This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the OPEN ACCESS Remote Sens. 2015, 7 4319 simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

Abstract:Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of topographic slopes. In order to obtain reliable rainfall intensity for disaster mitigation, this study proposes a rainfall-vector projection method for topographic-corrected rainfall. The topographic-corrected rainfall is derived from wind speed, terminal velocity of raindrops, and topographical factors from digital terrain model. In addition, scatter plot was used to present landslide distribution with two triggering factors and kernel density analysis is adopted to enhance the perception of the distribution. Numerical analysis is conducted for a historic event, typhoon Mindulle, which occurred in 2004, in a location in central Taiwan. The largest correction reaches 11%, which indicates that topographic correction is significant. The corrected rainfall distribution is then applied to the analysis of landslide triggering factors. The result with corrected rainfall distribution provides better agreement with the actual landslide occurrence than the result without correction.

This paper presents a mechanism that utilizes intensive multitemporal and multisensor satellite images to monitor land cover dynamics. The proposed approach could be applied for regular dynamics monitoring, disaster monitoring and assessment, and vegetation recovery after natural disasters. The disaster monitoring and assessment are the most important issues imbedded in the program. This paper gives an example using the proposed mechanism to cover a major watershed in Taiwan. Often natural hazards such as typhoons or earthquakes trigger landslides or debris flows, which can deliver large amounts of sediment into a reservoir, decreasing its capacity for water storage. Disaster assessment prior to decision-making and support efforts is a must. Three major typhoons that happened in 2004 and 2005 will be discussed here. The proposed mechanism is demonstrated to be feasible, practical, and effective, since with it we are able to generate disaster assessment in a shorter time than with on-site or aerial-photo surveying alone provided that intensive satellite images are available.Index Terms-Disaster assessment, disaster monitoring, remote sensing.

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