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Remote Sens. 2016, 8(11), 942; doi:10.3390/rs8110942

A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR

1,2,†
,
1,†
,
3
,
4
,
4
and
2,*
1
Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture, Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China
2
School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
3
Advanced Analysis and Testing Centre, Nanjing Forestry University, Nanjing 210037, China
4
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi, Clement Atzberger and Prasad S. Thenkabail
Received: 14 July 2016 / Revised: 3 November 2016 / Accepted: 7 November 2016 / Published: 11 November 2016
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [7939 KB, uploaded 11 November 2016]   |  

Abstract

Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD. View Full-Text
Keywords: point cloud data (PCD); terrestrial laser scanner (TLS); leaf area retrieval; computer graphics; computer vision point cloud data (PCD); terrestrial laser scanner (TLS); leaf area retrieval; computer graphics; computer vision
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yun, T.; An, F.; Li, W.; Sun, Y.; Cao, L.; Xue, L. A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR. Remote Sens. 2016, 8, 942.

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