The objective of this research is to evaluate the precision and accuracy of local soil class maps generated with four computer algorithms: minimum distance, parallelepiped, maximum likelihood, and artificial neural networks, using digital elevation models and spectral signatures of
local soil classes as input data. The study was done in the states of Mexico, San Luis Potosi, and Veracruz. Statistical binomial proportion tests were done to compare the difference between maps' precision and accuracy. The conclusion was that the combination of reflectance and elevation
improved the quality of soil class maps produced by CAC, due to the reflectance variation of local soil classes according to altitude, which helped to better identify them. The best precision was 84% and the best accuracy was 30%.
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Document Type: Research Article
Colegio de Postgraduados, Campus Montecillo, Texcoco, Mexico State, Mexico
Colegio de Postgraduados, Campus San Luis Potosi, Salinas, San Luis Potosi, Mexico
Instituto Nacional de Investigaciones Forestales Agricolas y Pecuarias, Texcoco, Mexico State, Mexico
Universidad Autonoma Chapingo, CRUO, Huatusco, Veracruz, Mexico
Publication date: 2010-04-01
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