Details
| Original language | English |
|---|---|
| Pages (from-to) | 64-75 |
| Number of pages | 12 |
| Journal | Journal of Ocean Technology |
| Volume | 17 |
| Issue number | 1 |
| Publication status | Published - 1 Mar 2022 |
| Externally published | Yes |
Abstract
Floods cause significant damages to different assets every year and, thus, it is important to monitor floods and assess their damage using advanced technologies. In this regard, remote sensing systems, which provide frequent and consistent observations over large areas with minimum cost, are great resources. In this study, we developed a method to assess the damages to different Land Use/Land Cover (LULC) types caused by floods in the three countries of Iran, Ireland, and Sweden. The amount of flood damage to different LULCs was investigated using the flooded areas reported in the Emergency Management Service (EMS) and the generated LULC maps using the Support Vector Machine (SVM) algorithm and Sentinel satellite data within the Google Earth Engine (GEE) cloud computing platform. Overall Accuracies (OAs) for the LULC maps of Iran, Ireland, and Sweden were 84%, 88%, and 70%, respectively. The experimental results showed that cropland and barrens with 25,099 and 17,164 flooded areas were the most damaged LULC classes, respectively. The amount of damage for the tree class was 3,949 hectares.
Keywords
- Flood, Google Earth Engine, Land cover, Machine learning, Sentinel
ASJC Scopus subject areas
- Engineering(all)
- Ocean Engineering
Sustainable Development Goals
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In: Journal of Ocean Technology, Vol. 17, No. 1, 01.03.2022, p. 64-75.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Flood damage assessment using satellite observations within the google earth engine cloud platform
AU - Sharifipour, Mehdi
AU - Amani, Meisam
AU - Moghimi, Armin
N1 - Publisher Copyright: © Journal of Ocean Technology 2022.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Floods cause significant damages to different assets every year and, thus, it is important to monitor floods and assess their damage using advanced technologies. In this regard, remote sensing systems, which provide frequent and consistent observations over large areas with minimum cost, are great resources. In this study, we developed a method to assess the damages to different Land Use/Land Cover (LULC) types caused by floods in the three countries of Iran, Ireland, and Sweden. The amount of flood damage to different LULCs was investigated using the flooded areas reported in the Emergency Management Service (EMS) and the generated LULC maps using the Support Vector Machine (SVM) algorithm and Sentinel satellite data within the Google Earth Engine (GEE) cloud computing platform. Overall Accuracies (OAs) for the LULC maps of Iran, Ireland, and Sweden were 84%, 88%, and 70%, respectively. The experimental results showed that cropland and barrens with 25,099 and 17,164 flooded areas were the most damaged LULC classes, respectively. The amount of damage for the tree class was 3,949 hectares.
AB - Floods cause significant damages to different assets every year and, thus, it is important to monitor floods and assess their damage using advanced technologies. In this regard, remote sensing systems, which provide frequent and consistent observations over large areas with minimum cost, are great resources. In this study, we developed a method to assess the damages to different Land Use/Land Cover (LULC) types caused by floods in the three countries of Iran, Ireland, and Sweden. The amount of flood damage to different LULCs was investigated using the flooded areas reported in the Emergency Management Service (EMS) and the generated LULC maps using the Support Vector Machine (SVM) algorithm and Sentinel satellite data within the Google Earth Engine (GEE) cloud computing platform. Overall Accuracies (OAs) for the LULC maps of Iran, Ireland, and Sweden were 84%, 88%, and 70%, respectively. The experimental results showed that cropland and barrens with 25,099 and 17,164 flooded areas were the most damaged LULC classes, respectively. The amount of damage for the tree class was 3,949 hectares.
KW - Flood
KW - Google Earth Engine
KW - Land cover
KW - Machine learning
KW - Sentinel
UR - http://www.scopus.com/inward/record.url?scp=85132128279&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85132128279
VL - 17
SP - 64
EP - 75
JO - Journal of Ocean Technology
JF - Journal of Ocean Technology
SN - 1718-3200
IS - 1
ER -