Certainty Factor Analyses and Spatiotemporal Characteristics of Landslide Evolution: Case Studies in the Chishan River Watershed in Taiwan

Abstract

The 1999 Chichi earthquake and Typhoon Morakot in 2009 caused two serious landslide events in the Chishan river watershed in southern Taiwan. In this study, certainty factor analysis was used to evaluate the effectiveness of landslide occurrence, and spatiotemporal hotspot analysis was used to explain the pattern and distribution of landslide hotspots. The Z-values from the Getis–Ord formula were used to assess the clustering strength of landslide evolution on different scales and with different landslide sizes in different time periods. The landslide-prone area had an elevation of 1000–1750 m, a slope of >40°, and hillslopes with N, NE, E, and SE aspects and was within 100 m of rivers. The main spatiotemporal hotspot patterns of landslide evolution during 1999–2017 were oscillating hotspots, intensifying hotspots, and persistent hotspots, and the three main hotspot patterns occupied 80.1–89.4% of all hotspot areas. The main spatiotemporal landslide hotspots were concentrated in the core landslide areas and the downslopes of riverbank landslide areas, especially in the upstream subwatersheds. The landslide clustered strength in the upstream watershed was 3.4 times larger than that in the Chishan river watershed, and that in large landslides was 2.4 and 6.6 times larger than those in medium and small landslides, respectively.

Lightweight Attention-Guided YOLO With Level Set Layer for Landslide Detection From Optical Satellite Images

“…Taiwan locates in the southeast of China, and is an island with an area of 36,000 km2. The region is one of the most landslide-prone areas in the world due to many factors, such as heavy rains, steep topography, weak geological formations, strong earthquakes, loose soils, human activities [32,33]. We > IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING< used Google Earth Engine to download Sentinel-2 images of Taiwan from January 2022 to January 2023.…”

National-scale spatiotemporal patterns of vegetation fire occurrences using MODIS satellite data

“…Emerging hotspot analysis is an approach used for statistical spatiotemporal analysis of phenomena. Recently, the approach has been used to detect spatiotemporal patterns of COVID-19 cases [13], landslides [14], invasive species [15] and forest loss [12,[16][17][18]. A few studies were done in Australia [19] and southeast Asia [5,20], utilizing the emerging hotspot analysis method to understand the spatiotemporal patterns of fires.…”