DOI: 10.1016/j.jag.2015.11.010
Scopus记录号: 2-s2.0-84990892638
论文题名: A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification
作者: Lin Y ; , Hyyppä J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 46 起始页码: 45
结束页码: 55
语种: 英语
英文关键词: Feature parameters
; Framework
; LiDAR
; SVM classifier
; Tree species classification
Scopus关键词: boreal forest
; coniferous tree
; lidar
; literature review
; parameterization
; support vector machine
; Picea abies
; Pinus sylvestris
; Populus tremula
; Quercus robur
英文摘要: Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector-machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80114
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Science, Peking University, Beijing, China; Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Masala, Finland
Recommended Citation:
Lin Y,, Hyyppä J. A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,46