A semi-automated object-based approach for landslide detection validated by Persistent Scatterer Interferometry measures and landslide inventories
Holbling, Daniel; Fureder, 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 Sensing, 4 (5). 1310-1336. 10.3390/rs4051310Before downloading, please read NORA policies.
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.
|Item Type:||Publication - Article|
|Digital Object Identifier (DOI):||10.3390/rs4051310|
|Programmes:||BGS Programmes 2010 > Land Use, Planning and Development|
|Additional Information. Not used in RCUK Gateway to Research.:||Remote Sensing (ISSN 2072-4292), an open access journal about the science and application of remote sensing technology, is published by MDPI online monthly.|
|Date made live:||27 Jun 2012 13:16|
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