Detailed temporal mapping of global human modification from 1990 to 2017

Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, 2015, and 2017 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). Our novel datasets are detailed (0.09 km2 resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset for ~2017 with 14 stressors for an even more comprehensive dataset. Also provided is a land/water mask to support subsequent analyses.

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Methods
Detailed global datasets for 1990, 2000, 2010, and 2015 for land use modification of terrestrial lands (excluding Antarctica) are provided here. These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km2 resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset for ~2017 with 14 stressors for an even more comprehensive dataset, but it should not be used to calculate change with the other datasets (1990-2015). Also provided is a land/water mask (~2015 conditions) to support subsequent analyses. The file gHM_landLakeReservoirOcean300m.zip provides a land/water mask, and differentiates land, ocean, lakes, and reservoirs to allow subsequent analyses to support subsequent analyses. We also provide 5 datasets representing major stressor groups (i.e. built-up, ag/timber, energy/mining, transportation/corridors, and human intrusion) that are components of the full dataset that contains all stressors.