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Geosciences 2018, 8(1), 23; https://doi.org/10.3390/geosciences8010023

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

1
Civil Engineering Department, California State Polytechnic University, Pomona, Pomona, CA 91768, USA
2
Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA)—CONICET, 5500 Mendoza, Argentina
3
Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
4
Department of Geography, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Received: 5 December 2017 / Revised: 10 January 2018 / Accepted: 13 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Natural Hazards and Risks Assessment)
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Abstract

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides. View Full-Text
Keywords: multi-temporal; LiDAR; landslide; change detection; DEM; feature extraction multi-temporal; LiDAR; landslide; change detection; DEM; feature extraction
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Mora, O.E.; Lenzano, M.G.; Toth, C.K.; Grejner-Brzezinska, D.A.; Fayne, J.V. Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs. Geosciences 2018, 8, 23.

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