Validação de Estimativas de Precipitação por Radar Meteorológico em uma Bacia Hidrológica na Região Central do Estado de São Paulo, Brasil

O objetivo do presente trabalho foi validar as estimativas de precipitação do radar meteorológico do Centro de Meteorologia de Bauru (IPMet/UNESP) para a bacia hidrográfica do Rio Jacaré Guaçu, localizada na região central do Estado de São Paulo/Brasil. Para isso, foram utilizados dados de 2013 de 18 estações pluviométricas. A quantificação da precipitação da bacia foi realizada através dos polígonos de Thiessen. Para a validação do radar, foram testadas 3 relações Z-R: Calheiros, Jones e Marshall-Palmer. Os melhores resultados da validação foram obtidos por Marshall-Palmer. Para o ajuste dos dados subestimados do radar meteorológico, utilizou-se um método de otimização nos dados das estações, encontrando o fator de correção de 3,135. Após o ajuste, observou-se uma semelhança entre a média da precipitação observada pelos pluviômetros e a precipitação estimada pelo radar. Sem as devidas correções, a chuva acumulada em 2013 na bacia hidrográfica de acordo com os pluviômetros foi de 1252,26 mm, enquanto que as obtidas pelas relações Z-R de Calheiros, Jones e Marshall-Palmer foram 512,63, 218,6 e 358,37 mm, respectivamente. Com esse estudo pudemos confirmar que ainda há muita dificuldade em se utilizar estimativas de chuva de radar meteorológico integrados a uma rede pluviométrica. Sugere-se realizar outros estudos semelhantes em escalas temporais diferentes

INTEGRAÇÃO DA INCERTEZA NA AMOSTRAGEM E CLASSIFICAÇÃO RANDOM FOREST UTILIZANDO BANDAS E ÍNDICES ESPECTRAIS PARA O MAPEAMENTO DE INUNDAÇÃO: Integration of uncertainty in sampling and random forest classification using bands and spectral indices for flood mapping

Traditional classifications present limitations for mapping floods due to mixing the spectral response of water with adjacent non-aquatic targets or similar spectral response of non-aquatic targets with water. Furthermore, in general, these classifications are evaluated only in terms of overall accuracy without considering the uncertainties in the classification process. Thus, this study aimed to integrate uncertainty in the Random Forest (RF) classification process for flood mapping, which guided the sampling process. The classification used 21 variables including indices and spectral bands from the Operational Land Imager sensor of the Landsat-8 satellite. Sampling was performed initially with the selection of points from the visual interpretation of the satellite image and later by collecting samples with high Shannon entropy values in the uncertainty map. The variables with the greatest importance for classification were selected by the Recursive Feature Elimination (RFE) algorithm. The final RF classification using samples collected based on the uncertainty map and with the four selected variables by the RFE presented an accuracy of 98.0% and a reduction of uncertainty, which indicates a greater confidence in the spatial representation and quantification of water permanent and temporary surface associated with floods. Keywords: Flood mapping. Random Forest Classifier. Spectral bands and indices. Variable selection. Shannon Entropy.Classificações tradicionais apresentam limitações para o mapeamento de inundações devido à mistura da resposta espectral da água com alvos adjacentes não aquáticos ou resposta espectral similar de alvos não aquáticos com a água. Além disso, em geral, as classificações são avaliadas apenas em termos de acurácia global sem considerar as incertezas no processo de classificação. Assim, neste estudo objetivou-se integrar a incerteza na classificação Random Forest (RF) para o mapeamento de inundações auxiliando o processo de amostragem. A classificação utilizou 21 variáveis representadas por bandas e índices espectrais do sensor Operational Land Imager do satélite Landsat-8. A amostragem foi realizada inicialmente com a seleção de pontos a partir da interpretação visual da imagem de satélite e posteriormente coletando amostras com alta entropia de Shannon no mapa de incerteza. As variáveis com maior importância para a classificação foram selecionadas utilizando o algoritmo Recursive Feature Elimination (RFE). Os resultados mostram que a classificação RF final usando amostras coletadas com base no mapa de incerteza e o conjunto de variáveis selecionadas pelo RFE apresentou 98,0% de exatidão e redução das incertezas do mapeamento da água superficial em relação à classificação RF com todas as variáveis e sem considerar a amostragem baseada na incerteza. Palavras-chave: Mapeamento de inundação. Classificador Random Forest. Bandas e índices espectrais. Seleção de variáveis. Entropia de Shannon

Estimativa de Parâmetros Biofísicos de Povoamentos de Eucalyptus Através de Dados SAREstimation of Biophysical Parameters in the Eucalyptus Stands by SAR Data

