Abstract
Slope instability studies appear to recognize a number of potential superficial slide-producing agents, which may be directly detected and monitored with Earth Observation (EO) data. The main objective of this work is to use conventional EO data and automatic techniques for providing land-use change maps useful in landslide prevention. The idea is to use the detection of changes in areas already involved in landslide events as a precursory sign of variations in the equilibrium status of the slope, independently from other natural triggering events, such as rain and seismic events. Attention is focused on man-induced surface changes, such as deforestation, urban expansion and construction of artificial structures.
A historical set of 20 multi-temporal Landsat TM images, covering the period 1987–2000, was analyzed using a supervised change detection technique on a test site affected by slope instability phenomena located in the Abruzzo region in Southern Italy. A change image is obtained by comparing year-specific thematic map pairs. It contains useful information not only on the place where a transition occurred, but also on the specific classes involved in the transitions between two different years. The full set of change images is used to extract class-conditional transition probabilities, to evaluate variations in specific class distribution and the total number of changed pixels in time. Four classes and their transitions were considered in the analysis: (1) arboreous land, (2) agricultural land, (3) barren land, and (4) artificial structures.
The quantitative analysis of the class-joint transition probability values of some specific class-transitions that may worsen slope stability showed that in an area prone to landslides the probability of landslide re-activation or first activation is higher where changes have occurred. Although based on a limited number of known events, such a result encourages extensive experimentation of the proposed technique on better documented landslide test sites.
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References
Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition-Part I. IEEE Trans System Man Cybernet B: Cybernet 29(6):778–785
Baraldi A, Bruzzone L, Blonda P (2005) Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Trans Geosci Remote Sens 43(4):857–873
Baraldi A, Puzzolo V, Blonda P, Buzzone L, Tarantino C (2006) Automatic spectral rule-based preliminary mapping of calibrated Landsat Tm and ETM+ images. IEEE Trans Geosci Remote Sens (in press)
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press
Blonda P, La Forgia V, Pasquariello G, Satalino G (1996) Feature extraction and pattern classification of remote sensing data by a modular neural system. Optical Eng 35(2):536–542
Bovenga F, Nutricato R, Refice A, Wasowski J (2006) Application of multitemporal differential interferometry analysis for detecting slope instability in urban/peri-urban areas. Eng Geol. special issue on “Remote sensing and ground-based geophysical techniques for recognition, characterisation and monitoring of unstable slopes” (in press)
Bruzzone L, Prieto DF, Serpico SB (1999) A neural-statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Trans Geosci Remote Sens 37(3):1350–1359
Bruzzone L, Prieto DF (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens 38:1171–1182
Castellana L, Tarantino C, Blonda P, Pasquariello G (2004) Bayesian approach to land cover change detection using a priori scene description. In: Proc. IEEE International Geosc. and Remote Sensing Symposium 2004, Anchorage, Alaska, 20–26 September 2004, IEEE Catalog Number: 04CH37612C (CD-ROM)
CEOS Disaster Management Support Group, Landslide hazards (2002) Earth observation for landslide hazard support in the use of earth observing satellites for hazard support: assessments & scenarios. 2002 Final Report of the CEOS Disaster Management Support Group, at web page: http://www.ceos.org/pages/DMSG/pdf/CEOSDMSG.pdf
Colesanti C, Fabbri A, Prati C, Rocca F (2003) Monitoring landslides and tectonic movements with permanent scatterers techniques. Eng Geol 68:3–14
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46
D’Addabbo A, Satalino G, Pasquariello G, Blonda P (2004) Three different unsupervised methods for change detection: an application. In: Proc. IEEE International Geosci. and Remote Sensing Symposium 2004, Anchorage, Alaska, 20–26 September 2004, IEEE Catalog Number: 04CH37612C (CD-ROM)
Fung F (1990) An assessment of TM imagery for land-cover change detection. IEEE Trans Geosci Remote Sens 28(4):681–684
LEWIS web page at http://www.silogic.fr/lewis/
Remondo J, Gonzales A, Diaz De Teran JR, Cendrero A, Fabbri A, Chung C-Jo F (2003) Validation of landslide susceptibility maps: examples and applications from a case study in Northern Spain. Nat Hazard 30:437–449
Richard M, Lippmann R (1991) Neural network classifiers estimate Bayesian a posteriory probabilities. Neural Comput 3(4):461–463
Tropeano D, Turconi L (2004) Using historical documents for landslides, debris flow and stream flood prevention. Applications in Northern Italy. Nat Hazard 31:663–679
Van Beek LPH, Van Asch Th WJ (2004) Regional assessment of the effects of land-use change on landslide hazard by means of physically based modeling. Nat Hazards 31:289–304
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazard 30:399–419
Wasowski J (1998) Understanding rainfall-landslide relationships in man-modified environments: a case-history from Caramanico Terme, Italy. Environ Geol 35(2–3):197–209
Acknowledgements
This work has been carried out in the framework of the EU project entitled: Landslide Early Warning Integrated System, LEWIS, Contract Number: EVG1-CT-2001-00055, http://www.silogic.fr/lewis/. The authors also thank the geologists involved in the project for their useful insights: Dr. Wasowski, Dr. E. Del Gaudio, and Dr. Gostelow. Particular thanks to the anonymous reviewers for their constructive suggestions, which have been very useful in improving the paper.
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Tarantino, C., Blonda, P. & Pasquariello, G. Remote sensed data for automatic detection of land-use changes due to human activity in support to landslide studies. Nat Hazards 41, 245–267 (2007). https://doi.org/10.1007/s11069-006-9041-x
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DOI: https://doi.org/10.1007/s11069-006-9041-x