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Article

Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI

Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
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Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7428; https://doi.org/10.3390/su17167428 (registering DOI)
Submission received: 25 July 2025 / Revised: 9 August 2025 / Accepted: 14 August 2025 / Published: 17 August 2025
(This article belongs to the Special Issue Landslide Hazards and Soil Erosion)

Abstract

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems.
Keywords: landslide susceptibility mapping; EHSA (Emerging Hot Spot Analysis); Mann–Kendall trend test; spatiotemporal analysis; vegetation dynamics; debris flow hazard; Taiwan mountainous regions landslide susceptibility mapping; EHSA (Emerging Hot Spot Analysis); Mann–Kendall trend test; spatiotemporal analysis; vegetation dynamics; debris flow hazard; Taiwan mountainous regions

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MDPI and ACS Style

Nguyen, K.A.; Jiang, Y.-J.; Chen, W. Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI. Sustainability 2025, 17, 7428. https://doi.org/10.3390/su17167428

AMA Style

Nguyen KA, Jiang Y-J, Chen W. Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI. Sustainability. 2025; 17(16):7428. https://doi.org/10.3390/su17167428

Chicago/Turabian Style

Nguyen, Kieu Anh, Yi-Jia Jiang, and Walter Chen. 2025. "Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI" Sustainability 17, no. 16: 7428. https://doi.org/10.3390/su17167428

APA Style

Nguyen, K. A., Jiang, Y.-J., & Chen, W. (2025). Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI. Sustainability, 17(16), 7428. https://doi.org/10.3390/su17167428

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