Paper
10 November 2004 Change detection of man-induced landslide causal factors
Author Affiliations +
Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.565811
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain
Abstract
In the framework of the EU project titled: Landslide Early Warning Integrated project (LEWIS) optical RS data have been periodically processed to detect surface features changes which can be correlated with the development of slope instability mechanisms. The attention is focused on man's activity induced surface features changes, such as deforestation and ploughing, which affects slope equilibrium conditions by decreasing the effective slope shear strength and increasing the slope shear stress, respectively. Fourteen optical Landsat TM images (two per year), has been analysed on the Caramanico test site in Regione Abruzzo, Southern Italy. The main objective of the work was to verify the advantages and limitations of conventional space-borne RS data for the prevention of landslide events. The data were analysed by supervised classifier based on neural network techniques. Four classes and their transitions were considered in the analysis. Supervised techniques were preferred to unsupervised techniques because the former can provide useful information not only on the place were a transition occurred, but also on the specific classes involved in the transition between two dates. The results seem to show that in years 1987-2000 the following surface class changes, potentially related to landslide phenomena, occurred: i) a strong decrease of arboreous land in agricultural land and an increase of barren land, mainly in the area interested by landslides events; ii) an increase of artificial structures, mainly stemming from a transformation of cultivated areas.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cristina Tarantino, Palma N. Blonda, and Guido Pasquariello "Change detection of man-induced landslide causal factors", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); https://doi.org/10.1117/12.565811
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Cited by 2 scholarly publications.
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KEYWORDS
Landslide (networking)

Agriculture

Remote sensing

Earth observing sensors

Landsat

Neural networks

Spatial resolution

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