Geographic Object-Based Image Analysis And Artificial Neural Networks For Digital Soil Mapping

The use of techniques to calibrate soil prediction models based on legacy maps is restricted to areas where conventional survey and soil mapping were performed. It is necessary to seek alternatives to calibrate soil predictive models without a legacy soil map. The problem is the difficulty to delineate polygons of soil classes based on soil sampling points. This paper presents a novel digital soil mapping strategy using only georeferenced points of soil profiles to delineate detailed polygons of soil classes by the Geographic Object-Based Image Analysis (GEOBIA) approach and Artificial Neural Networks (ANN) models. The main objective was to evaluate the integration of the GEOBIA approach at different segmentation levels with ANN's models and sampling points to produce a digital soil map of the Vale dos Vinhedos region, in Rio Grande do Sul, Brazil. From a Digital Terrain Model, 10 predictive variables were extracted. From the RapidEye remote sensing image the following spectral variables were extracted: Normalized Difference Vegetation Index and Normalized Difference Water Index. A new set of variables was produced using principal component analysis after standardization of the original variables. The multiresolution segmentation algorithm was used to create image objects at different segmentation levels. Four segmentation levels were tested with scale parameter (SP) 1, 2, 5 and 10; and the Cheesboard segmentation (CS) was used to transform the original pixels into polygons. Information on types of soil was obtained from 163 georeferenced soil profiles. Five ANN's structures were implemented: four for the segmented data (SP01, SP02, SP05 and SP10) and one for the per-pixel approach (CS); 16 repetitions of each ANN were performed. With the best models for each segmentation level, extrapolations of soil classes were performed for the entire study area and validated with a detailed legacy soil map. The per-pixel approach model obtained the worst result. The best result in the extrapolation of soil types found was with the object-based approach (SP1). In our understanding, the scientific community can profit from the proposed methodology particularly when it only has georeferenced points of soil classes and not a legacy map, i.e., immediately after a field survey without the need for photointerpretation for delineation of the soil mapping units. The use of GEOBIA for digital mapping of soil classes seems to be a promising approach for future research.