RESUMOEste trabalho apresenta um modelo para determinar a fragilidade ambiental em bacias hidrográficas. O estudo foi realizado na Bacia do Rio Aldeia Velha, RJ, localizada na zona de contato e transição entre a baixada litorânea e o relevo montanhoso da Serra do Mar. Fatores que influenciam a ocorrência de processos erosivos foram integrados por algoritmos em um SIG para construção de classes de fragilidade. A análise multicriterial considerou o modelo numérico de terreno, dados oficiais sobre variáveis ambientais, imagem orbital de alta resolução e a opinião de especialistas. Através de informações secundárias sobre pedologia, intensidade das chuvas e declividade do terreno gerou-se o Mapa de Fragilidade Potencial (MFP). Através da combinação desse mapa com informações sobre uso e cobertura da terra obteve-se o Mapa da Fragilidade Emergente (MFE). Os resultados mostram que mais de 70% da área da bacia possui fragilidade ambiental considerada alta ou muito alta, tanto potencial como emergente. Os Processos Erosivos Aparentes (PEA) relacionaram-se positivamente com as áreas de alta fragilidade nos produtos cartográficos finais, destacando regiões mais propensas à intensificação de movimentos de massa e prioritárias para prevenção contra perda de solo. Os modelos geraram informações importantes para o planejamento territorial, possibilitando um zoneamento acessível e de fácil atualização para as prefeituras municipais e organizações da sociedade civil, inclusive para o monitoramento das áreas de alta fragilidade ambiental.Palavras-chave: geoprocessamento, diagnóstico, planejamento territorial. Mapping of Environmental Fragility in the Aldeia Velha River Basin, State of Rio de Janeiro, Brazil ABSTRACTThis paper presents an approach to mapping the environmental fragility of river basins. The study was performed at the Aldeia Velha river basin, a rainforest watershed located between Rio de Janeiro's coastal plains and the Serra do Mar highlands. A multiple-criteria analysis was performed involving factors that affect the risk of erosion; these variables were analyze using GIS tools and were integrated by algorithm in order to form a description of the different classes of environmental fragility in the basin. The multicriteria analysis considered the use of a numerical land model, official data, orbital imagery and the opinion of subject-matter experts. A Map of Potential Fragility (MPF) was generated through the collection of secondary information such as soil types, rainfall intensity and terrain slope. This initial map was later combined with a land-use projection to produce a Map of Emerging Fragility (MEF). The mapping results pointed to a highly fragile environment, where more than 70 percent of the basin's area was classified with high or very high degree of fragility, in both the potential and emerging context. The Apparent

The soil organic matter (SOM) content and dynamic are related to vegetation cover, climate, relief, and geology; these factors have strong variation in space in the southeastern of Brazil. The objective of the study was to compare and evaluate performance of classical multiple linear regressions (MLR) and geographically weighted regression (GWR) models to predict soil organic carbon (SOC) and chemical fractions of organic matter in the Brazilian southeastern mountainous region. The regression models were fitted based on SOC and chemical fractions of SOM. The points ( = 89) were selected by pedologist's experience along transects and toposequences. The covariates were also selected using the empirical knowledge of pedologists when choosing variables that drive soil carbon content and its dynamics. Geology map, legacy soils map, terrain attributes derived from digital elevation model, and remote sensing indices derived from RapidEye sensor bands were used as covariates. In all MLR models (except for fulvic acid fraction [FAF]), the legacy soil map was selected as a covariate by the stepwise approach. The geology map was not selected as important covariate to predict FAF and humin (HUM). At least one variable derived from remote sensing was selected by the adjusted models. For the prediction of the SOC, HUM, and FAF, the GWR models had the highest performance. The MLR models extrapolated the results, especially for SOC. The relationships among SOC, SOM fractions, and environmental covariates were affected by local landscape variability, and the GWR model was better at modeling.

Resumo -O objetivo deste trabalho foi avaliar modelos digitais de elevação (MDE), obtidos por diferentes fontes de dados, e selecionar um deles para derivar variáveis morfométricas utilizadas em mapeamento digital de solos. O trabalho foi realizado na Bacia Guapi-Macacu, RJ. Os dados primários utilizados nos modelos gerados por interpolação (MDE-carta e MDE-híbrido) foram: curvas de nível, drenagem, pontos cotados e dados de sensor remoto transformados em pontos. Utilizaram-se, na comparação, modelos obtidos por sensor remoto e por aerorrestituição (MDE SRTM e MDE IBGE). Todos os modelos apresentaram resolução espacial de 30 m. A avaliação dos modelos de elevação foi baseada na análise de: atributos derivados (declividade, aspecto e curvatura); depressões espúrias; comparação entre feições derivadas a partir dos modelos e as originais, oriundas de cartas planialtimétricas; e análise das bacias de contribuição derivadas. O modelo digital de elevação híbrido apresenta qualidade superior à dos demais modelos.Termos para indexação: atributos de terreno, bacias de contribuição, levantamento de solo, modelagem digital, SRTM. Elevation models for obtaining terrain attributes used in digital soil mappingAbstract -The objective of this work was to evaluate digital elevation models (DEM) obtained by different data sources and to select one of them for deriving morphometric variables used in digital soil mapping. The work was performed in the Guapi-Macacu river basin, RJ, Brazil. The primary data used in the models generated by interpolation (DEM map and DEM hybrid) were: contour lines, drainage, elevation points, and remote sensor data transformed into points. The obtained models by remote sensing and aero-restitution (DEM SRTM and DEM IBGE) were used in the comparison. All models showed spatial resolution of 30 m. The elevation model evaluations were based on: the terrain derived attribute analysis (slope, aspect, and curvature); spurious depressions (sink); comparison between features derived from the models and the original ones originated from planialtimetric maps; and the analysis of derived watersheds. The DEM hybrid showed a superior quality than the other models.

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