Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales
To avoid wetland degradation and promote sustainable wetlands use, decision-makers and managing institutions need quantified and spatially explicit information on wetland ecosystem condition for policy development and wetland management. Remote sensing holds a significant potential for wetland mappi...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/129668 |
| _version_ | 1855543727484829696 |
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| author | Steinbach, S. Hentschel, E. Hentze, K. Rienow, A. Umulisa, V. Zwart, Sander J. Nelson, A. |
| author_browse | Hentschel, E. Hentze, K. Nelson, A. Rienow, A. Steinbach, S. Umulisa, V. Zwart, Sander J. |
| author_facet | Steinbach, S. Hentschel, E. Hentze, K. Rienow, A. Umulisa, V. Zwart, Sander J. Nelson, A. |
| author_sort | Steinbach, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | To avoid wetland degradation and promote sustainable wetlands use, decision-makers and managing institutions need quantified and spatially explicit information on wetland ecosystem condition for policy development and wetland management. Remote sensing holds a significant potential for wetland mapping, inventorying, and monitoring. The Wetland Use Intensity (WUI) indicator, which is not specific to a particular crop and which requires little ancillary data, is based on the Mean Absolute Spectral Dynamics (MASD), which is a cumulative measure of reflectance change across a time series of optical satellite images. It is sensitive to the compound effects of land cover changes caused by different agricultural practices, flooding or burning. The more frequent and intrusive management practices are on the land cover, the stronger the WUI signal. WUI thus serves as a surrogate indicator to measure pressure on wetland ecosystems.
We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks. |
| format | Journal Article |
| id | CGSpace129668 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1296682025-12-08T10:11:39Z Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales Steinbach, S. Hentschel, E. Hentze, K. Rienow, A. Umulisa, V. Zwart, Sander J. Nelson, A. wetlands ecosystems environmental health assessment remote sensing indicators earth observation satellites datasets land use surface water water quality vegetation gomorphology satellite imagery To avoid wetland degradation and promote sustainable wetlands use, decision-makers and managing institutions need quantified and spatially explicit information on wetland ecosystem condition for policy development and wetland management. Remote sensing holds a significant potential for wetland mapping, inventorying, and monitoring. The Wetland Use Intensity (WUI) indicator, which is not specific to a particular crop and which requires little ancillary data, is based on the Mean Absolute Spectral Dynamics (MASD), which is a cumulative measure of reflectance change across a time series of optical satellite images. It is sensitive to the compound effects of land cover changes caused by different agricultural practices, flooding or burning. The more frequent and intrusive management practices are on the land cover, the stronger the WUI signal. WUI thus serves as a surrogate indicator to measure pressure on wetland ecosystems. We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks. 2023-07 2023-03-16T06:17:22Z 2023-03-16T06:17:22Z Journal Article https://hdl.handle.net/10568/129668 en Open Access Elsevier Steinbach, S.; Hentschel, E.; Hentze, K.; Rienow, A.; Umulisa, V.; Zwart, Sander J.; Nelson, A. 2023. Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales. Ecological Informatics, 75:102032. [doi: https://doi.org/10.1016/j.ecoinf.2023.102032] |
| spellingShingle | wetlands ecosystems environmental health assessment remote sensing indicators earth observation satellites datasets land use surface water water quality vegetation gomorphology satellite imagery Steinbach, S. Hentschel, E. Hentze, K. Rienow, A. Umulisa, V. Zwart, Sander J. Nelson, A. Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title | Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title_full | Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title_fullStr | Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title_full_unstemmed | Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title_short | Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales |
| title_sort | automatization and evaluation of a remote sensing based indicator for wetland health assessment in east africa on national and local scales |
| topic | wetlands ecosystems environmental health assessment remote sensing indicators earth observation satellites datasets land use surface water water quality vegetation gomorphology satellite imagery |
| url | https://hdl.handle.net/10568/129668 |
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