Vogel, Anissa, Seeger, Katharina ORCID: 0000-0003-0766-9818, Brill, Dominik ORCID: 0000-0001-8637-4641, Brueckner, Helmut, Soe, Khin Khin, Oo, Nay Win, Aung, Nilar, Myint, Zin Nwe and Kraas, Frauke ORCID: 0000-0002-3498-6758 (2022). Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974-2021) Using a Hybrid Ensemble Learning Model. Remote Sens., 14 (15). BASEL: MDPI. ISSN 2072-4292
Full text not available from this repository.Abstract
Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world's most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure and vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.
Item Type: | Journal Article | ||||||||||||||||||||||||||||||||||||||||
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URN: | urn:nbn:de:hbz:38-674004 | ||||||||||||||||||||||||||||||||||||||||
DOI: | 10.3390/rs14153568 | ||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Remote Sens. | ||||||||||||||||||||||||||||||||||||||||
Volume: | 14 | ||||||||||||||||||||||||||||||||||||||||
Number: | 15 | ||||||||||||||||||||||||||||||||||||||||
Date: | 2022 | ||||||||||||||||||||||||||||||||||||||||
Publisher: | MDPI | ||||||||||||||||||||||||||||||||||||||||
Place of Publication: | BASEL | ||||||||||||||||||||||||||||||||||||||||
ISSN: | 2072-4292 | ||||||||||||||||||||||||||||||||||||||||
Language: | English | ||||||||||||||||||||||||||||||||||||||||
Faculty: | Unspecified | ||||||||||||||||||||||||||||||||||||||||
Divisions: | Unspecified | ||||||||||||||||||||||||||||||||||||||||
Subjects: | no entry | ||||||||||||||||||||||||||||||||||||||||
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URI: | http://kups.ub.uni-koeln.de/id/eprint/67400 |
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