Abstract
Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.
Highlights
The environmental impacts related to production activities have generated an increased demand for updated and detailed soil information, stimulating the need for evolution and adaptation of conventional methods for acquiring this information
Several techniques are used for DSM, e.g., the application of parametric methods such as logistic regression, geostatistical analysis and fuzzy logic, as well as non-parametric approaches such as auto-learning algorithms (ALA), decision trees, neural networks, and expert systems (Zhou et al, 2004; Grinand et al, 2008; Sarmento et al, 2008), based on the quantitative analysis of spatially distributed characteristics of soil formation factors (Florinsky, 2012)
The aim of this study was to apply artificial neural networks to predict soil types by using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data source
Summary
The environmental impacts related to production activities have generated an increased demand for updated and detailed soil information, stimulating the need for evolution and adaptation of conventional methods for acquiring this information. The technological innovations of the last two decades enabled access to a set of tools related to computational intelligence, remote sensing data, computer products and conventional cartography, enabling progress in the forms of acquisition and analysis of environmental information. Benefiting from these innovations, the methods for generating soil information were adjusted and improved by incorporating digital mathematical techniques for spatial soil prediction. These techniques currently used in soil science are more widely known as digital soil mapping “DSM” (McBratney et al, 2003). Several techniques are used for DSM, e.g., the application of parametric methods such as logistic regression, geostatistical analysis and fuzzy logic, as well as non-parametric approaches such as auto-learning algorithms (ALA), decision trees, neural networks, and expert systems (Zhou et al, 2004; Grinand et al, 2008; Sarmento et al, 2008), based on the quantitative analysis of spatially distributed characteristics of soil formation factors (Florinsky, 2012)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
