Skip to main content

Evaluation of four digital classifiers for automated cartography of local soil classes based on reflectance and elevation in Mexico

Buy Article:

$63.00 plus tax (Refund Policy)

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%.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: Colegio de Postgraduados, Campus Montecillo, Texcoco, Mexico State, Mexico 2: Colegio de Postgraduados, Campus San Luis Potosi, Salinas, San Luis Potosi, Mexico 3: Instituto Nacional de Investigaciones Forestales Agricolas y Pecuarias, Texcoco, Mexico State, Mexico 4: Universidad Autonoma Chapingo, CRUO, Huatusco, Veracruz, Mexico

Publication date: 2010-04-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more