DOI: 10.1016/j.jag.2017.09.004
Scopus记录号: 2-s2.0-85032197039
论文题名: Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest
作者: Zhu X ; , Skidmore A ; K ; , Darvishzadeh R ; , Niemann K ; O ; , Liu J ; , Shi Y ; , Wang T
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 64 起始页码: 43
结束页码: 50
语种: 英语
英文关键词: Classification
; Geometric feature
; Radiometric feature
; Radius search
; Terrestrial laser scanning
Scopus关键词: algorithm
; biomass
; image classification
; leaf area index
; lidar
; machine learning
; national park
; radiometer
; scanner
; Bavaria
; Bavarian Forest National Park
; Germany
英文摘要: Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79900
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, P.O. Box 217, Enschede, AE, Netherlands; Department of Environmental Science, Macquarie UniversityNSW, Australia; Department of Geography, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC, Canada
Recommended Citation:
Zhu X,, Skidmore A,K,et al. Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,64