Topography-driven satellite imagery analysis for landslide mapping

We describe a semi-automatic procedure for the classification of satellite imagery into landslide or no landslide categories, aimed at preparing event landslide inventory maps. The two-steps procedure requires knowledge of the occurrence of a landslide event, availability of a pre- and post- event p...

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Bibliographic Details
Published in:Geomatics, natural hazards and risk natural hazards and risk, 2018-01, Vol.9 (1), p.544-567
Main Authors: Alvioli, M., Mondini, A. C., Fiorucci, F., Cardinali, M., Marchesini, I.
Format: Article
Language:English
Subjects:
Change detection
Classification
Digital Elevation Models
Discriminant analysis
discriminant function
Ground truth
Image classification
Imagery
landslide event
landslide mapping
Landslides
Mapping
Satellite imagery
Satellites
slope units
Spaceborne remote sensing
Spectral sensitivity
Subdivisions
Topography
Topography (geology)
Training
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Summary:We describe a semi-automatic procedure for the classification of satellite imagery into landslide or no landslide categories, aimed at preparing event landslide inventory maps. The two-steps procedure requires knowledge of the occurrence of a landslide event, availability of a pre- and post- event pseudo-stereo pair and a digital elevation model. The first step consists in the evaluation of a discriminant function, applied to a combination of well-known change detection indices tuned on landslide spectral response. The second step is devoted to discriminant function classification, aimed at distinguishing the only landslide class, through an improvement of the usual 'thresholding' method. We devised a multi-threshold classification, in which thresholding is applied separately in small subsets of the scene. We show that using slope units as topographic-aware subsets produces best classification performance when compared to the ground truth of a landslide inventory prepared by visual interpretation. The method proved to be superior to the use of a single threshold and to any multi-threshold procedure based on topography-blind subdivisions of the scene, especially in the validation stage. We argue that the improved classification performance and limited training requirements represent a step forward towards an automatic, real-time landslide mapping from satellite imagery.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2018.1458050