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Titel
A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories
VerfasserHölbling, Daniel ; Füreder, Petra ; Antolini, Francesco ; Cigna, Francesca ; Casagli, Nicola ; Lang, Stefan In der Gemeinsamen Normdatei der DNB nachschlagen
Erschienen in
Remote Sensing, Basel, 2012, Jg. 2012, H. 4, S. 1310-1336
ErschienenMDPI, 2012
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)object-based image analysis (OBIA) / landslide mapping / persistent scatterers (PS) / radar-interpretation / validation
URNurn:nbn:at:at-ubs:3-1125 Persistent Identifier (URN)
DOI10.3390/rs4051310 
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 Das Werk ist frei verfügbar
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A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories [8.44 mb]
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Zusammenfassung (Englisch)

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 km was detected as landslide-affected area, 1.68 km were classified as flow-like landslides and 2.09 km 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.

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