Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies

	Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies
	Ma, Lixia 1; Zheng, Guang 1; Eitel, Jan U. H.1; Moskal, L. Monika 1; He, Wei 1; Huang, Huabing 1
刊名	IEEE Transactions on Geoscience and Remote Sensing
	2016
卷号	54 期号:2 页码:679-696
关键词	JANUARY 2013 EASTERN CHINA OPTICAL-THICKNESS NORTHERN CHINA AIR-POLLUTION HONG-KONG MODIS VARIABILITY RETRIEVAL QUALITY
英文摘要	Accurate separation of photosynthetic and nonphotosynthetic components in a forest canopy from 3-D terrestrial laser scanning (TLS) data is a challenging but of key importance to understand the spatial distribution of the radiation regime, photosynthetic processes, and carbon and water exchanges of the forest canopy. The objective of this paper was to improve current methods for separating photosynthetic and nonphotosynthetic components in TLS data of forest canopies by adding two additional filters only based on its geometric information. By comparing the proposed approach with the eigenvalues plus color information-based method, we found that the proposed approach could effectively improve the overall producer's accuracy from 62.12% to 95.45%, and the overall classification producer's accuracy would increase from 84.28% to 97.80% as the forest leaf area index (LAI) decreases from 4.15 to 3.13. In addition, variations in tree species had negligible effects on the final classification accuracy, as shown by the overall producer's accuracy for coniferous (93.09%) and broadleaf (94.96%) trees. To remove quantitatively the effects of the woody materials in a forest canopy for improving TLS-based LAI estimates, we also computed the "woody-to-total area ratio" based on the classified linear class points from an individual tree. Automatic classification of the forest point cloud data set will facilitate the application of TLS on retrieving 3-D forest canopy structural parameters, including LAI and leaf and woody area ratios. © 2015 IEEE.
学科主题	Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20153801299801
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39520]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. International Institute for Earth System Science, Nanjing University, Nanjing 2.210023, China 3. Reveley Geospatial Laboratory for Environmental Dynamics, College of Natural Resources, University of Idaho, Moscow 4.ID 5.83844, United States 6. Remote Sensing and Geospatial Analysis Laboratory, Precision Forestry Cooperative, School of Environment and Forest Science, University of Washington, Seattle 7.WA 8.98195, United States 9. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 10.100101, China
推荐引用方式 GB/T 7714	Ma, Lixia,Zheng, Guang,Eitel, Jan U. H.,et al. Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):679-696.
APA	Ma, Lixia,Zheng, Guang,Eitel, Jan U. H.,Moskal, L. Monika,He, Wei,&Huang, Huabing.(2016).Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies.IEEE Transactions on Geoscience and Remote Sensing,54(2),679-696.
MLA	Ma, Lixia,et al."Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies".IEEE Transactions on Geoscience and Remote Sensing 54.2(2016):679-696.