Land cover classification in SE Asia using near and short wave infrared bands

A land cover classification map is necessary for modelling interactions between the land surface and the atmosphere, monitoring the environment and estimating food production. In order to classify land cover in SE Asia in 2000, Normalized Difference Vegetation Index (NDVI), reflectance of near-infrared (NIR) band, and reflectance of short wave infrared (SWIR) band of Systeme pour l'Observation de la Terre (SPOT) VEGETATION data were used in this study. First, ground data were collected for training data. In addition, supervised classification was performed on twelve months of NDVI data. As a result, some deserts and peripheral sparse vegetative areas were classified into urban, compared with the world atlas. Secondly, the number of months when the reflectance of the SWIR band is higher than that of the NIR band was counted (SWIR>NIR month-count condition) in each pixel, and pixels with counts of 10 were classified as Sparse Herbaceous/Shrub and of 11 or 12 were classified as Bare Areas, respectively. Finally, land cover was classified based on the SWIR>NIR month-count condition combined with NDVI, and it was compared with the existing land cover map. It was found that the SWIR>NIR month-count condition gives a better result for areas of non- or sparsely vegetative classification than when using only NDVI.     

SPOT‐4 VEGETATION multi‐temporal compositing for land cover change studies over tropical regions

Multi‐temporal compositing of SPOT‐4 VEGETATION imagery over tropical regions was tested to produce spatially coherent monthly composite images with reduced cloud contamination, for the year 2000. Monthly composite images generated from daily images (S1 product, 1‐km) encompassing different land cover types of the state of Mato Grosso, Brazil, were evaluated in terms of cloud contamination and spatial consistency. A new multi‐temporal compositing algorithm was tested which uses different criteria for vegetated and non‐vegetated or sparsely vegetated land cover types. Furthermore, a principal components transformation that rescales the noise in the image—Maximum Noise Fraction (MNF)—was applied to a multi‐temporal dataset of monthly composite images and tested as a method of additional signal‐to‐noise ratio improvement. The back‐transformed dataset using the first 12 MNF eigenimages yielded an accurate reconstruction of monthly composite images from the dry season (May to September) and enhanced spatial coherence from wet season images (October to April), as evaluated by the Moran's I index of spatial autocorrelation. This approach is useful for land cover change studies in the tropics, where it is difficult to obtain cloud‐free optical remote sensing imagery. In Mato Grosso, wet season composite images are important for monitoring agricultural crop cycles.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

NDVI-derived land cover classifications at a global scale

Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

Hierarchical Land Cover Classification based on MODIS NDVI Time-series

In this paper,we mainly used MODIS NDVI time-series dataset at 16-days temporal resolution and 250-meters spatial resolution to analyze land cover mapping of northeastern China.We used two different filter methods to fit NDVI time-series dataset,and compared their average classes’ separability based on Jeffries-Matusita distance index.In addition,we made use of hierarchical classification method to complete classification,combined with short-wave infrared spectral reflectance data and DEM.We conformed to the principle that separate area hierarchically into several parts first and then classify each part further,and use a single characteristic band first and then multiple feature bands.In the process of classification,we adopted threshold value method,support vector machine,artificial net neural and C5.0 decision tree classification to distinguish each land-cover type hierarchically.Finally,we evaluated the accuracy of the final classification of study area using known land-cover classification data and high-resolution remote sensing imagery,overall accuracy is 84.61%,Kappa coefficient is 0.8262.     

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.     

Boosted decision tree classifications of land cover over Turkey integrating MODIS,climate and topographic data

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP). Twelve land cover maps were generated for Turkey using boosted decision trees (BDTs) based on the stepwise addition of 14 explanatory variables derived from a time series of 16-day MODIS composites between 2000 and 2006 (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and four spectral bands) and ancillary climate and topographic data (minimum and maximum air temperature, precipitation, potential evapotranspiration, aspect, elevation, distance to sea and slope) at 500-m resolution. Evaluation of the 12 BDTs indicated that the BDT built as a function of all the MODIS and climate variables, aspect and elevation produced the highest degree of overall classification accuracy (79.8%) and kappa statistic (0.76) followed by the BDTs that additionally included distance to sea (DtS), and both DtS and slope. Based on an independent validation dataset derived from a pre-existing national forest map and Landsat images of Turkey, the highest overall accuracy (64.7%) and kappa coefficient (0.58) among the 12 land cover maps was achieved by using MODIS-derived NDVI time series only, followed by NDVI and EVI time series combined; NDVI, EVI and four MODIS spectral bands; and the combination of all MODIS and climate data, aspect, elevation and distance to sea, respectively. The largest improvements in producer's accuracies were observed for grasslands (+50%), barrenlands (+46%) and mixed forests (+39%) and in user's accuracies for grasslands (+53%), shrublands (+30%) and mixed forests (+28%), in relation to the lowest producer's accuracy. The results of this study indicate that BDTs can increase the accuracy of land cover classifications at the national scale.     

