Landslides have frequently occurred in last years, due to the disorderly growth of the cities and the occupation of risk areas by the poor population, causing social, environmental and economic impacts. Urban areas in expansion move to geologically unstable areas and topographically inclined, such as the River Bengalas Basin, located in the city of Nova Friburgo, mountainous region of the State of Rio de Janeiro, Brazil. This article aims to present the model developed and used to evaluate the susceptibility and vulnerability of the River Bengalas Basin to landslides, which in January 2011, with the occurrence of heavy rains, caused landslides that impacted in the death of 429 people in city of Nova Friburgo. For the case study, several investigations have been made related to the areas of the basin, such as slope, soil conditions, lithology, land use and cover, vertical curvature, horizontal curvature, and precipitation data. With this study it was possible to understand how the natural and anthropics elements of the basin are related to the local dynamics of the disasters regarding to their interferences in the induction of landslides; evaluate the effectiveness of the guidelines of the Plano Diretor Participativo do Município de Nova Friburgo regarding the landslides; identify the susceptible and vulnerable basin areas to landslides and calculate the rates of susceptibility and vulnerability to landslides from new calculation model proposed..
Classifiers that make use of pixel-by-pixel approaches are limited in the high spatial and radiometric resolution of urban areas, that happens mostly because of the similarity between the target's spectral response like ceramic roofs and bare soil. Because of that, the literature favors approaches that make use of object-oriented analysis for image interpretation, those approaches make a better use of the high spatial resolution and do not use only the target spectral response. Assuming that the object-oriented analysis is a favorable approach to be employed for intra-urban image classification, this paper will assess the results of such approach through an implementation of it in an urbanized area from the city of Campinas (Brazil), which has a size close to twelve square kilometers. Making use of the fusion of high spatial resolution image from Worldview-2 sensor and it's panchromatic band, the experiments were performed with the use of eCognition Developer 8 as the segmentation platform, and the classification being based on a decision tree generated by J48 (C4.5) algorithm on the software WEKA. This work also assess which approach best suits the experiment needs, being an optimal attribute selection achieved through a Wrapper filter, with a final kappa statistic of 0.9425.
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