Engineering topics
Publications and source records attributed to Novo, E. M. L. D..
Analyses for seven land-use classes are presented. The classes are: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation, and natural vegetation. The automatic classification of LANDSAT MSS data using a maximum likelihood algorithm shows a 39% average error of emission and a 3.45 error of commission for the seven classes.
The use of LANDSAT data to evaluate pasture quality in the Amazon region is demonstrated. Pasture degradation in deforested areas of a traditional tropical forest cattle-raising region was estimated. Automatic analysis using interactive multispectral analysis (IMAGE-100) shows that 24% of the deforested areas were occupied by natural vegetation regrowth, 24% by exposed soil, 15% by degraded pastures, and 46% was suitable grazing land.
The application of temporal LANDSAT data to study floods was verified, and the natural features responsible for this phenomenon were surveyed using the Doce river valley as a test site, because of the catastrophic (1978-1979) flood. Data from LANDSAT images and CCT's were used. Geomorphical mapping evaluated morphostructural features. Seven and nine classes of water surfaces for dry and rainy seasons were analyzed. The magnitude of the changes from preflood to postflood stage are estimated. The single Pixel program was applied to correlate the drainage basin characteristics to the grey level of LANDSAT data.
Applications in the areas of agronomy, geology, oceanography, geography, environment and cartography are presented. A brief description of the Brazilian program is also given.
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.