Flood Mapping Using Multi-Source Remotely Sensed Data and Logistic Regression in the Heterogeneous Mountainous Regions in North Korea

• Main Authors: Joongbin Lim, Kyoo-seock Lee
• Format: Article
• Language: English
• Published: MDPI AG 2018-07-01
• Series: Remote Sensing
• Subjects: floodplain delineation; inaccessible region; machine learning
• Online Access: http://www.mdpi.com/2072-4292/10/7/1036

Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced extensive damage to life and property almost every year due to a levee breach resulting from typhoons and heavy rainfall during the summer monsoon season. Recently, Hoeryeong City (2016) experienced heavy rain during Typhoon Lionrock, and the resulting flood killed and injured many people (68,900) and destroyed numerous buildings and settlements (11,600). The NK state media described it as the most significant national disaster since 1945. Thus, almost all annual repeat occurrences of floods in NK have had a severe impact, which makes it necessary to figure out the extent of floods to restore the damaged environment. However, this is difficult due to inaccessibility. Under such a situation, optical remote sensing (RS) data and radar RS data along with a logistic regression were utilized in this study to develop modeling for flood-damaged area delineation. High-resolution web-based satellite imagery was also interpreted to confirm the results of the study.