O objetivo deste trabalho foi estabelecer as relações entre os parâmetros dendométricos de florestas e a resposta da radiometria e da interferometria obtidas por sensores SAR (Synthetic Aperture Radar), utilizando técnicas de regressão multivariada. Uma área de povoamento de Eucalyptus grandis, com 6,1 anos de plantio, foi selecionada para estudo. Os resultados indicaram que o volume das parcelas foi altamente correlacionado com o logaritmo da altura interferométrica (LogHint) obtida pela diferença entre os modelos interferométricos nas bandas X e P, enquanto que o DAP (Diâmetro à Altura do Peito) apresentou melhor relação com a combinação da coerência interferométrica na banda PVV (CohPVV) e o LogHint. A altura comercial da vegetação, similarmente ao DAP, foi melhor explicada com a combinação da CohPvv e o LogHint. Os resultados obtidos demonstraram que, devido ao povoamento ser constituído de indivíduos cuja estrutura era predominantemente cilíndrica de orientação vertical, houve uma maior interação com a polarização VV. Os resultados indicaram também que a resposta radiométrica na banda P, de maior comprimento de onda, não foi tão eficaz quanto a interferometria (CohPvv e o LogHint) para estimativa dos parâmetros dendométricos estudados, obtendo índices de determinação de 84 a 88% em relação ao inventário florestal das áreas.Abstract The main goal of this paper was to establish the relationship between the forest dendrometric parameters and the interferometric and radiometric image response, obtained from a SAR sensor (Synthetic Aperture Radar), using multivariable regression techniques. A 6.1-year-old Eucalyptus grandis stand area was selected for this study. The results pointed out that the stand volume was highly correlated with the interferometric height logarithm (LogHint), obtained by the difference between interferometric models in X and P bands, whereas the DBH presented better relationship with the combination of VV coherence (CohPvv) and the LogHint. The commercial vegetation height, similar to the DBH, was better explained by the combination of CohPvv and LogHint. The results showed that due to the fact that the population of individuals whose structure was predominantly cylindrical and vertically oriented, caused a higher interaction with the vertical polarization. The higher wavelength P band radiometry efficacy wasn’t as good as the interferometry (CohPvv and LogHint) to estimate the dendometric parameters studied, which obtained 84 to 88% of determination coefficient in relation to the area forest inventory

Uso de Modelo Aditivo Generalizado para Análise Espacial da Suscetibilidade a Movimentos de Massa

Neste artigo, é analisada a distribuição espacial da suscetibilidade a movimentos de massa da Bacia Hidrográfica do Rio Luís Alves, localizada no estado de Santa Catarina.  A modelagem empregada baseia-se em processos pontuais espaciais, na qual se define uma medida de suscetibilidade que varia continuamente sobre a região de estudo e é estimada por meio de métodos de modelos aditivos generalizados (GAM). A suscetibilidade a movimentos de massa, neste contexto, é quantificada por níveis de probabilidades. O procedimento empregado incorpora ao modelo fatores condicionantes de suscetibilidade, de forma simples e de fácil interpretação. O método viabiliza a construção de superfícies de decisão que permitem a geração de mapas com contornos de tolerância baseado em medidas de probabilidade. Tais mapas auxiliam na identificação de áreas de alta/baixa suscetibilidade, uma vez que a hipótese nula de suscetibilidade constante na região de estudo pode ser testada. O resultado da aplicação do modelo mostrou que a variação espacial da suscetibilidade na área de estudo foi significativa a certos fatores condicionantes, apontando um caminho para avanços nos sistemas técnicos de monitoramento e alerta a estas situações, e ampliando as possibilidades para as decisões necessárias que possam minimizar os impactos de processos geomorfológicos danosos, tais como movimentos de massa.This paper analyzes spatial distribution of mass movements susceptibility from Luís Alves watershed, Santa Catarina State, Brazil. The modeling framework adopted in this research is based on spatial point processes, which defines a susceptibility measure that varies continuously over the study region and is estimated by means of generalized additive modeling methods. In this paper, the mass movements susceptibility is quantified by probability levels. The procedure employed allows susceptibility factors to be incorporated into the model in a simple way and easy interpretation. The procedure also allows the construction of maps with tolerance contours which help identify areas of significantly high/low susceptibility and an overall test for the null hypothesis of constant risk over the region. The application of the model to the data of susceptibility to mass movements, presented results consistent with the geomorphology of the study region, showed that the spatial variation in the susceptibility is significant, and pointing a way to the advance of monitoring and decisions making support systems

White sand vegetation in an Amazonian lowland under the perspective of a young geological history

What controls the formation of patchy substrates of white sand vegetation in the Amazonian lowlands is still unclear. This research integrated the geological history and plant inventories of a white sand vegetation patch confined to one large fan-shaped sandy substrate of northern Amazonia, which is related to a megafan environment. We examined floristic patterns to determine whether abundant species are more often generalists than the rarer one, by comparing the megafan environments and older basement rocks. We also investigated the pattern of species accumulation as a function of increasing sampling effort. All plant groups recorded a high proportion of generalist species on the megafan sediments compared to older basement rocks. The vegetation structure is controlled by topographic gradients resulting from the smooth slope of the megafan morphology and microreliefs imposed by various megafan subenvironments. Late Pleistocene-Holocene environmental disturbances caused by megafan sedimentary processes controlled the distribution of white sand vegetation over a large area of the Amazonian lowlands, and may have also been an important factor in species diversification during this period. The integration of geological and biological data may shed new light on the existence of many patches of white sand vegetation from the plains of northern Amazonia. © 2019, Academia Brasileira de Ciencias. All rights reserved

