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Geosciences 2017, 7(2), 37; doi:10.3390/geosciences7020037

Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors

1
Department of Geoinformatics–Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
2
GRID-IT—Gesellschaft für angewandte Geoinformatik mbH, Technikerstraße 21a, 6020 Innsbruck, Austria
3
Geological Survey of Austria (GBA), Neulinggasse 38, 1030 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Francesca Cigna and Jesus Martinez-Frias
Received: 31 March 2017 / Revised: 10 May 2017 / Accepted: 11 May 2017 / Published: 19 May 2017
(This article belongs to the Special Issue Observing Geohazards from Space)
View Full-Text   |   Download PDF [21658 KB, uploaded 19 May 2017]   |  

Abstract

Object-based image analysis (OBIA) has been increasingly used to map geohazards such as landslides on optical satellite images. OBIA shows various advantages over traditional image analysis methods due to its potential for considering various properties of segmentation-derived image objects (spectral, spatial, contextual, and textural) for classification. For accurately identifying and mapping landslides, however, visual image interpretation is still the most widely used method. The major question therefore is if semi-automated methods such as OBIA can achieve results of comparable quality in contrast to visual image interpretation. In this paper we apply OBIA for detecting and delineating landslides in five selected study areas in Austria and Italy using optical Earth Observation (EO) data from different sensors (Landsat 7, SPOT-5, WorldView-2/3, and Sentinel-2) and compare the OBIA mapping results to outcomes from visual image interpretation. A detailed evaluation of the mapping results per study area and sensor is performed by a number of spatial accuracy metrics, and the advantages and disadvantages of the two approaches for landslide mapping on optical EO data are discussed. The analyses show that both methods produce similar results, whereby the achieved accuracy values vary between the study areas. View Full-Text
Keywords: landslides; remote sensing; semi-automated mapping; object-based image analysis (OBIA); manual mapping; visual interpretation; spatial accuracy metrics; Alps landslides; remote sensing; semi-automated mapping; object-based image analysis (OBIA); manual mapping; visual interpretation; spatial accuracy metrics; Alps
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hölbling, D.; Eisank, C.; Albrecht, F.; Vecchiotti, F.; Friedl, B.; Weinke, E.; Kociu, A. Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors. Geosciences 2017, 7, 37.

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