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 soil-landscape relatio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade de São Paulo
2016-06-01
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| Series: | Scientia Agricola |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300266&lng=en&tlng=en |
| _version_ | 1818454205472440320 |
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| author | Gustavo Pais de Arruda José A. M. Demattê César da Silva Chagas Peterson Ricardo Fiorio Arnaldo Barros e Souza Caio Troula Fongaro |
| author_facet | Gustavo Pais de Arruda José A. M. Demattê César da Silva Chagas Peterson Ricardo Fiorio Arnaldo Barros e Souza Caio Troula Fongaro |
| author_sort | Gustavo Pais de Arruda |
| collection | DOAJ |
| description | 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 soil-landscape 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. |
| first_indexed | 2024-12-14T21:51:11Z |
| format | Article |
| id | doaj.art-2b692d618b6a41c8bb74fda97ee3028d |
| institution | Directory Open Access Journal |
| issn | 1678-992X |
| language | English |
| last_indexed | 2024-12-14T21:51:11Z |
| publishDate | 2016-06-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Scientia Agricola |
| spelling | doaj.art-2b692d618b6a41c8bb74fda97ee3028d2022-12-21T22:46:14ZengUniversidade de São PauloScientia Agricola1678-992X2016-06-0173326627310.1590/0103-9016-2015-0131S0103-90162016000300266Digital soil mapping using reference area and artificial neural networksGustavo Pais de ArrudaJosé A. M. DemattêCésar da Silva ChagasPeterson Ricardo FiorioArnaldo Barros e SouzaCaio Troula FongaroABSTRACT 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 soil-landscape 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300266&lng=en&tlng=enmap extrapolationpedological surveylandscape attributespedological classesdata mining |
| spellingShingle | Gustavo Pais de Arruda José A. M. Demattê César da Silva Chagas Peterson Ricardo Fiorio Arnaldo Barros e Souza Caio Troula Fongaro Digital soil mapping using reference area and artificial neural networks Scientia Agricola map extrapolation pedological survey landscape attributes pedological classes data mining |
| title | Digital soil mapping using reference area and artificial neural networks |
| title_full | Digital soil mapping using reference area and artificial neural networks |
| title_fullStr | Digital soil mapping using reference area and artificial neural networks |
| title_full_unstemmed | Digital soil mapping using reference area and artificial neural networks |
| title_short | Digital soil mapping using reference area and artificial neural networks |
| title_sort | digital soil mapping using reference area and artificial neural networks |
| topic | map extrapolation pedological survey landscape attributes pedological classes data mining |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300266&lng=en&tlng=en |
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