Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM
Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to de...
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Format: | Dataset |
Language: | English |
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Zenodo
2018
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Online Access: | https://dx.doi.org/10.5281/zenodo.1447209 https://zenodo.org/record/1447209 |
Summary: | Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to derive all DEM derivatives. To access original DEM tiles please refer to MERIT DEM download page. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: dtm = theme: digital terrain models, twi = variable: SAGA GIS Topographic Wetness Index, merit.dem = determination method: MERIT DEM, m = mean value, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2017 = time reference: year 2017, v1.0 = version number: 1.0, : {"references": ["Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., ... & Bates, P. D. (2017). A high\u2010accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844-5853.", "Bauer-Marschallinger, B., Sabel, D., & Wagner, W. (2014). Optimisation of global grids for high-resolution remote sensing data. Computers & Geosciences, 72, 84-93."]} |
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