MORPHOLOGICAL ANALYSIS OF LANDSLIDES IN EXTREME TOPOGRAPHY BY UAS-SfM: DATA ACQUISITION, 3D MODELS AND CHANGE DETECTION

Landslides are one major kind of natural disasters and geomorphological processes on Earth’s surface. Accurate geodetic observations are crucial for understanding morphological changes, providing a quantitative basis of further research in surface process and hazard management. In recent years, the...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B5-2020; pp. 173 - 178
Main Authors Yeh, T. W., Chuang, R. Y.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 24.08.2020
Copernicus Publications
Subjects
Change detection
Data acquisition
Disasters
Earth surface
Geomorphology
Landforms
Landslides
Landslides & mudslides
Mathematical models
Morphology
Natural disasters
Three dimensional models
Topography
Workflow
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Summary:Landslides are one major kind of natural disasters and geomorphological processes on Earth’s surface. Accurate geodetic observations are crucial for understanding morphological changes, providing a quantitative basis of further research in surface process and hazard management. In recent years, the development of UAVs and SfM technology enhance research to build high quality digital surface models of landforms with low budget and efficiency. In areas of extreme topography where landslides occur on steep slopes, however, it is required to specifically design the UAV-SfM workflow to keep the data quality. This study aims to use UAS-SfM workflow to develop a low-cost, efficient methodology to detect detailed morphological change of landslide morphology in extreme topography. The study focuses on examining results of different flight design and GCPs distribution geometry, which are important components in the workflow. In addition, we applied a mathematical model to compare point clouds to calculate volumetric change of the landslide with reduced distortion.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B5-2020-173-2020