Orientador: Messias Meneguette Júnior / Banca: João Fernando Custodio da Silva / Banca: Ricardo Luís Barbosa / Resumo: As vertentes, como superf cies inclinadas, consistem em express~oes da Geomorfologia moldadas por fatores naturais (end ogenos e ex ogenos) e pelo pr oprio homem. Suas formas determinam o uxo ou o ac umulo de agua e representam caracter sticas fundamentais para a preven c~ao e resolu c~ao de problemas associados ao relevo, tais como utiliza c~ao do solo, constru c~ao civil entre outros. A classi ca c~ao da vertente em c^oncava, convexa ou retil - nea permite a identi ca c~ao de areas conforme sua declividade. Assim, por meio de uma grade retangular regular, base do modelo digital de terreno, gera-se uma malha interpolada por fun c~oes estimadoras: thin-plate spline, que possui caracter sticas de suaviza c~ao e krigagem, que al em da suavidade tamb em considera a depend^encia espacial. Logo ap os, a classi ca c~ao e feita, obedecendo a infer^encia fuzzy baseada em fun c~oes de pertin^encia que de nem classes a partir do c alculo da inclina c~ao e da concavidade ou convexidade do terreno. Entretanto, o resultado dessa classi ca c~ao est a atrelado a resolu c~ao da malha, n~ao permitindo fazer qualquer corre c~ao pontual. Pois, pequenas areas de pouca signi c^ancia podem ser formadas, necessitando elimin a-las. Nesse sentido, para que o resultado seja ajustado, aplica-se o diagrama de Voronoi, caracterizado por sua rela c~ao de abrang^encia e proximidade, como ferramenta para agregar regi~oes anteriormente classi cadas de modo a permitir um ajuste local e tornar o resultado mais condizente com a area em estudo, quando comparada a mapas geomorfol ogicos correspondentes / Abstract: Slopes, such as inclined surfaces, consist in geomorphological expressions shaped by natural factors (endogenous and exogenous) and also by man himself. Their shapes determine the ow or accumulation of water and represent fundamental characteristics for the prevention and resolution of problems associated with relief, as land use, buildings, and others. Classi- cating slopes in concave, convex or straight allows to identi cate areas based on declivity. Thus, by regular rectangular grid which represents a digital terrain model, it generates a interpolated mesh by estimator functions: thin-plate spline, which has characteristics of smoothing, and kriging, which besides smoothing also considers spatial dependence. After that, the classi cation is realized according to fuzzy inference based on membership functions that de ne classes from the calculation of the slope and concavity or convexity of the ground. However, the classi cation depends on mesh resolution and it not allows any point correction. Once small areas with little importance can be formed requiring eliminate them. In order to adjust the result, it applies the Voronoi diagram, characterized by its comprisement and close relationship and scope, as a tool to aggregate regions previously classi ed and allow a local adjustment, that can provides a consistent result in study areas, if it was compared to the corresponding geomorphological maps / Mestre

Made available in DSpace on 2014-06-11T19:23:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-02-16Bitstream added on 2014-06-13T20:30:02Z : No. of bitstreams: 1 berveglieri_a_me_prud.pdf: 2017717 bytes, checksum: 69925cc487658d0455ded0ccb94753b8 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / As vertentes, como superf cies inclinadas, consistem em express~oes da Geomorfologia moldadas por fatores naturais (end ogenos e ex ogenos) e pelo pr oprio homem. Suas formas determinam o uxo ou o ac umulo de agua e representam caracter sticas fundamentais para a preven c~ao e resolu c~ao de problemas associados ao relevo, tais como utiliza c~ao do solo, constru c~ao civil entre outros. A classi ca c~ao da vertente em c oncava, convexa ou retil - nea permite a identi ca c~ao de areas conforme sua declividade. Assim, por meio de uma grade retangular regular, base do modelo digital de terreno, gera-se uma malha interpolada por fun c~oes estimadoras: thin-plate spline, que possui caracter sticas de suaviza c~ao e krigagem, que al em da suavidade tamb em considera a depend encia espacial. Logo ap os, a classi ca c~ao e feita, obedecendo a infer encia fuzzy baseada em fun c~oes de pertin encia que de nem classes a partir do c alculo da inclina c~ao e da concavidade ou convexidade do terreno. Entretanto, o resultado dessa classi ca c~ao est a atrelado a resolu c~ao da malha, n~ao permitindo fazer qualquer corre c~ao pontual. Pois, pequenas areas de pouca signi c ancia podem ser formadas, necessitando elimin a-las. Nesse sentido, para que o resultado seja ajustado, aplica-se o diagrama de Voronoi, caracterizado por sua rela c~ao de abrang encia e proximidade, como ferramenta para agregar regi~oes anteriormente classi cadas de modo a permitir um ajuste local e tornar o resultado mais condizente com a area em estudo, quando comparada a mapas geomorfol ogicos