Ana Paula Barbosa

Estimation of croplands using indicator kriging and fuzzy classification

Indicator kriging classifier confused citrus with native vegetation.Fuzzy classifier confused citrus with forest regeneration.The citrus crops exhibited a larger range using the indicator kriging method.The multispectral bands showed spatial dependence for both citrus and sugarcane.Fuzzy is a potential method to distinguish citrus crop in mid-resolution image. The knowledge of the land use is important for the agricultural economy and for defining the deployment of new crops. Thus, a tool for the mapping and quantification of crops is necessary. The development of a tool is possible with the use of remote-sensing images and efficient algorithm classifiers. The objective of this study was to verify the accuracies of indicator kriging and fuzzy classification as supervised classifiers in identifying sugarcane and citrus crops, which are more cultivated in Sao Paulo, Brazil. The investigated area was located on the border of the municipalities of Sao Manuel and Botucatu, Sao Paulo State, Brazil. We used digital images from satellite IRS-P6 (ResourceSat-1; path/row 329/94, January 29, 2011) with a spatial resolution of 23.5m. In the pre-processing phase, images were prepared for classification using several techniques, such as enhancement, geometric rectification and registration and reduction of dimensionality. In the process of image classification for citrus and sugarcane areas, two methods of classification were utilized, namely indicator kriging (IK) and fuzzy classification, and compared to the visual classification, which was assumed to reflect the reality on the ground (citrus, sugarcane, native vegetation, forest regeneration, soil and water). The classifications were made based on bands 2, 3, 4 and 5 and were evaluated using the kappa index. From the results of the classification of the pictures used for the discrimination and quantification of areas cultivated with citrus and sugarcane, the following conclusions can be drawn: the multispectral bands showed spatial dependence for both citrus and sugarcane, whereas a comparison of the maps revealed that the IK classifier confused citrus with areas of vegetation, and the fuzzy classifier confused citrus with forest regeneration; the IK classifier overestimated the areas of sugarcane crops, whereas the fuzzy classifier underestimated these areas. Based on the kappa index, band 2 (0.52-0.59µm) better represented the citrus and sugarcane areas, whereas band 5 (1.55-1.70µm) had the worst classification for both the IK and fuzzy algorithms. Citrus crop were best identified using the fuzzy classifier, and the sugarcane crop was best identified using the IK classifier.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

O objetivo deste trabalho foi comparar a precisao de metodos de classificacao de imagens orbitais na determinacao de areas cultivadas com citros, por metodos de sensoriamento remoto e de geoestatistica. A area de estudo utilizada nesta pesquisa esta localizada na cidade de Araraquara, regiao central do Estado de Sao Paulo, com 5.296,52 ha. Foi utilizada a imagem multiespectral do satelite CCD/CBERS 2B, adquirida em 2009 e processada no Sistema de Informacoes Geograficas (SIG) SPRING. Tres metodos de classificacao foram utilizados, sendo um nao supervisionado (Cluster) e dois supervisionados (Krigagem Indicativa/KI e Maxima verossimilhanca/Maxver), alem da classificacao em tela tida como verdade terrestre. A fidedignidade das classificacoes foi avaliada pelo indice Kappa. A Krigagem Indicativa foi o metodo que melhor classificou as areas de citros na banda 4, com forte grau de concordância, conforme a estatistica kappa, para as bandas 2 e 4. Pela validacao, o Cluster para a banda 2 (verde) foi o classificador que obteve a melhor qualidade. A Krigagem Indicativa foi o classificador que apresentou area total de citros mais proxima da verdade terrestre em -3,01%, enquanto o Maxver foi o classificador que mais superestimou a area total de citros em 42,94 %.

Modelo numérico do terreno obtido por diferentes métodos em cartas planialtimétricas

The digital elevation model is important to determine the slope and land use capability, therefore, a proposal of methodology for acquisition of elevation data contemplating an efficient algorithm to generate a slope map was developed. Thus, it was aimed to obtain and evaluate a digital elevation model without the vetorization of the contours on planialtimetric charts. The area for acquisition of elevation data was Sao Manuel, SP. The data were collected by two methods: level contour vetorization and the gathering of elevation points on the level contour with maximum elevation points. The elevation data were analyzed by geostatistical techniques. Inspite of wide difference in the number of collected points between two methods, the variograms were adjusted to the exponential model and showed a range of approximately 1500 m, which does not justify the wide difficulty in vetorization of the planialtimetric charts, once the data points collected in the area were appropriately distributed, they represented rightly the terrain surface