Artículo
Evolutionary feature selection to estimate forest stand variablesusing LiDAR
Autor/es | García Gutiérrez, Jorge
González Ferreiro, Eduardo Riquelme Santos, José Cristóbal Miranda, David Diéguez Aranda, Ulises Navarro Cerrillo, Rafael M. |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2014 |
Fecha de depósito | 2016-07-13 |
Publicado en |
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Resumen | Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing ... Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing interest in machine learning and evolutionary computation has recently arisen to improve regression use in LiDAR data processing. Although evolutionary machine learning has already proven to be suitable for regression, evolutionary computation may also be applied to improve parametric models such as MLR. This paper provides a hybrid approach based on joint use of MLR and a novel genetic algorithm for the estimation of the main forest stand variables. We show a comparison between our genetic approach and other common methods of selecting predictors. The results obtained from several LiDAR datasets with different pulse densities in two areas of the Iberian Peninsula indicate that genetic algorithms perform better than the other methods statistically. Preliminary studies suggest that a lack of parametric conditions in field data and possible misuse of parametric tests may be the main reasons for the better performance of the genetic algorithm. This research confirms the findings of previous studies that outline the importance of evolutionary computation in the context of LiDAR analisys of forest data, especially when the size of fieldwork datatasets is reduced. |
Identificador del proyecto | TIN2007- 68084-C-00
TIN2011-28956-C02 09MRU022291P CGL2011-30285-C02-02 FP7-SME-2011-BSG |
Cita | García Gutiérrez, J., González Ferreiro, E., Riquelme Santos, J.C., Miranda, D., Diéguez Aranda, U. y Navarro Cerrillo, R.M. (2014). Evolutionary feature selection to estimate forest stand variablesusing LiDAR. International Journal of Applied Earth Observation and Geoinformation, 26, 119-131. |
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