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A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories
Z_GIS Centre for Geoinformatics, Salzburg University, Schillerstrasse 30, A-5020 Salzburg, Austria
Department of Earth Sciences, University of Firenze, Via La Pira 4, I-50121 Firenze, Italy
Present address: Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy.
Present address: British Geological Survey, Nicker Hill, Keyworth, Nottingham NG12 5GG, UK.
* Author to whom correspondence should be addressed.
Received: 29 March 2012; in revised form: 27 April 2012 / Accepted: 28 April 2012 / Published: 7 May 2012
Abstract: Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.
Keywords: object-based image analysis (OBIA); landslide mapping; persistent scatterers (PS); radar-interpretation; validation
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Hölbling, D.; Füreder, P.; Antolini, F.; Cigna, F.; Casagli, N.; Lang, S. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories. Remote Sens. 2012, 4, 1310-1336.
Hölbling D, Füreder P, Antolini F, Cigna F, Casagli N, Lang S. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories. Remote Sensing. 2012; 4(5):1310-1336.
Hölbling, Daniel; Füreder, Petra; Antolini, Francesco; Cigna, Francesca; Casagli, Nicola; Lang, Stefan. 2012. "A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories." Remote Sens. 4, no. 5: 1310-1336.