Digital soil mapping using reference area and artificial neural networks

Abstract

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.

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

“…The occurrence of RR, RL, and NV in similar locations often hindered the establishment of soil-landscape relationships. Arruda et al (2016), also working with DSM, observed that similar behavior of environmental covariates complicated the distinction between soil classes. Soils in the NV class were the less representatives both in the RA and in Carajazinho map, thus, they showed a lower number of samples in the model training, which resulted in a relatively small area predicted as NV.…”

“…The study 2 (Arruda et al, 2016) presents an RA of 500 ha located in the center of the area of study (scale 1:10,000), which was planned with the aim at building a DSM based on artificial neural networks (ANN), using environmental covariates that expressed soil-landscape relationships for an area of 110.72 km 2 in an undulated terrain, generating high values of GA (83.7%) and K (0.80).…”

“…The obtained results were compared with the findings of studies previously conducted in Brazil by using RA for DSM (ten Caten et al, 2011;Arruda et al, 2013Arruda et al, , 2016Villela, 2013;Silva et al, 2016). Scientific articles were searched using the Scielo, Scopus, and Web of Science platforms, based on a study by ten Caten et al (2012).…”

Soil Spectral Mapping and Its Correlation With the Traditional Methodology

“…Figure 5 shows a heterogenous spatial distribution of the soil classes in the study area. This can be explained by geological analysis and hence of the source material in the region (Santos and Gasparetto 2008;Stevaux et al 2009, Arruda et al 2016). Factors such as water table fluctuation, drying and wetting successions, constant deposits of organic matter or even seasonal flooding conditions can be mentioned here as decisive in the formation and alteration of local soils, as already observed by Chicati (2017).…”

“…In this sense, the techniques of diffuse spectroradiometry are presented as a method of obtaining reliable results, because the management of soil properties is non-destructive and can be applied in studies of short or medium term, such as mappings made using geostatistical techniques or even based on hyperspectral sensors (Zelikman and Carmina 2013;Franceschini et al 2015;Arruda et al 2016). Advances in digital soil mapping like digital spatial data (DEM, orbital images), the computer power to process large data or the progress of GIS tools are attracting new soil scientists by the spatial analyses of soils (Minasny and McBratney 2016).…”