Discretização espacial de bacias hidrográficas

A major problem in developing digital elevation models (DEMs)for hydrological applications is the realistic representation of flow through complex terrain. A contour-based DEM, which uses natural flow lines and contours to define the element network in the model, is an approach to represent hydrological processes that should model natural processes well. The goal of this paper is to compare this method with the one based on regular grids that is quite common in GIS as a whole. It was shown that regular grids render the topography smoother and the resulting flux lines appear to be unnatural.Pages: 485-49

Detecting individual palm trees (Arecaceae family) in the Amazon rainforest using high resolution image classification

The identification of plant species individuals in ecological and conservation research is elementary to calculate population and community parameters and indexes. However, the database in these studies is usually limited to small length areas due the difficulty of collecting field data. Remote sensing of high spatial resolution has the potential to identify plant species individual in the forest canopy. In these studies, besides the high resolution images the specialist knowledge is fundamental to quality of the work. There are different high spatial resolution images, which can be acquired from sensors onboard of satellites or airplanes (aerial photographs). This study aims to identify individuals of palm trees using high spatial resolution image from videography, obtained from a flight mission over Amazon, and techniques of digital image processing. Images of different spatial resolution obtained over Madeira-Purus inter-rivers were tested using different techniques the image processing. Region growing segmentation technique and three classification methods were used to test the classification of videography images with two different image contrasts. Considering visual interpretation, the negative contrast in the images enhanced better the palm tree crown than the original real color images. The segmentation and classification methods were efficient to distinguish palm individuals in the 0.60m spatial resolution images. As the resolution improved to 0.20m, palm leaves were identified instead. The use of aerial videography data showed to be a potential alternative for high spatial resolution images in studies of ecology and biodiversity of tropical forests.Pages: 7628-763

Análise de Imagem Orientada a Objeto e Mineração de Dados aplicadas ao mapeamento da cana-de-açúcar

The aim of this research was to develop a methodology that can automate the sugar cane mapping task when remote sensing data are used. For this, we tested the integration of two major approaches of Artificial Intelligence: Object Based Image Analysis (OBIA) and Data Mining (DM). The study area comprises the municipalities of Ipuã, Guará and São Joaquim da Barra, located in the northwestern of São Paulo state, which are well representatives of the conditions of agriculture in southern and southeastern regions of Brazil. OBIA was used to emulate the interpreter knowledge in the process of sugar cane mapping, and MD techniques were employed for automatic generation of knowledge model. MD algorithm used was C4.5, which generates decision trees (DT) from a previous prepared training set. A time series of Landsat images was acquired in order to represent the wide patterns variability within the sugar cane crop season. The objects were generated by application of multiresolution segmentation algorithm. Thereafter, the knowledge extraction process has begun, which ends with the acquisition of DT. Once properly trained, the DT was applied to the Landsat time series and then generated the thematic map. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance, indicating that the examined classification routines achieved an overall accuracy of 94% and Kappa of 0,87. The results shows that OBIA and MD are very efficient and promising in the direction of automating the sugar cane classification process.Pages: 467-47

Padrões espaciais dos remanescentes da Mata Atlântica e elementos que compõem a paisagem da Serra Do Mar no Vale Do Paraíba - Microrregião do Paraíbuna-Paraitinga

The intense fragmentation of the Brazilian Atlantic Forest raises some important questions for understanding its spatial dynamics, especially about the relationships between the natural and anthropic elements. Regions of high altitude and rough reliefs, such as Serra do Mar, are well conserved and the natural vegetation can be found in large continuous areas. However, the flatter regions are quite different; they are more developed and disturbed and hence have few forests remnants scattered across the landscape. Thus, the aim of this study was to understand the spatial distribution of the Atlantic Forest remnants based on their relations with some physical and anthropic factors, in the Microregion Paraibuna-Paraitinga. By a multivariate linear regression model we analyzed the forest remnants and some factors that could be related with its own spatial distribution, such as: altitude, slope, aspect, hydrography, Conservation Units (CUs) and roads network. We create a cellular database (500m x 500m), which was filled with 9 different variables created by the elements cited before. These data were analyzed using the R software, specifically its statistical package aRT. The dependent variable for the regression was the total percentage of forest, based on vegetation data of 2010. The final regression model found a R2 of 0.76, so that the presence of CU's was the factor with major significance to represent the fragments. For CUs outside locations, the terrains aspect and the distance from municipal roads (unpaved) were the most significant elements to represent the spatial distribution of the Atlantic Forest remnants.Pages: 7663-767