Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years
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- Meiyappan, P. & Jain, A.K. Front. Earth Sci. (2012) 6: 122. doi:10.1007/s11707-012-0314-2
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Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 landcover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.