Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-5838
Main Title: Automated spatiotemporal landslide mapping over large areas using RapidEye time series data
Author(s): Behling, Robert
Roessner, Sigrid
Kaufmann, Hermann
Kleinschmit, Birgit
Type: Article
Language Code: en
Is Part Of: http://dx.doi.org/10.14279/depositonce-5525
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.
URI: http://depositonce.tu-berlin.de/handle/11303/6279
http://dx.doi.org/10.14279/depositonce-5838
Issue Date: 2014
Date Available: 13-Apr-2017
DDC Class: DDC::500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::550 Geowissenschaften
Subject(s): landslide inventory
optical remote sensing
time series analysis
object-oriented analysis
digital elevation model
Kyrgyzstan
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Remote sensing
Publisher: MDPI
Publisher Place: Basel
Volume: 6
Issue: 9
Publisher DOI: 10.3390/rs6098026
Page Start: 8026
Page End: 8055
ISSN: 2072-4292
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 6 Planen Bauen Umwelt » Institut für Landschaftsarchitektur und Umweltplanung » Publications

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