Investigation of the Effect of the Dataset Size and Type in the Earthquake-Triggered Landslides Mapping: A Case Study for the 2018 Hokkaido Iburu Landslides

Resul Comert

doi:10.3389/feart.2021.633665

Abstract

Rapid mapping of landslides that occur after an earthquake is important for rapid crisis management. In this study, experimental research was conducted on the size of the model area and the data types used in developing classifiers for the supervised classification approaches used in rapid landslide mapping. The Hokkaido Iburu earthquake zone that occurred on September 6, 2018, was selected as the study area. PlanetScope pre-event and post-event images and ALOS-PALSAR Digital Elevation Model (DEM) were used in the analysis processes. In this context, five model areas with different sizes and one test area were determined. Object-based image analysis (OBIA) was used as a landslide mapping approach. Random Forest classifier, which is a supervised classification algorithm, was performed in the mapping of image objects produced by the segmentation stage of OBIA. Two different data sets were created for landslide mapping: change-based dataset and post-event dataset. The change-based dataset is generated from change data such as the difference of normalized difference vegetation index (δNDVI), change detection Image (CDI), princiable component analysis (PCA), and Independent component analysis (ICA) which are used in change detection applications. The post-event dataset was created from data generated from post-event image bands. When the obtained results were examined, higher accuracy results were obtained with the post-event dataset. Increasing the size of the model area, in other words, increasing the training data slightly increases the accuracy of landslide mapping. However, a model area that represents the region to be mapped in small sizes to make rapid decisions provides a 94% F-measure accuracy for earthquake-triggered landslide detection.

Highlights

Earthquakes are natural events that cause great damage to nature, buildings, engineering structures, and cause human death (Gorum and Carranza 2015)
The success of different datasets and different data sizes produced from remote sensing images in rapid earthquaketriggered landslide mapping were investigated
Change-based and post-event datasets were created for five model areas and one test area

Summary

Introduction

Earthquakes are natural events that cause great damage to nature, buildings, engineering structures, and cause human death (Gorum and Carranza 2015). Moderate and severe magnitude earthquakes trigger thousands of landslides, especially in rugged and high-slope mountainous regions (Gorum et al, 2013; Tanyas et al, 2017). Landslides that appear as a secondary effect of earthquakes cause human deaths and economic losses. Fatal landslides occurred in 76 of the 196 earthquakes between 1811 and 2016. These fatalities correspond to 17.7% (213,913 people) of deaths caused by earthquakes (Jessee et al, 2020). Earthquaketriggered landslides accounted for 5.2% ($ 170 billion) of economic damage from earthquakes between 1900 and 2016 (Daniell et al, 2017). Rapid mapping of landslides is important for rapid response to disaster areas and crisis management

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Conclusion