Estimating above‐ground burned biomass and CO2 emissions for tropical Africa for the year 1990 with the NOAA–NASA Pathfinder AVHRR 8 km land dataset

Biomass burning combusts Earth's vegetation (in forests, savannas and agricultural lands) and occurs over huge areas of the Earth's surface. Global estimates of biomass burning are thus required in order to provide exact figures of the gas fluxes derived from this source. In this paper we use coarse resolution images for estimating above‐ground burned biomass and CO₂ emissions for tropical Africa for the year 1990. The burned land cover areas have been derived from burn scar and land cover maps using the global daily National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder AVHRR 8?km land dataset. A burned area estimation of (742±222)?Mha has been considered. Monthly maximum Normalized Difference Vegetation Index (NDVI) composites and biomass density measurements have been used for modelling the temporal behaviour of above‐ground biomass for the main seasonal vegetation classes in Africa (humid savanna, derived humid savanna, dry savanna grassland and broadleaf savanna). The amount of above‐ground burned biomass and therefore CO₂ emissions can be estimated from burned land cover area, above‐ground biomass density, burn efficiency and emission factor of trace gas by land cover class. A total of 6494 (3675–9312) Tg for CO₂ emissions was computed for tropical Africa for the year 1990.     

A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data

We present results from analyses conducted to evaluate the performance of advanced supervised classification algorithms (decision trees and neural nets) applied to AVHRR data to map regional land cover in Central America. Our results indicate that the sampling procedure used to stratify ground data into train and test sub-populations can substantially bias accuracy assessment results. In particular, we found spatial autocorrelation in test data to inflate estimates of classification accuracy by up to 50 points. Results from evaluations performed using independent train and test data suggest that the feature space provided by AVHRR NDVI data is poorly suited for most land cover mapping problems, with the exception of those involving highly generalized classes.     

The IGBP-DIS global 1km land cover data set,DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Fourier analysis of historical NOAA time series data to estimate bimodal agriculture

In the present study, NDVI time‐series 10‐day composites derived from NOAA AVHRR data were used to estimate bimodal agriculture areas (where there are two seasons of cultivation per annum) using Fourier approach. The NDVI sequence was transformed into harmonic signals and the amplitude and phase of first and second harmonics were used for the analysis. A classification was applied, using a decision tree, to discriminate bimodal agriculture area from other land cover types, principally over the Asian sub‐region. When the amplitude of second harmonics in a sample region, where bimodal agriculture is predominant, was compared with the irrigated area statistics developed by FAO‐UF, a linear relationship was determined. The derived function was applied to transform the amplitude of second harmonics to bimodal agriculture area estimates. Thus large‐scale irrigation projects appear on the map and provide an encouraging initial result. This result indicates that estimating bimodal agriculture area that is one of the main sources of information for irrigated area mapping at regional or global scale, with improved accuracy possible if greater spatial, temporal resolution is achieved, for instance from MODIS or SPOT vegetation time series NDVI data, combined with (1) an improved decision tree classification algorithm and (2) a greater precision and geographical distribution of ground‐truth data. The principle merits of this approach are automation and repeatability.     

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, nearinfrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite dataset as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).     

多时机NOAA—AVHRR数据主成分分析的生物学意义

利用多时上NOAA－AVHRR的中国归一化植被指数NDVI数据进行主成分分析,并与从NDVI派生的4个生物不数作相关分析,结果表明：主成分变换既压缩了信息,将21个月的信息主要压缩到前4个主分量,又提取了关键的变化信息,第一主分量反映基本植被覆信息,第二、第三和第四主分量反映植被季相变化信息,正是由于一年12个月的NDVI曲线反映了植被季相变化特征,使得主成分变换得到的各主分量具有一定的生物学意义,而且17种中国典型植被在这4个主分量图像上存在一定的差异性,使其具有进行较高精度土地覆盖分类的潜力。     

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.     