correspondentes / Slopes, such as inclined surfaces, consist in geomorphological expressions shaped by natural factors (endogenous and exogenous) and also by man himself. Their shapes determine the ow or accumulation of water and represent fundamental characteristics for the prevention and resolution of problems associated with relief, as land use, buildings, and others. Classi- cating slopes in concave, convex or straight allows to identi cate areas based on declivity. Thus, by regular rectangular grid which represents a digital terrain model, it generates a interpolated mesh by estimator functions: thin-plate spline, which has characteristics of smoothing, and kriging, which besides smoothing also considers spatial dependence. After that, the classi cation is realized according to fuzzy inference based on membership functions that de ne classes from the calculation of the slope and concavity or convexity of the ground. However, the classi cation depends on mesh resolution and it not allows any point correction. Once small areas with little importance can be formed requiring eliminate them. In order to adjust the result, it applies the Voronoi diagram, characterized by its comprisement and close relationship and scope, as a tool to aggregate regions previously classi ed and allow a local adjustment, that can provides a consistent result in study areas, if it was compared to the corresponding geomorphological maps

The National Wetlands Inventory (NWI) is the definitive source for wetland resources in the United States. The NWI production unit in Hadley, MA has begun to upgrade their digital map database, integrating descriptors for assessment of wetland functions. Updating is conducted manually and some automation is needed to increase production and efficiency. This study assigned landscape position descriptor codes to NWI wetland polygons and correlated polygon environmental properties with public domain terrain, soils, hydrology, and vegetation data within the Coastal Plain of Virginia. Environmental properties were applied to a non-metric multidimensional scaling technique to identify similarities within individual landscape positions based on wetland plant indicators, primary and secondary hydrology indicators, and field indicators of hydric soils. Individual NWI landscape position classes were linked to field-validated environmental properties. Measures provided by this analysis indicated that wetland plant occurrence and wetland plant status obtained a stress value of 0.136 (Kruskal's stress measure = poor), which is a poor indicator when determining correlation among wetland environmental properties. This is due principally to the highly-variable plant distribution and wetland plant status found among the field-validated sites. Primary and secondary hydrology indicators obtained a stress rating of 0.097 (Kruskal's stress measure = good) for correlation. The hydrology indicators measured in this analysis had a high level of correlation with all NWI landscape position classes due the common occurrence of at least one primary hydrology indicator in all field validated wetlands. The secondary indicators had an increased accuracy in landscape position discrimination over the primary indicators because they were less ubiquitous. Hydric soil characteristics listed in the 1987 Manual and NTCHS field indicators of hydric soils proved to be a relatively poor indicator, based on Kruskal's stress measure of 0.117, for contrasting landscape position classes because the same values occurred across all classes. The six NWI field–validated landscape position classes used in this study were then further applied in a public domain digital data analysis. Mean pixel attribute values extracted from the 180 field-validated wetlands were analyzed using cluster analysis. The percent hydric soil component displayed the greatest variance when compared to elevation and slope curvature, streamflow and waterbody, Cowardin classification, and wetland vegetation type. Limitations of the soil survey data included: variable date of acquisition, small scale compared to wetland size, and variable quality. Flow had limitations related to its linear attributes, therefore is often found insignificant when evaluating pixel values that are mean of selected pixels across of wetland landscape position polygons. NLCD data limitations included poor quality resolution (large pixel size) and variable classification of cover types. The three sources of information that would improve wetland mapping and modeling the subtle changes in elevation and slope curvature that characterize wetland landscapes are: recent high resolution leaf-off aerial photography, high-quality soil survey data, and high-resolution elevation data. Due to the data limitations and the choice of variables used in this study, development of models and rules that clearly separate the six different landscape positions was not possible, and thus automation of coding could not be attempted. / Master of Science