Using Artificial Neural Networks to Produce High- Resolution Soil Property Maps
High-resolution maps of soil property are considered as the most important inputs for decision support and policy-making in agriculture, forestry, flood control, and environmental protection. Commonly, soil properties are mainly obtained from field surveys. Field soil surveys are generally time-cons...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16833
- Acceso en línea:
- https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/using-artificial-neural-networks-to-produce-high-resolution-soil-property-maps
http://hdl.handle.net/20.500.12010/16833
- Palabra clave:
- Biología
Carbono orgánico del suelo
Textura de la tierra
Drenaje del suelo
- Rights
- License
- Abierto (Texto Completo)
| Summary: | High-resolution maps of soil property are considered as the most important inputs for decision support and policy-making in agriculture, forestry, flood control, and environmental protection. Commonly, soil properties are mainly obtained from field surveys. Field soil surveys are generally time-consuming and expensive, with a limitation of application throughout a large area. As such, high-resolution soil property maps are only available for small areas, very often, being obtained for research purposes. In the chapter, artificial neural network (ANN) models were introduced to produce high-resolution maps of soil property. It was found that ANNs can be used to predict high-resolution soil texture, soil drainage classes, and soil organic content across landscape with reasonable accuracy and low cost. Expanding applications of the ANNs were also presented. |
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