Historical Land-Cover Change and Land-Use Conversions Global Dataset
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title: Historical Land-Cover Change and Land-Use Conversions Global Dataset
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date: 2012-09-06
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abstract: A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The longest of the three estimates spans 1770-2010. The dataset presented here takes into account land-cover change due to four major land-use/management activities: (1) cropland expansion and abandonment, (2) pastureland expansion and abandonment, (3) urbanization, and (4) secondary forest regrowth due to wood harvest. Due to uncertainties associated with estimating historical agricultural (crops and pastures) land use, the study uses three widely accepted global reconstruction of cropland and pastureland in combination with common wood harvest and urban land data set to provide three distinct estimates of historical land-cover change and underlying land-use conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and extent to which different ecosystem have undergone changes. The three estimates use a consistent methodology, and start with a common land-cover map during pre-industrial conditions (year 1765), taking different courses as determined by the land-use/management datasets (cropland, pastureland, urbanization and wood harvest) to attain forest area distributions close to satellite estimates of forests for contemporary period. The satellite based estimates of forest area are based on MODIS sensor. All data uses the WGS84 spatial coordinate system for mapping.
purpose: This dataset was developed to understand the consequences of historical changes in land use and land cover for ecosystem goods and services. In particular, the data sets can be used to study how global land-cover changes have influenced climate, biogeochemical cycles, water cycle, biodiversity, etc. This data set can be used directly within spatially-explicit climate and biogeochemical models. The data are unique by providing a complete reconstruction of the past, and, in terms of details in land-cover types and methodology, are also relatively flexible to implement in other models. The dataset provides new avenues for climate models to study how uncertainties in historical land-cover change translate into uncertainties in quantifying its impacts on environmental variables of interest.
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individualName: Prasanth Meiyappan
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keyword: Earth Science > Biosphere > Ecosystems > Anthropogenic/Human Influenced Ecosystems > Agricultural Lands
keyword: Earth Science > Biosphere > Ecosystems > Terrestrial Ecosystems > Deserts
keyword: Earth Science > Biosphere > Ecosystems > Terrestrial Ecosystems > Forests
keyword: Earth Science > Biosphere > Ecosystems > Terrestrial Ecosystems > Grasslands
keyword: Earth Science > Biosphere > Ecosystems > Terrestrial Ecosystems > Savannas
keyword: Earth Science > Biosphere > Ecosystems > Terrestrial Ecosystems > Shrubland/Scrub
keyword: Earth Science > Biosphere > Ecosystems > Anthropogenic/Human Influenced Ecosystems > Urban Lands
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keyword: Earth Science > Biosphere > Vegetation > Dominant Species
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keyword: Earth Science > Biosphere > Vegetation > Reforestation
keyword: Earth Science > Biosphere > Vegetation > Vegetation Cover
keyword: Earth Science > Human Dimensions > Human Settlements > Urban Areas
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