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

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  • Kieu Anh Nguyen

    (Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Yi-Jia Jiang

    (Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Walter Chen

    (Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

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.

Suggested Citation

  • Kieu Anh Nguyen & Yi-Jia Jiang & Walter Chen, 2025. "Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI," Sustainability, MDPI, vol. 17(16), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7428-:d:1726234
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    References listed on IDEAS

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    1. Miqi Guo & Chaodong Gou & Shucheng Tan & Churan Feng & Fei Zhao, 2024. "Spatiotemporal Characteristics and Factors Influencing the Cycling Behavior of Shared Electric Bike Use in Urban Plateau Regions," Sustainability, MDPI, vol. 16(15), pages 1-18, July.
    2. Jiayan Kong & Yinghe An & Xian Shi & Zhongyi Sun & Lan Wu & Wei Cui, 2023. "Meteorological-Data-Driven Rubber Tree Powdery Mildew Model and Its Application on Spatiotemporal Patterns: A Case Study of Hainan Island," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    3. Minkyung Kim & Sangdon Lee, 2023. "Identification of Emerging Roadkill Hotspots on Korean Expressways Using Space–Time Cubes," IJERPH, MDPI, vol. 20(6), pages 1-12, March.
    4. Muhammad Younas & Shuhab D. Khan & Muhammad Qasim & Younes Hamed, 2022. "Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis," Land, MDPI, vol. 11(12), pages 1-20, December.
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