DOI: 10.1016/j.jag.2013.06.005
Scopus记录号: 2-s2.0-84897837984
论文题名: Evolutionary feature selection to estimate forest stand variables using LiDAR
作者: Garcia-Gutierrez J ; , Gonzalez-Ferreiro E ; , Riquelme-Santos J ; C ; , Miranda D ; , Dieguez-Aranda U ; , Navarro-Cerrillo R ; M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 26, 期: 1 起始页码: 119
结束页码: 131
语种: 英语
英文关键词: Evolutionary computation
; Forest-stand variables
; LiDAR
; Regression
; Stepwise selection
Scopus关键词: data processing
; data set
; fieldwork
; forestry
; genetic algorithm
; lidar
; multiple regression
; stand dynamics
; Iberian Peninsula
英文摘要: 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 ourgenetic approach and other common methods of selecting predictors. The results obtained from severalLiDAR 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. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79723
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Computer Science Languages and Systems, University of Seville, E.T.S.I.I., Reina Mercedes s/n, 41012 Sevilla, Spain; Department of Agroforestry Engineering, University of Santiago de Compostela, E.P.S., R/Benigno Ledo-Campus Universitario, 27002 Lugo, Spain; Department of Forestry Engineering, University of Cordoba, E.T.S.I.A.M, Edificio Leonardo da Vinci, Campus de Rabanales, 14014 Cordoba, Spain
Recommended Citation:
Garcia-Gutierrez J,, Gonzalez-Ferreiro E,, Riquelme-Santos J,et al. Evolutionary feature selection to estimate forest stand variables using LiDAR[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,26(1)