Advanced Detection and Mapping of Landslides Using Interferometric SAR Imagery: A Case Study of the July 2024 Event in Wayanad

Author:

V Vineesh1ORCID

Affiliation:

1. PG and Research Department of Geography, Government College Chittur, Palakkad

Abstract

Abstract

Landslides, particularly in mountainous regions like Wayanad, India, present significant risks to human life, infrastructure, and the environment. The rapid and accurate detection of such events is critical for effective disaster response and risk mitigation. This study investigates the application of Interferometric Synthetic Aperture Radar (InSAR) for detecting and mapping a landslide that occurred on July 30, 2024, in Wayanad. Utilizing Sentinel-1 SAR imagery, this research highlights the advantages of InSAR in monitoring ground deformation over time, particularly in challenging terrains characterized by dense vegetation and steep slopes. The methodology involves analyzing the phase differences between pre- and post-event SAR images to detect surface displacement, enabling the precise delineation of affected areas. The study further enhances the interpretability of results by generating an interactive map using the Folium library, which visually represents the extent of the landslide through a differential interferogram with a customized color palette. This approach not only improves understanding of landslide dynamics but also provides valuable insights for decision-makers involved in disaster management. The findings underscore the potential of SAR-based techniques in advancing landslide detection and risk mitigation, thereby contributing to more resilient strategies in vulnerable regions like Wayanad.

Publisher

Springer Science and Business Media LLC

Reference9 articles.

1. Vineesh V (2024) Google Colab. August 17, https://colab.research.google.com/drive/1SP0fK1p_ekCjPahKLZR-5kZRk5rIxd5T?usp=sharing

2. European Space Agency (2024) Sentinel-1 GRD: SAR Data from the Copernicus Mission. Copernicus Open Access Hub. Accessed August 12, https://scihub.copernicus.eu/dhus

3. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone;Gorelick N;Remote Sens Environ

4. Folium: Python Data, Leaflet.js Maps. Folium. Accessed August 12 (2024) https://python-visualization.github.io/folium/

5. Google Developers (2024) Earth Engine Python API. Google Earth Engine Documentation. Accessed August 12, https://developers.google.com/earth-engine/python_install

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