266 Scientia Agricola http://dx.doi.org/10.1590/0103-9016-2015-0131 Digital soil mapping using reference area and artificial neural networks Gustavo Pais de Arruda1, José A. M. Demattê2*, César da Silva Chagas4, Peterson Ricardo Fiorio3, Arnaldo Barros e Souza2, Caio Troula Fongaro2 1APagri Agronomic consultancy, Av. Adauto Pinheiro, 401 – ABSTRACT: Digital soil mapping is an alternative for the recognition of soil classes in areas 15040-368 – São José do Rio Preto, SP – Brazil. where pedological surveys are not available. The main aim of this study was to obtain a digital 2University of São Paulo/ESALQ − Dept. of Soil Science, soil map using artificial neural networks (ANN) and environmental variables that express soil- Av. Pádua Dias, 11, PO Box 9 − 13418-900 – Piracicaba, landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra SP − Brazil. Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference 3University of São Paulo/ESALQ − Dept. of Biosystems area of approximately 500 ha located in the center of the area studied. With the mapping units Engineering. identified together with the environmental variables elevation, slope, slope plan, slope profile, 4Embrapa Soils − R. Jardim Botânico, 1024 − 22460-000 − convergence index, geology and geomorphic surfaces, a supervised classification by ANN was Rio de Janeiro, RJ − Brazil. implemented. The neural network simulator used was the Java NNS with the learning algorithm *Corresponding author <[email protected]> "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 Edited by: Dionysis Bochtis 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 impor- tant 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. Received March 25, 2015 Keywords: map extrapolation, pedological survey, landscape attributes, pedological classes, Accepted September 01, 2015 data mining Introduction (2008) - classification trees; Kempen et al. (2009) - mul- tinomial logistic regression; Yigini and Panagos (2014) Knowledge of the properties and attributes of soil - regression-kriging. Among the data mining methods, is extremely important for adequate soil management artificial neural networks are characterized by the pos- programs whose aim is environmental sustainability and sibility of efficient handling of large amounts of data food production efficiency. However, factors such as a together with an ability to generalize, as found in the decline in the number of soil scientists, high cost and literature (Zhu, 2000; Behrens et al., 2005; Boruvka and time-consuming execution of the soil survey have con- Penizek, 2007). tributed to a shortage of such information. However, extrapolation of soil classes from a refer- Data mining methods can provide solutions to ence area using ANN has not yet been reported in the assist in the automatic extraction of information from literature. Such a technique would help to diminish the a set of available data (Behrens et al., 2005). Allied to soil map shortage, by generating at least one set of pri- this, strategies such as extrapolation of soil-landscape re- or information in areas not mapped based on a smaller lationships from a reference area for physiographically pedological mapping. It could be a quick and inexpen- similar regions where these relationships are not yet sive alternative for obtaining soil information for use in known, can help reduce the time required for and the projects that require this type of data. Thus, the present high cost of soil surveys. study aimed to evaluate the efficiency of ANN in the ex- The reference area method aims to characterize in trapolation of soil classes for an unmapped area from a detail the soil from a small representative area, called reference area mapped in detail, based on environmen- a reference area, a natural region where the main soil tal variables that express the soil-landscape relationship. classes are identified and established in terms of soil- The hypothesis is that the digital soil map would match landscape relationships (mapping rules). The knowledge the field soil distribution, supporting the use of ANN to acquired is used to facilitate and accelerate the inves- identify soils. tigation of soils in other areas in the same natural re- gion. The importance, advantages, and disadvantages of Materials and Methods this method were highlighted by Favrot (1989) and by Lagacherie et al. (1995). Characterization of field area The use of the reference area as a digital map- The research was carried out in an area of 11,072​​ ping strategy of soils associated with a certain predictive ha in Barra Bonita municipality , State of Sao Paulo, Bra- method can be found in studies such as Grinand et al. zil, located between the Universal Transverse Mercator Sci. Agric. v.73, n.3, p.266-273, May/June 2016 267 Arruda et al. Artificial neural network and soil classes (UTM) coordinates 750 530.3 and 764 287.8 m E and soil age. Thus, the soil factor “time” was indirectly con- 7 524 287.8 and 7 514 029.1 m N (Fuse 22, Datum sidered together with the “relief” and “parent material” SAD 69). The climate is Cwa (Köppen system), sub- soil factors. tropical of altitude with dry winters. Maximum average temperature is 30 ºC during the hottest month and 12.2 Digital soil mapping strategy ºC during the coldest month. Average annual rainfall is For a reference area (Favrot, 1989) using the 1,471 mm. The geology consists of sandstones cemented ANN approach about 500 ha cultivated almost exclu- by clay of Itaqueri Formation and basalts of Serra Geral sively with sugar cane were utilized, located in the Formation, São Bento group (IPT, 1981). The original center of the study area (Figures 1 and 2), for which vegetation was deciduous broadleaf forest which has there was a soil survey with a level of detail on a scale now been superceded by sugar cane. of 1: 10,000. The use of a reference area is based on the princi- Environmental variables ple that the informations and classifications obtained in In the present study, the following environmental small areas that represent a set of physiographic features variables were considered: elevation, slope, slope pro- in particular can be extrapolated to areas with similar file, slope plan and convergence index, all derived from soil-landscape relationship. a digital elevation model (DEM) with a spatial resolu- To clarify the soil landscape relationship, three tion of 20 m, geology and geomorphic surface. The DEM toposequences were also analyzed using point sequences was obtained by applying the TOPO to RASTER func- and all soil units were included. For each toposequence, tion in the ArcGIS 9.2 software (ESRI) to the digitalized graphs of topography, clay and sand content, Al3+, Sum level curves (at each 5 m), hydrography and reference of Bases (SB) and Cation Exchange Capacity (CEC) were points mapped on topographical maps from the IGC generated. (Geographic and Cartographic Institute of São Paulo), on Consequently, the data about the environmental a spatial scale of 1:10,000. Geomorphological surfaces variables used to feed the ANN originated from the ref- were identified based on relief variations using stereo- scopic analysis on aerial photographs on a 1:30,000 scale. These surfaces were bounded by discontinuities and changes in slope gradients, using the analysis of ter- rain attributes derived from the DEM. The level curves obtained from the digitalization of planialtimetric charts give an interpretation of the local stratigraphy, according to the criteria proposed by Ruhe (1969) and Daniels et al. (1971). The use of the “geomorphic surface” variable is justified because such information expresses a relation- ship between the soil distribution on the landscape and Figure 1 − Flowchart of the study. Figure 2 − Convencional soil map of the reference area and study area location. Sci. Agric. v.73, n.3, p.266-273, May/June 2016 268 Arruda et al. Artificial neural network and soil classes erence area. The process started with the creation of files (DSM). To generalize the neural network information, with: (i) data for training each architecture of the neural the file of the chosen network and an image file with network and (ii) data for the validation of each one of the pixel data from the raster files were processed using them. These training files allow the algorithm to learn the JavaNNS software. At this stage, the chosen network the relationship between the environmental variables already trained with the information from the reference (input data) used by the network and the mapping units area is used to process all pixels from the experimental (output data). The validation data are used for evaluating area. The final image file obtained includes the classifica- the generalization performance of the learned mapping. tion of each pixel according to the relationships between All variable data were rescaled to a range from 0 to 1, in mapping units and the environmental variables estab- accordance with Chagas et al. (2010). lished by the network. Next, information in each pixel of the reference area was extracted to create training files and valida- Validation of the Digital Soil Map tion files for each set of environmental variables tested. To validate the DSM, 22 random georeferenced These files were independent from each other.