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Correction published on 8 January 2015, see Remote Sens. 2015, 7(1), 666-667.

Open AccessArticle
Remote Sens. 2014, 6(9), 8026-8055; doi:10.3390/rs6098026

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

1
GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany
2
Geoinformation in Environmental Planning Lab, Department of Landscape Architecture and Environmental Planning, TU Berlin, Straße des 17. Juni 145, D-10623 Berlin, Germany
*
Author to whom correspondence should be addressed.
Received: 28 May 2014 / Revised: 31 July 2014 / Accepted: 11 August 2014 / Published: 27 August 2014
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Abstract

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale. View Full-Text
Keywords: landslide inventory; optical remote sensing; time series analysis; object-oriented analysis; digital elevation model; Kyrgyzstan landslide inventory; optical remote sensing; time series analysis; object-oriented analysis; digital elevation model; Kyrgyzstan
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Behling, R.; Roessner, S.; Kaufmann, H.; Kleinschmit, B. Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data. Remote Sens. 2014, 6, 8026-8055.

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