Influência do uso e ocupação do solo na qualidade da água e no processo erosivo da bacia de captação do Rio Barro Preto, Coronel Vivida-PR

This study was developed in objecting to investigate the use and occupation of land in 1999, 2005, 2011 e 2015 and estimate soil degradation by laminar erosion and the relation with water quality in 2015 in the catchment basin of the Barro Preto river, Coronel Vivida – PR. For multitemporal analysis of use and occupation of land in the basin used in the Landsat 5, 7 and 8 images and Geographic Information System. The laminar erosion was estimated by the Universal Soil Loss Equation through the systematization of calculations of the factors that compose the equation in SPRING/INPE. The water quality of the studied river section was evaluated according to the Water Quality Idex and the Resolution CONAMA n. 357/2005. The multitemporal analysis of the use and occupation of land has demonstrated that basin is predominantly agricultural in all years studied, as well as the permanent preservation area presents it not regularized during the period in accordance with the Brazilian Forest Code in force. In relation the quantification of laminar soil erosion in the study period, the rainfall and runoff factor was estimated considering the rainfall data from 1986 to 2014 and resulted in a value of 11.573,47 MJ/ha.mm/a. The Dystrophic Red Latosol, Dystrophic Red Nitisol, Fluvisol and Leptosol soil erodibility factor were 0,0138, 0,0137, 0,0207, 0,0196 t.ha.h/ha.MJ.mm/a, respectively. The topographical factor has demonstrated that the catchment basin has the rough terrain because the moderate and moderate strong classes are dominant in the study area. The cover and management and support practice factors were estimated according to the multitemporal analysis of the use and occupation of land in the basin and the values ranged from 0,0006 to 0,0688. The soil losses by laminar erosion were simulated with agriculture areas with corn and soybeans in no-till. The soil losses with maize crop in no-till in 1999, 2005, 2011 and 2015 were 9.782,75, 10.592,71, 9.636,61 e 11.058,26 t/year, respectively, and soybeans crops in no-till were 15.140,01, 16.645,20, 14.662,14 e 17.049,85 t/year, respectively. In relation with water quality of the section studied river, the average of Water Quality Index during the season were 55,47, 53,09 and 49,72, for the first, second and third sample point, respectively. Indication a decrease in water quality since the source to the last sample point. It is concluded that the use and occupation of land in the catchment basin interferes in the water quality, as well as in soil degradation.

Detecção de região saliente em imagens usando dissimilaridade de cor e amostragem por pixeis aleatórios

The purpose of this work is to demonstrate and to assess a simple algorithm for automatic estimation of the most salient region in an image, that have possible application in computer vision. The algorithm uses the connection between color dissimilarities in the image and the image’s most salient region. The algorithm also avoids using image priors. Pixel dissimilarity is an informal function of the distance of a specific pixel’s color to other pixels’ colors in an image. We examine the relation between pixel color dissimilarity and salient region detection on the MSRA1K image dataset. We propose a simple algorithm for salient region detection through random pixel color dissimilarity. We define dissimilarity by accumulating the distance between each pixel and a sample of n other random pixels, in the CIELAB color space. An important result is that random dissimilarity between each pixel and just another pixel (n = 1) is enough to create adequate saliency maps when combined with median filter, with competitive average performance if compared with other related methods in the saliency detection research field. The assessment was performed by means of precision-recall curves. This idea is inspired on the human attention mechanism that is able to choose few specific regions to focus on, a biological system that the computer vision community aims to emulate. We also review some of the history on this topic of selective attention.