A comprehensive but efficient framework of proposing and validating   feature parameters from airborne LiDAR data for tree species   classification

doi:10.1016/j.jag.2015.11.010

CORC > 北京大学 > 地球与空间科学学院

	A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification
	Lin, Yi ; Hyyppa, Juha
刊名	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
	2016
关键词	Tree species classification LiDAR Framework Feature parameters SVM classifier LASER-SCANNING DATA STATIC TERRESTRIAL TEMPERATE FOREST GROWTH IMAGES MANAGEMENT VOLUME STAND
DOI	10.1016/j.jag.2015.11.010
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. (C) 2015 Elsevier B.V. All rights reserved.; National Natural Science Foundation of China [41471281]; Beijing Natural Science Foundation [4154074]; Research Fund for Doctoral Program of Higher Education of China [20130001120016]; SRF for ROCS, SEM, China; SCI(E); ARTICLE; yi.lin@pku.edu.cn; 45-55; 46
语种	中文
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/438408]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Lin, Yi,Hyyppa, Juha. 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.
APA	Lin, Yi,&Hyyppa, Juha.(2016).A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.
MLA	Lin, Yi,et al."A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2016).