10.5061/DRYAD.N5TB2RBS1
Theobald, David
0000-0002-1271-9368
Conservation Planning Technologies
Kennedy, Christina
0000-0001-8902-8728
The Nature Conservancy
Chen, Bin
University of California, Davis
Oakleaf, James
The Nature Conservancy
Kiesecker, Joe
The Nature Conservancy
Baruch-Mordo, Sharon
The Nature Conservancy
Data from: Detailed temporal mapping of global human modification from
1990 to 2017
Dryad
dataset
2020
human land use
sustainable development
Geographic information systems
global dataset
None
2020-01-31T00:00:00Z
2020-01-31T00:00:00Z
en
https://doi.org/10.5194/essd-2019-252
18957944014 bytes
12
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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. Please also be sure to check your spam
folder if you do not receive an email with the link from Dryad, which is
provided because of the large file size.
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.
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.