The influence of temperature and precipitation climate regimes on vegetation dynamics in the US Great Plains: a satellite bioclimatology case study

Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data are widely used in global‐change research, yet relationships between the NDVI and ecoclimatological variables are not fully understood. This study attempts to model climate‐driven vegetation dynamics through the integration of satellite‐derived NDVI data with climate data collected from ground‐based meteorological stations in the US Great Plains. Monthly maximum value composites of NDVI data (8‐km resolution) and monthly temperature and precipitation records from 305 stations were collected from 1982 to 2001. Analyses involving deseasonalized datasets supported temperature as the dominant climate regime, demonstrating a higher average NDVI–temperature correlation (r = 0.73) than the NDVI–precipitation relationship (r = 0.38). Cluster analysis was used to develop a climate regionalization scheme based primarily on temperature, and NDVI characteristics of each subregion were compared. In the context of global climate change, findings from this study emphasize the influence of temperature and precipitation variability over vegetation cover in the Great Plains region.     

Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu,India

Drought is an insidious hazard of nature and is considered to be the most complex but least understood of all natural hazards. Large historical datasets are required to study drought and these involve complex interrelationships between climatological and meteorological data. Rainfall is an important meteorological parameter; the amount and distribution influence the type of vegetation in a region. To analyse the changes in vegetation cover due to variation in rainfall and identify the land-use areas facing drought risk, rainfall data from 1981 to 2003 were categorized into excess, normal, deficit and drought years. The Advanced Very High Resolution Radiometer (AVHRR) sensor's composite dataset was used for analysing the temporal and interannual behaviour of surface vegetation. The various land-use classes – crop land (annual, perennial crops), scrub land, barren land, forest land, degraded pasture and grassland – were identified using satellite data for excess, normal, deficit and drought years. Normalized Difference Vegetation Indices (NDVIs) were derived from satellite data for each land-use class and the highest NDVI mean values were 0.515, 0.436 and 0.385 for the tapioca crop in excess, normal and deficit years, respectively, whereas in the drought year, the groundnut crop (0.267) showed the maximum. Grassland recorded the lowest value of NDVI in all years except for the excess year. Annual crops, such as groundnut (0.398), pulses (0.313), sorghum (0.120), tapioca (0.436) and horse gram (0.259), registered comparatively higher NDVI values than the perennial crops for the normal year. The Vegetation Condition Index (VCI) was used to estimate vegetation health and monitor drought. Among land-use classes, the maximum VCI value of 92.1% was observed in onions for the excess year, whereas groundnut witnessed the maximum values of 78.2, 64.5 and 55.2% for normal, deficit and drought years, respectively. Based on the VCI classification, all land-use classes fall into the optimal or normal vegetation category in excess and normal years, whereas in drought years most of the land-use classes fall into the drought category except for sorghum, groundnut, pulses and grasses. These crops (sorghum 39.7%, groundnut 55.2%, pulses 38.5% and grassland 38.6%) registered maximum VCI values, revealing that they were sustained under drought conditions. It is suggested that the existing crop pattern be modified in drought periods by selecting the suitable crops of sorghum, groundnut and pulses and avoiding the cultivation of onion, rice and tapioca.     

Land‐cover mixing and spectral vegetation indices

Vegetation indices have been widely used as indicators of seasonal and inter‐annual variations in vegetation caused by either human activities or climate, with the overall goal of observing and documenting changes in the Earth system. While existing satellite remote sensing systems, such as NASA's Multi‐angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), are providing improved vegetation index data products through correcting for the distortions in surface reflectance caused by atmospheric particles as well as ground covers below vegetation canopy, the impact of land‐cover mixing on vegetation indices has not been fully addressed. In this study, based on real image spectral samples for two‐component mixtures of forest and common nonforest land‐cover types directly extracted from a 1.1?km MISR image by referencing a 30?m land‐cover classification, the effect of land‐cover mixing on the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) has been quantitatively evaluated. When the areal fraction of forest was lower than 80%, both NDVI and EVI varied greatly with mixed land‐cover types, although EVI varied less than NDVI. Such a phenomenon can cause errors in applications based on use of these vegetation indices. This study suggests that methods that reduce land‐cover mixing effects should be introduced when developing new spectral vegetation indices.     

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.