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Geometrical Variation Analysis of Landslides in Different Geological Settings Using Satellite Images: Case Studies in Japan and Sri Lanka.

Authors :
Neranjan, Suneth
Uchida, Taro
Yamakawa, Yosuke
Hiraoka, Marino
Kawakami, Ai
Source :
Remote Sensing; May2024, Vol. 16 Issue 10, p1757, 19p
Publication Year :
2024

Abstract

Over the past three decades, Sri Lanka has observed a substantial rise in landslide occurrences linked to intensified rainfall. However, the lack of comprehensive landslide inventories has hampered the development of effective risk analysis and simulation systems, requiring Sri Lanka to rely heavily on foreign-developed models, despite the difficulty of fully examining the similarities between the characteristics of landslides in Sri Lanka and the areas where the model has been developed. Satellite images have become readily available in recent years and have provided information about the Earth's surface conditions over the past few decades. Thus, this study verifies the utility of satellite images as a cost-effective remote-sensing method to clarify the commonalities and differences in the characteristics of landslides in two regions Ikawa, Japan, and Sabaragamuwa, Sri Lanka, which exhibit different geological formations despite similar annual rainfall. Using Google Earth satellite images from 2013 to 2023, we evaluated land-slide density, types, and geometry. The findings reveal that Ikawa exhibits a higher landslide density and experiences multiple-type landslides. In contrast, both areas have similar initiation areas; however, Sabaragamuwa predominantly experiences single landslides that are widespread and mobile. The findings also reveal that various characteristics of landslides are mainly influenced by varied topography. Here, we confirmed that even in areas where comprehensive information on landslides is conventionally lacking, we can understand the characteristics of landslides by comparing landslide geometry between sites using satellite imagery. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
10
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
177496957
Full Text :
https://doi.org/10.3390/rs16101757