Department of Geography began a joint project with the Institute for Advanced
Computer Studies (UMIACS) to employ advanced computational methodologies to
tackle issues of data volume, data processing, and data analyses in global and regionalscale studies (Davis and Townshend, 1993). Because of this joint effort, a National
Science Foundation Grand Challenge grant was awarded to UMIACS and the
Department of Geography. This award challenged the University of Maryland team to
resolve the data volume issue involved in global and regional-scale data analyses as
well as addressing the user community’s need for faster processing time (Townshend
et al., 2000). The results of this research were the basis for the current GLCF structure.
Currently, the GLCF is funded by NASA’s Earth Science Information Partnership
(ESIP).
2.2 PRODUCTS GENERATED USING GLCF DATA AND DISSEMINATED ON
THE GLCF WEBSITE
The GLCF develops, disseminates, and archives not only satellite imagery but
earth science products as well. The type of data the GLCF provide includes (but is not
limited to): 1) Landsat imagery (Enhanced Thematic Mapper (ETM), Thematic Mapper
(TM) and Multispectral Scanner (MSS); 2) Landsat derived products (e.g. mosaics); 3)
Moderate Resolution Imaging Spectro-radiometer (MODIS) products (e.g. 32-day
composites); 4) AVHRR products; 5) satellite-derived calculations of radiative flux; 6)
NOAA’s Geostationary Operational Environmental Satellite (GOES) data; and 7) urban
growth of major United States metropolitan centers. The following sections briefly
describe each of the major data products applicable to coastal ecosystem research.
2.2.1 High Resolution Data Products
Deforestation Mapping Product. The purpose of the Deforestation Mapping Project
(DMP) was to generate digital forest cover maps and offer them to other researchers for
use as baseline data. Deforestation can have extremely negative affects on aquatic
communities (fish species, aquatic invertebrates) due to increased runoff and sediment
deposition, as well as coastal land structure (Duarte, 1996). The DMP dataset used
multiple dates of Landsat TM and ETM+ to classify change in forest extent over
various time periods. There are six major classes in these datasets: forest, degraded
forest, nonforest, water, cloud, and shadow. The (DMP) generated several major
products: 1) 1980s and 1990s country map products of Bolivia, Peru, Colombia,
Ecuador (1990s only), and the Democratic Republic of Congo; and 2) 1990s and 1980s
time series products of Pan-Amazon Deforestation Hotspots, the Central African
region, other Pan-Amazon areas of interest, and a Bolivia (1970s, 1980s and 1990s)
land cover change map (1999, UMD).
Coastal Marsh Project. The objective of the Coastal Marsh Project (CMP) was to
analyze Landsat TM images in combination with data from the USDA National
Wetlands Inventory. The results of this analysis allowed researchers to locate varied
surface conditions of coastal marshes and classify areas according to percentage of
water as follows: “up to 10% water is healthy marsh; up to 20% is slightly deteriorated
marsh; up to 30% is moderately deteriorated marsh; and above 50% indicates complete
deterioration” (Kearney et al., 1995). This classification helped determine the
mechanism for total marsh loss. This dataset is available for most of the east coast of
the United States. The final products include the development of a classified health
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Computer Studies (UMIACS) to employ advanced computational methodologies to
tackle issues of data volume, data processing, and data analyses in global and regionalscale studies (Davis and Townshend, 1993). Because of this joint effort, a National
Science Foundation Grand Challenge grant was awarded to UMIACS and the
Department of Geography. This award challenged the University of Maryland team to
resolve the data volume issue involved in global and regional-scale data analyses as
well as addressing the user community’s need for faster processing time (Townshend
et al., 2000). The results of this research were the basis for the current GLCF structure.
Currently, the GLCF is funded by NASA’s Earth Science Information Partnership
(ESIP).
2.2 PRODUCTS GENERATED USING GLCF DATA AND DISSEMINATED ON
THE GLCF WEBSITE
The GLCF develops, disseminates, and archives not only satellite imagery but
earth science products as well. The type of data the GLCF provide includes (but is not
limited to): 1) Landsat imagery (Enhanced Thematic Mapper (ETM), Thematic Mapper
(TM) and Multispectral Scanner (MSS); 2) Landsat derived products (e.g. mosaics); 3)
Moderate Resolution Imaging Spectro-radiometer (MODIS) products (e.g. 32-day
composites); 4) AVHRR products; 5) satellite-derived calculations of radiative flux; 6)
NOAA’s Geostationary Operational Environmental Satellite (GOES) data; and 7) urban
growth of major United States metropolitan centers. The following sections briefly
describe each of the major data products applicable to coastal ecosystem research.
2.2.1 High Resolution Data Products
Deforestation Mapping Product. The purpose of the Deforestation Mapping Project
(DMP) was to generate digital forest cover maps and offer them to other researchers for
use as baseline data. Deforestation can have extremely negative affects on aquatic
communities (fish species, aquatic invertebrates) due to increased runoff and sediment
deposition, as well as coastal land structure (Duarte, 1996). The DMP dataset used
multiple dates of Landsat TM and ETM+ to classify change in forest extent over
various time periods. There are six major classes in these datasets: forest, degraded
forest, nonforest, water, cloud, and shadow. The (DMP) generated several major
products: 1) 1980s and 1990s country map products of Bolivia, Peru, Colombia,
Ecuador (1990s only), and the Democratic Republic of Congo; and 2) 1990s and 1980s
time series products of Pan-Amazon Deforestation Hotspots, the Central African
region, other Pan-Amazon areas of interest, and a Bolivia (1970s, 1980s and 1990s)
land cover change map (1999, UMD).
Coastal Marsh Project. The objective of the Coastal Marsh Project (CMP) was to
analyze Landsat TM images in combination with data from the USDA National
Wetlands Inventory. The results of this analysis allowed researchers to locate varied
surface conditions of coastal marshes and classify areas according to percentage of
water as follows: “up to 10% water is healthy marsh; up to 20% is slightly deteriorated
marsh; up to 30% is moderately deteriorated marsh; and above 50% indicates complete
deterioration” (Kearney et al., 1995). This classification helped determine the
mechanism for total marsh loss. This dataset is available for most of the east coast of
the United States. The final products include the development of a classified health
186
Gebelein