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A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories
- Source :
- Remote Sensing, Vol 4, Iss 5, Pp 1310-1336 (2012)
- Publication Year :
- 2012
- Publisher :
- MDPI AG, 2012.
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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.
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 4
- Issue :
- 5
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f8c82a7b504ea58ab2d1e6811900bc
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs4051310