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

Abstract

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.

Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia

“…In recent times, due to the need to handle the complex data required for flood vulnerability analysis in a more efficient way and account for likely uncertainties, various advanced machine learning algorithms and statistical models such as multivariate regression models have been explored [27]. Other studies have used support vector machine models [28], ANN model [29], ANFIS [30], a combination of fuzzy logic and support vector machine [31], integrated machine learning and statistical models [32], DT algorithm [33], logistic regression [34], and an ensemble of regression trees and support vector machine [35]. Despite the recent focus on quantitative methods of flood vulnerability analysis, qualitative approaches such as the AHP and ANP are still being adopted [36,37] due to their relative simplicity in computing flood influencing parameters.…”

Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis

“…Many studies using EO data for North Korea have been proposed for monitoring land use and land cover (LULC) over the Remote Sens. 2020, 12, 255 7 of 26 past several decades (for example, [51][52][53][54][55]). Furthermore, a number of government institutes and think-tanks have already established different types of thematic maps in North Korea using EO data (e.g., agricultural maps; deforestation maps; land cover maps, etc.).…”

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

“…Baekdu is the highest mountain on the Korean peninsula. The administrative area is bordered by North Korea's Yanggang Province and China's Jilin Province, with a total area of 8000 km 2 . The climate is a typical alpine climate and experiences severe climatic changes.…”

“…North Korea is suffering from extreme forest degradation due to food and energy shortages [1][2][3]. Degraded and deforested lands are vulnerable to natural disasters, such as landslides and floods, which not only cause environmental damage, but also destroy agricultural infrastructure [1][2][3]. This results in a vicious cycle of degradation in the forest by woodcutting to address food and fuel shortages.…”