Geotecnologia aplicada na análise de bacias hidrográficas e rede de drenagem: estudo das bacias hidrográficas do Murucutu e Aurá, Belém, Pará.

O objetivo deste trabalho foi avaliar variáveis físicas das bacias do rio Murucutu e rio Aurá (BHMA) usando geotecnologias para subsidiar o uso e a conservação desses recursos hídricos. Foi utilizada imagem do ?Shuttle Radar Topography Mission? (SRTM) na modelagem e identificação de características físicas, bem como o uso de sistema geográfico de informação (SIG), técnicas de geoprocessamento, quantificação de áreas das bacias, redes de drenagens, hierarquias fluviais, área de influência de nascentes. As análises dos dados foram realizadas na ferramenta Q.Gis 2.8, versão ?Wien?, integradas ao programa ?Terrain Analysis Using Digital Elevation Models? (TauDEM). Os resultados evidenciaram que as bacias do rio Murucutu e do rio Aurá possuem 37% de área antropizadas. A bacia do rio Aurá possui maior cobertura florestal do que a bacia do rio Murucutu. A ordem hierárquica do rio Aurá é de 5°ordem e do Murucutu é de 4ºordem, evidenciando que o Aurá é mais extenso com maior ramificação dos canais em relação ao Murucutu. 34% das nascentes estão em áreas urbanas na bacia do rio Murucutu. Conclui-se que com a análise melhora-se o entendimento dos elementos físicos, em especial ligados à hidrografia como também, desenvolve-se um produto de importância substancial para estudos morfométricos, principalmente para subsidiar o gerenciamento e outorgas do direito de uso prioritários da água, controle de enchentes, potencial de abastecimento hídrico, vulnerabilidade ao processo erosivo, dinâmica de transporte de poluentes, principais vias de contaminação hídrica, entre outras aplicações diretas.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area

Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.