No abstract

Resumo:O índice de dissecação do relevo é uma análise morfométrica que considera o grau de entalhamento do vale e dimensão interfl uvial média. Ross (1992Ross ( e 1994 formalizou este índice o qual possui diversas aplicações, como segmentação do relevo, fornecer bases para o mapeamento geomorfológico, estudar a relação morfogênese -pedogênese e vulnerabilidade ambiental. O cálculo e mapeamento do índice de dissecação de forma analógica é extremamente dispendioso além de estar sujeito a erros humanos e a diferenças de interpretação. Atualmente não existe um método para realizar de forma totalmente automatizada o cálculo do índice de dissecação e que considere ambas as variáveis propostas por Ross (grau de entalhamento dos vales e dimensão interfl uvial média). O objetivo deste artigo é apresentar uma rotina de automatização do cálculo do índice de dissecação para

Abstract:This work is an altimetry evaluation study involving Digital Elevation Models ASTER GDEM version 2 and SRTM version 3. Both models are readily available free of charge, however as they are built from different remote sensing methods it is also expected that they present different data qualities. LIDAR data with 25 cm vertical accuracy were used as reference for assessment validation. The evaluation study, carried out in urbanized area, investigated the distribution of the residuals and the relationship between the observed errors with land slope classes. Remote sensing principles, quantitative statistical methods and the Cartographic Accuracy Standard of Digital Mapping Products (PEC-PCD) were considered. The results indicated strong positive linear correlation and the existence of a functional relationship between the evaluated models and the reference model. Residuals between -4.36 m and 3.11 m grouped 47.7% of samples corresponding to ASTER GDEM and 63.7% of samples corresponding to SRTM. In both evaluated models, Root Mean Square Error values increased with increasing of land slope. Considering 1: 50,000 mapping scale the PEC-PCD classification indicated class B standard for SRTM and class C for ASTER GDEM. In all analyzes, SRTM presented smaller altimetry errors compared to ASTER GDEM, except in areas with steep relief.Keywords: Digital Elevation Models; ASTER GDEM v2; SRTM v3; Altimetry Assessment.

Abstract:In many countries, the positional accuracy control by points in Cartography or Spatial data corresponds to the comparison between sets of coordinates of well-defined points in relation to the same set of points from a more accurate source. Usually, each country determines a maximum number of points which could present error values above a pre-established threshold. In many cases, the standards define the sample size as 20 points, with no more consideration, and fix this threshold in 10% of the sample. However, the sampling dimension (n), considering the statistical risk, especially when the percentages of outliers are around 10%, can lead to a producer risk (to reject a good map) and a user risk (to accept a bad map). This article analyzes this issue and allows defining the sampling dimension considering the risk of the producer and of the user. As a tool, a program developed by us allows defining the sample size according to the risk that the producer / user can or wants to assume. This analysis uses 600 control points, each of them with a known error. We performed the simulations with a sample size of 20 points (n) and calculate the associated risk. Then we changed the value of (n), using smaller and larger sizes, calculating for each situation the associated risk both for the user and for the producer. The computer program developed draws the operational curves or risk curves, which considers three parameters: the number of control points; the number of iterations to create the curves; and the percentage of control points above the threshold, that can be the Brazilian standard or other parameters from different countries. Several graphs and tables are presented which were created with different parameters, leading to a better decision both for the user and for the producer, as well as to open possibilities for other simulations and researches in the future. Keywords: Control quality; Cartography; Simulation; Sampling; Producer's risk; User's risk. Resumo:Em muitos países, o controle da acurácia posicional, feita por pontos, em Cartografia, corresponde à comparação entre conjuntos de coordenadas de pontos bem definidos em relação ao mesmo conjunto de pontos obtido a partir de uma fonte mais precisa. Normalmente, cada país fixa um número admissível de pontos que podem apresentar erros acima de um limite pré-estabelecido. Em muitos casos, as normas definem o tamanho da amostra (20 pontos), sem maiores considerações e fixam a condição de que somente 10% dos pontos pode ultrapassar certo limite. No entanto, a dimensão da amostragem (n), considerando o risco estatístico, especialmente quando os percentuais de outliers estão próximos de 10%, para mais ou para menos, conduzem a um risco do produtor (de rejeitar um bom mapa) e o risco do usuário (de aceitar um mapa ruim). Este artigo analisa esta questão e permite a definição da dimensão de amostragem considerando o risco do produtor e do usuário. O presente trabalho analisa essa questão e permite definir do tamanho da amostra em função do risco que o produt...

Abstract:The environmental problems of fires change the dynamics of the planet modifying and destroying their cycles and ecosystems. The human being is responsible for almost all the fires, but he is also protagonist of prevention initiatives. Thus, it becomes necessary to plan actions to combat these environmental damages. Since the geographic location is a important attribute, this research aims to support prevention and control of fires generating and validating maps with prediction risk of fire models applied in João Pessoa city. The data were modeled, processed, handled and analyzed in ArcGIS software v10.0 and Matlab, as well as the generation and overlay of thematic maps using multicriteria analysis, weighting the variables and fuzzy logic. In the next step, it was made the data validation considering the real data and the results demonstrated that the templates generated with the aid of fuzzy logic showed the coefficient of determination above 85%. The rainfall variable was the factor that contributed significantly to the models having greater reliability. This variable was not used and not specifically recommended in other methods compared in this study. The factors that contributed to the high degree of vulnerability risk of fires: high slope, vegetation, areas of high concentration of people, subnormal agglomerations and regions within the influence of the road network and hydrography. Finally, this study aimed to contribute to the decision making of the environment, social security and defense managers quickly and accurately using a few variables and low cost.