This work addresses the topic of flow direction and flow accumulation simulations in urban areas over digital surface models derived from light detection and ranging (LiDAR) data and multispectral high-resolution imagery. LiDAR data are very dense point clouds that include many objects that, in a 2 1/2-dimensional model, may become false obstacles for runoff, such as power lines or treetops. The presence of such obstacles is a problem for the flow paths simulation, especially in urban areas. We describe a methodology to produce a surface model more suitable for runoff modeling, by filtering objects that are above the surface and should not influence the flow paths. In a first step, thin obstacles are suppressed by applying mathematical morphology to a raster surface model. In a second step, satellite multispectral data and LiDAR data are classified using a support vector machine to identify trees, which are also removed from the digital model, and produce a more coherent surface model for runoff simulation. To simulate and evaluate the results, the flow-routing algorithm Dinfinity was used. The results show that the filtering is necessary to achieve a better characterization of runoff paths and allows identifying places where runoff may accumulate, causing floods or other problems.
The process of desertification, which extends from a long time ago, became a reality in Brazil. This phenomenon can be
understood as land degradation, caused by factors including climatic changes and human activities. Besides being a
threat to biodiversity, causes loss of soil productivity, threatening the lives of thousands of people living in affected
regions. So, the identification of affected areas is essential to diagnose and prevent the problem. Satellite image has been
a source of relatively low cost and widely used in this task. Therefore, is proposed in this study, a method to extract
automatically areas heavily affected by desertification. The method is based on concepts of mathematical morphology,
vegetation index and classification of digital images. Experiments are conducted separately, with images of CBERS 2
and 2B, and subsequently compared. The validation is done by crossing the results obtained with a reference image,
created by a manual process.