Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applicatio...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2015
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/72516 |
| _version_ | 1855527486626988032 |
|---|---|
| author | Fluet-Chouinard, E. Lehner, B. Rebelo, Lisa-Maria Papa, F. Hamilton, S.K. |
| author_browse | Fluet-Chouinard, E. Hamilton, S.K. Lehner, B. Papa, F. Rebelo, Lisa-Maria |
| author_facet | Fluet-Chouinard, E. Lehner, B. Rebelo, Lisa-Maria Papa, F. Hamilton, S.K. |
| author_sort | Fluet-Chouinard, E. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged decision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 × 106 km2 ), mean annual maximum (12.1 × 106 km2 ), and long-term maximum ( 17.3 × 106 km2 ); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. |
| format | Journal Article |
| id | CGSpace72516 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace725162025-06-17T08:23:47Z Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data Fluet-Chouinard, E. Lehner, B. Rebelo, Lisa-Maria Papa, F. Hamilton, S.K. flooding floodplains mapping land cover satellite imagery remote sensing surface water topography decision support systems databases hydrology models wetlands ecosystems Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged decision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 × 106 km2 ), mean annual maximum (12.1 × 106 km2 ), and long-term maximum ( 17.3 × 106 km2 ); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. 2015-03 2016-03-09T06:16:04Z 2016-03-09T06:16:04Z Journal Article https://hdl.handle.net/10568/72516 en Open Access Elsevier Fluet-Chouinard, E.; Lehner, B.; Rebelo, Lisa-Maria; Papa, F.; Hamilton, S. K. 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158:348-361. doi: https://doi.org/10.1016/j.rse.2014.10.015 |
| spellingShingle | flooding floodplains mapping land cover satellite imagery remote sensing surface water topography decision support systems databases hydrology models wetlands ecosystems Fluet-Chouinard, E. Lehner, B. Rebelo, Lisa-Maria Papa, F. Hamilton, S.K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title | Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title_full | Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title_fullStr | Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title_full_unstemmed | Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title_short | Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data |
| title_sort | development of a global inundation map at high spatial resolution from topographic downscaling of coarse scale remote sensing data |
| topic | flooding floodplains mapping land cover satellite imagery remote sensing surface water topography decision support systems databases hydrology models wetlands ecosystems |
| url | https://hdl.handle.net/10568/72516 |
| work_keys_str_mv | AT fluetchouinarde developmentofaglobalinundationmapathighspatialresolutionfromtopographicdownscalingofcoarsescaleremotesensingdata AT lehnerb developmentofaglobalinundationmapathighspatialresolutionfromtopographicdownscalingofcoarsescaleremotesensingdata AT rebelolisamaria developmentofaglobalinundationmapathighspatialresolutionfromtopographicdownscalingofcoarsescaleremotesensingdata AT papaf developmentofaglobalinundationmapathighspatialresolutionfromtopographicdownscalingofcoarsescaleremotesensingdata AT hamiltonsk developmentofaglobalinundationmapathighspatialresolutionfromtopographicdownscalingofcoarsescaleremotesensingdata |