantations and successional vegetation added forest cover over the 15-year time period, but sometimes replaced native forest types, reducing floristic diversity. Decision tree classifiers, capable of combining data from multiple sources, are highly adaptable for mapping and monitoring land cover changes important to biodiversity and other ecosystem services in complex wet tropical environments.     

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.     

A non-parametric,supervised classification of vegetation types on the Kaibab National Forest using decision trees

Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30 m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in northern Arizona to a 10-m spatial resolution with field data, using topographical information and Landsat TM imagery as auxiliary variables. Vegetation types were identified by clustering the field variables total basal area and proportion of basal area by species, and then using a decision tree based on auxiliary variables to predict vegetation types. Vegetation types modelled included pinyon-juniper, ponderosa pine, mixed conifer, spruce- and deciduous-dominated mixes, and openings. To independently assess the accuracy of the final vegetation maps using reference data from different sources, we used a post-stratified, multivariate composite estimator. Overall accuracy was 74.5% (Kappa statistic = 49.9%). Sources of error included differentiating between mixed conifer and spruce-dominated types and between openings in the forest and deciduous-dominated mixes. Overall, our non-parametric classification method successfully identified dominant vegetation types on the study area at a finer spatial resolution than can typically be achieved using traditional classification techniques.     

Using AVIRIS data and multiple-masking techniques to map urban forest tree species

Tree type and species information are critical parameters for urban forest management, benefit cost analysis and urban planning. However, traditionally, these parameters have been derived based on limited field samples in urban forest management practice. In this study we used high-resolution Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and multiple-spectral masking techniques to identify and map urban forest trees. Trees were identified based on their spectral character difference in AVIRIS data. The use of multiple-masking techniques shift the focus to the target land cover types only, thus reducing confounding noise during spectral analysis. The results were checked against ground reference data and by comparison to tree information in an existing geographical information system (GIS) database. At the tree type level, mapping was accomplished with 94% accuracy. At the tree species level, the average accuracy was 70% but this varied with both tree type and species. Of the four evergreen tree species, the average accuracy was 69%. For the 12 deciduous tree species, the average accuracy was 70%. The relatively low accuracy for several deciduous species was due to small tree size and overlapping among tree crowns at the 3.5 m spatial resolution of AVIRIS data. This urban forest tree species mapping method has the potential to increase data update intervals and accuracy while reducing costs compared to field sampling or other traditional methods.     

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.     

Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data

The tundra-taiga transition zone stretches around the northern hemisphere separating boreal forest to the south from treeless tundra to the north. Tree cover and height are important variables to characterize this vegetation transition. Accurate continuous fields of tree cover and height would enable the delineation of the forest extent according to different criterion and provide useful data for change detection of this climatically sensitive ecotone. This study examined if multiangular remote sensing data has potential to improve the accuracy of the tree cover and height estimates in relation to nadir-view data. The satellite data consisted of Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m and 1.1 km resolutions. The study area was located in the Fennoscandian tundra-taiga transition zone, in northernmost Finland. The continuous fields of tree cover and height were estimated using neural networks, which were trained and assessed by high-resolution biotope inventory data. The spectral-angular data together produced lower estimation errors than single band nadir, multispectral nadir or single band multiangular data alone. RMSE of the tree cover estimates reduced from 7.8% (relative RMSE 67.4%) to 6.5% (56.1%) at 275 m resolution, and from 5.4% (49.2%) to 4.1% (36.9%) at 1.1 km resolution, when multispectral nadir data were used together with multiangular data. RMSE of the tree height estimates reduced from 2.3 m (44.3%) to 2.0 m (37.6%) and from 1.8 m (35.4%) to 1.3 m (25.4%), respectively. The largest estimation errors occurred in mires and in areas of dense shrub cover, but the use of multiangular data also reduced estimation errors in these areas. The results suggest that directional information has potential to improve the tree cover and height estimates, and hence the accuracy of the land cover change detection in the tundra-taiga transition zone.     

Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Temperate East Asia (TEA) is characterized by diverse land cover types, including forest and agricultural lands, one of the world's largest temperate grasslands, and extensive desert and barren landscapes. In this paper, we explored the potential of SPOT-4 VEGETATION (VGT) data for the classification of land cover types in TEA. An unsupervised classification was performed using multi-temporal (March–November 2000) VGT-derived spectral indices (Land Surface Water Index [LSWI] and Enhanced Vegetation Index [EVI]) to generate a land cover map of TEA (called VGT-TEA). Land cover classes from VGT-TEA were aggregated to broad, general class types, and then compared and validated with classifications derived from fine-resolution (Landsat) data. VGT-TEA produced reasonable results when compared to the Landsat products. Analysis of the seasonal dynamics of LSWI and EVI allows for the identification of distinct growth patterns between different vegetation types. We suggest that LSWI seasonal curves can be used to define the growing season for temperate deciduous vegetation, including grassland types. Seasonal curves of EVI tend to have a slightly greater dynamic range than LSWI during the peak growing season and can be useful in discriminating between vegetation types. By using these two complementary spectral indices, VGT data can be used to produce timely and detailed land cover and phenology maps with limited ancillary data needed.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

Mapping a keystone shrub species,huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains

Black huckleberries (Vaccinium membranaceum) provide a critical food resource to many wildlife species, including apex omnivores such as the grizzly bear (Ursus arctos), and play an important socioeconomic role for many communities in western North America, especially indigenous peoples. Remote sensing imagery offers the potential for accurate landscape-level mapping of huckleberries because the shrub changes colour seasonally. We developed two methods, for local and regional scales, to map a shrub species using leaf seasonal colour change from remote sensing imagery. We assessed accuracy with ground-based vegetation surveys. The high-resolution supervised random forest classification from one-meter resolution National Agricultural Imagery Program (NAIP) imagery achieved an overall accuracy of 75.31% (kappa = 0.26). The approach using multi-temporal 30-meter Landsat imagery similarly had an overall accuracy of 79.19% (kappa = .31). We found underprediction error was related to higher forest cover and a lack of visible colour change on the ground in some plots. Where forest cover was low, both models performed better. In areas with <10% forest cover, the high-resolution classification achieved an accuracy of 80.73% (kappa = 0.48), while the Landsat model had an accuracy of 82.55% (kappa = 0.47). Based on the fine-scale predictions, we found that 94% of huckleberry shrubs identified in our study area of Glacier National Park, Montana, USA are over 100 meters from human recreation trails. This information could be combined with productivity and phenology information to estimate the timing and availability of food resources for wildlife and to provide managers with a tool to identify and manage huckleberries. The development of the multi-temporal Landsat models sets the stage for assessment of impacts of disturbance at regional scales on this ecologically, culturally, and economically important shrub species. Our approach to map huckleberries is straightforward, efficient and accessible to wildlife and environmental managers and researchers in diverse fields.     

High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests

Satellite imagery with fine spectral and spatial resolution and high temporal resolution provides a unique opportunity to map and monitor biodiversity. The aim of this article is (i) to review the state of the use of high-resolution remote sensing for ecological studies in the Indian Himalayan Region (IHR) and (ii) to suggest further potential avenues of research in the region using this technology. It has been recognized that the recent improvements in remote sensing have enabled researchers to categorize and spatially map species and communities and to detect vegetation types based on ecological gradients and environmental drivers. Across the globe, the use of current high-resolution imagery in vegetation studies is increasing due to their improved efficiency, as fine-scale vegetation information is needed for both theoretical understanding of the processes involved and conservation towards the maintenance of ecological functions of natural ecosystems. Remote sensing is particularly useful in the IHR, where field-based mapping of forest vegetation and land cover using conventional techniques is a cost- and time-intensive effort. Mapping fine-scale patterns of tree species using high-resolution remote-sensing data has important implications for the understanding and monitoring of ecosystem function and biodiversity patterns in forests.     

Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.