Data/hora: 07/06/2024 08:07:26

Provedor de dados: 	90	
País: 	Brazil	
Título: 	Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping	
Autores: 	Chagas,César da Silva; Vieira,Carlos Antônio Oliveira; Fernandes Filho,Elpídio Inácio.	
Data: 	2013-04-01	
Ano: 	2013	
Palavras-chave: 	Terrain attributes; Neural networks; Maximum likelihood.	
Resumo: 	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.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832013000200005	
Editor: 	Sociedade Brasileira de Ciência do Solo	
Relação: 	10.1590/S0100-06832013000200005	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Ciência do Solo  v.37 n.2 2013	
Fonte: 	info:eu-repo/semantics/openAccess