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.
Highlights
IntroductionGravitational mass movements such as landslides constitute a major natural hazard in all hilly or mountainous regions throughout the world
An area of 3.77 km2 was classified as landslide-affected; comprising an area of 1.68 km2 flow-like landslides and 2.09 km2 classified as other landslide types
The presented approach demonstrates how landslide mapping can be improved and existing landslide inventories updated using a combination of object-based image analysis and radar-interpretation of ENVISAT and RADARSAT measures
Summary
Gravitational mass movements such as landslides constitute a major natural hazard in all hilly or mountainous regions throughout the world. These movements are mostly a very local phenomenon, they cause damage to all types of man-made structures and affect infrastructures from local to regional scales and even on a national scale. 1,765 persons and ranked in the top 10 of the most important disasters by number of persons killed [1] Landslide triggering conditions, such as heavy rain falls and typhoons or earthquakes, can affect very large areas and sometimes cause several thousand landslides per event; for example Tsai et al [2]
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