Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasi...
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Gespeichert in:

Autor*in:	César da Silva Chagas [verfasserIn] Carlos Antônio Oliveira Vieira [verfasserIn] Elpídio Inácio Fernandes Filho [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	atributos do terreno redes neurais artificiais máxima verossimilhança

Übergeordnetes Werk:	In: Revista Brasileira de Ciência do Solo - Sociedade Brasileira de Ciência do Solo, 2004, 37(2013), 2, Seite 339-351
Übergeordnetes Werk:	volume:37 ; year:2013 ; number:2 ; pages:339-351

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1590/S0100-06832013000200005

Katalog-ID:	DOAJ074339133

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callnumber-first	S - Agriculture
author	César da Silva Chagas
spellingShingle	César da Silva Chagas misc S1-972 misc atributos do terreno misc redes neurais artificiais misc máxima verossimilhança misc Agriculture (General) Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
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topic_title	S1-972 Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping atributos do terreno redes neurais artificiais máxima verossimilhança
topic	misc S1-972 misc atributos do terreno misc redes neurais artificiais misc máxima verossimilhança misc Agriculture (General)
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title	Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
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title_full	Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
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title_sort	comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
callnumber	S1-972
title_auth	Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
abstract	Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.
abstractGer	Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.
abstract_unstemmed	Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.
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title_short	Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping
url	https://doi.org/10.1590/S0100-06832013000200005 https://doaj.org/article/03f784f02fa5473b94bd1fba466c7b75 http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832013000200005&lng=en&tlng=en https://doaj.org/toc/1806-9657
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Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

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