Geomorphometric attributes applied to soil-landscapes supervised classification of mountainous tropical areas in Brazil: A case study

Abstract

The present study aimed to improve the recognition of patterns of soils organization in mountainous tropical landscapes, hence helping soil surveys. The study area is located in the northwest Rio de Janeiro State, with a total area of approximately 16.470,ha. In this concern, geomorphometric features that define the geomorphic signature of the soil-landscape, were used. Geomorphometric features includes: elevation, relative elevation, aspect, curvature, curvature plane, curvature profile, slope, flow direction, flow accumulation and drainage's Euclidian distance, being all these features obtained by geoprocessing techniques. Almost all attributes were obtained from a digital elevation model and, therefore, the primary elevation data were obtained from the topographic maps. Through these geomorphometric attributes, a geomorphometric signature of the landscape was elaborated, and the particularities of each soil-landscape unit improved the supervised classification. The results showed the feasibility of using geomorphometric attributes to perform a supervised classification, using either neural networks or a maximum likelihood algorithm for soil-landscapes classification of mountainous tropical areas. In addition, we showed that geoprocessing techniques used to extract geomorphometrics attributes can subsidize soil surveys, making soil mapping faster and less biased by subjectivity. © 2008 Springer Science+Business Media B.V.