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
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
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. / Meyer, Rasmus P.; Søgaard, Mikkel G.; Schødt, Mathias P.; Horion, Stéphanie; Prishchepov, Alexander V.
In: BSGLg, Vol. 78, No. 1, 2022, p. 123-138.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - 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
AU - Meyer, Rasmus P.
AU - Søgaard, Mikkel G.
AU - Schødt, Mathias P.
AU - Horion, Stéphanie
AU - Prishchepov, Alexander V.
N1 - Publisher Copyright: © 2022 Societe Geographique de Liege. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Google Earth Engine
KW - land cover classification
KW - machine learning
KW - random forest
KW - Remote sensing
KW - SAR
KW - support vector machines
U2 - 10.25518/0770-7576.6653
DO - 10.25518/0770-7576.6653
M3 - Journal article
AN - SCOPUS:85135032025
VL - 78
SP - 123
EP - 138
JO - BSGLg
JF - BSGLg
SN - 0583-8622
IS - 1
ER -
ID: 317730456