The effects of solar incidence angle over digital processing of LANDSAT data

A technique to extract the topography modulation component from digital data is described. The enhancement process is based on the fact that the pixel contains two types of information: (1) reflectance variation due to the target; (2) reflectance variation due to the topography. In order to enhance the signal variation due to topography, the technique recommends the extraction from original LANDSAT data of the component resulting from target reflectance. Considering that the role of topographic modulation over the pixel information will vary with solar incidence angle, the results of this technique of digital processing will differ from one season to another, mainly in highly dissected topography. In this context, the effects of solar incidence angle over the topographic modulation technique were evaluated. Two sets of MSS/LANDSAT data, with solar elevation angles varying from 22 to 41 deg were selected to implement the digital processing at the Image-100 System. A secondary watershed (Rio Bocaina) draining into Rio Paraiba do Sul (Sao Paulo State) was selected as a test site. The results showed that the technique used was more appropriate to MSS data acquired under higher Sun elevation angles. Topographic modulation components applied to low Sun elevation angles lessens rather than enhances topography

Topographic studies through texture image analysis of LANDSAT data

There are no author-identified significant results in this report

Use of LANDSAT data to monitor pasture project in Amazonia

The author has identified the following significant results. No differences were found between acreage evaluation by visual and automatic interpretation of LANDSAT images. It was necessary to interpret both channels 5 and 7 to exactly outline the deforested areas. Channel 7 was necessary for the identification of deforested areas in the presence of recently grown natural vegetation, and channel 5 was necessary to identify the deforested areas in the cerrado regions. Automatic interpretation permitted the discrimination between areas with predominant grass coverage and recently grown natural vegetation

A water level relationship between consecutive gauge stations along Solim\~oes/Amazonas main channel: a wavelet approach

Gauge stations are distributed along the Solim\~oes/Amazonas main channel to monitor water level changes over time. Those measurements help quantify both the water movement and its variability from one gauge station to the next downstream. The objective of this study is to detect changes in the water level relationship between consecutive gauge stations along the Solim\~oes/Amazonas main channel, since 1980. To carry out the analyses, data spanning from 1980 to 2010 from three consecutive gauges (Tefe, Manaus and Obidos) were used to compute standardized daily anomalies. In particular for infra-annual periods it was possible to detect changes for the water level variability along the Solim\~oes/Amazonas main channel, by applying the Morlet Wavelet Transformation (WT) and Wavelet Cross Coherence (WCC) methods. It was possible to quantify the waves amplitude for the WT infra-annual scaled-period and were quite similar to the three gauge stations denoting that the water level variability are related to the same hydrological forcing functions. Changes in the WCC was detected for the Manaus-Obidos river stretch and this characteristic might be associated with land cover changes in the floodplains. The next steps of this research, will be to test this hypotheses by integrating land cover changes into the floodplain with hydrological modelling simulations throughout the time-series

Variação temporal e espacial de parâmetros de qualidade de água no reservatório de Barra Bonita/SP, como subsídio ao manejo da bacia de contribuição.

bitstream/item/135805/1/BPD-228-Qualidade-Agua.pd

Application of remote sensing to hydrological problems and floods

The main applications of remote sensors to hydrology are identified as well as the principal spectral bands and their advantages and disadvantages. Some examples of LANDSAT data applications to flooding-risk evaluation are cited. Because hydrology studies the amount of moisture and water involved in each phase of hydrological cycle, remote sensing must be emphasized as a technique for hydrological data acquisition

Produção de bovinos de corte em manejo intensivo de pastagem.

Estacionalidade de produção; Desempenho animal a pasto; Manejo do sistema; Resultados de produção e viabilidade economica.Resumo expandido

Planejamento de uma propriedade leiteira.

O planejamento de uma propriedade leiteira é fundamental para que no futuro não se colha decepções e frustrações. É um instrumento norteador das decisões e atitudes a serem tomadas, servindo também como simulador de sistemas potenciais e de avaliação de novas tecnologias. Existem várias formas de se fazer o planejamento de uma propriedade, todas elas partindo de uma situação existente: uma propriedade, uma área destinada à atividade leiteira e um rebanho. De modo geral, os técnicos tendem a iniciar o planejamento partindo da premissa de um volume diário de leite e estimando uma produção por vaca em lactação. Este procedimento pode acarretar em desilusões, pois o volume produzido não é garantia de lucro no processo. Por vezes, mesmo em situações com margens positivas de rentabilidade, pode ocorrer desânimo no final do mês, caso os valores não sejam compatíveis com a expectativa do investidor. "Se você não sabe para onde está indo, qualquer caminho estará errado" (Laurence Peter, educador canadense)

Land use in the Paraiba Valley through remotely sensed data

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results