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Data from: Detailed temporal mapping of global human modification from 1990 to 2017

Citation

Theobald, David et al. (2020), Data from: Detailed temporal mapping of global human modification from 1990 to 2017, Dryad, Dataset, https://doi.org/10.5061/dryad.n5tb2rbs1

Abstract

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.

Usage Notes

The file naming convention for the zip-files that contain the datasets provided here is as follows:

gHM_landLakeReservoirOcean300m.zip - contains TIFs at 300 m resolution to represent the following classes: 1=land, 2=lake (natural water bodies), 3=reservoirs (water bodies created by dams), and 4=ocean.

gHMv1_300m_1990_change.zip - contains TIFs at 300 m resolution for calculating change between 1990 and 2000, 2010, or 2015.

gHMv1_300m_2000_change.zip - contains TIFs at 300 m resolution for calculating change between 2000 and 1990, 2010, or 2015.

gHMv1_300m_2010_change.zip - contains TIFs at 300 m resolution for calculating change between 2010 and 1990, 2000, or 2015.

gHMv1_300m_2015_change.zip - contains TIFs at 300 m resolution for calculating change between 2015 and 1990, 2000, or 2010.

gHMv1_300m_2017_static.zip - contains TIFs at 300 m resolution that contains all stressors that represents ~2017 conditions. This is NOT to be used to compare to other "change" datasets.

gHMv1_1000m_2017_static_stressors.zip - contains TIFs at 1000 m resolution, where each major stressor group is separate: "static_builtup" (urban & built-up), "static_ag" (agriculture and biological harvesting of forests), "static_energy" (energy production and mining), "static_trans" (transportation & service corridors), and "static_intrusion" (human intrusions, natural system modifications, and pollution). 

The original values of the datasets ranged from 0.0 to 1.0, where 0.0 is no human modification and 1.0 is full or complete human modification. These values were represented as 32-bit floating point values, but were converted to a 16-bit integer to reduce file size, by multiplying by 32767. Note that to obtain the terrestrial portions of the globe, these datasets will need to be masked by the gHM_landLakeReservoirOcean300m dataset using values 2 and 4 to set NO DATA values. Datasets are in EPSG 3857 coordinates and the extent of longitude/latitude is -180, -75, 180, 85.

Funding

None