Flood damage assessment using satellite observations within the google earth engine cloud platform

verfasst von

Mehdi Sharifipour, Meisam Amani, Armin Moghimi

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.

Externe Organisation(en)

Tadbir Kesht Golestan Company
Wood Environment & Infrastructure Solutions
K.N. Toosi University of Technology

Typ

Artikel

Journal

Journal of Ocean Technology

Band

17

Seiten

64-75

Anzahl der Seiten

12

ISSN

1718-3200

Publikationsdatum

01.03.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Meerestechnik

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land