EMERGENCY FLOOD MAPPING IN AUSTRALIA WITH SENTINEL-1 AND SENTINEL-2 SATELLITE IMAGERY = CARTOGRAPHIE D'URGENCE DES INONDATIONS EN AUSTRALIE À PARTIR D'IMAGES SATELLITAIRES SENTINEL-1 ET SENTINEL-2
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Timely inputs for spatial planning are essential to support decisions about preventive or damage controlling measures, including flood. Climate change predictions suggest more frequent floods in the future, implying a need for flood mapping. The objectives of the study were to evaluate the suitability of Sentinel-1 SAR data to map the extent of flood and to explore how land cover classification through different machine learning techniques and optical Sentinel-2 imagery can be applied as an emergency mapping tool. The Australian floods in March 2021 were used as a case study. Google Earth Engine was used to process and classify the flood extent and affected land cover types. Our study revealed the great suitability of Sentinel-1 SAR data for emergency mapping of flooded areas. Furthermore, land cover maps were produced using random forest (RD) and support vector machines (SVM) on optical Sentinel-2 Imagery. The presented workflow can be implemented in other parts of the world for the rapid assessment of flooded areas.
Originalsprog | Engelsk |
---|---|
Tidsskrift | BSGLg |
Vol/bind | 78 |
Udgave nummer | 1 |
Sider (fra-til) | 123-138 |
Antal sider | 16 |
ISSN | 0770-7576 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Publisher Copyright:
© 2022 Societe Geographique de Liege. All rights reserved.
- Google Earth Engine, land cover classification, machine learning, random forest, Remote sensing, SAR, support vector machines
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