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

文献类型： 外文期刊

作者： Yun, Ting 1 ; An, Feng 1 ; Li, Weizheng 3 ; Sun, Yuan 4 ; Cao, Lin 4 ; Xue, Lianfeng 2 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Minist Agr, Danzhou Invest & Expt Stn Trop Crops, Danzhou 571737, Peoples R China

2.Nanjing Forestry Univ, Sch Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China

3.Nanjing Forestry Univ, Adv Anal & Testing Ctr, Nanjing 210037, Jiangsu, Peoples R China

4.Nanjing Forestry Univ, Coll Forestry, Nanjing 210037, Jiangsu, Peoples R China

关键词： point cloud data (PCD);terrestrial laser scanner (TLS);leaf area retrieval;computer graphics;computer vision

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2016 年 8 卷 11 期

页码：

收录情况： SCI

摘要： 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.

  • 相关文献

[1]Using point cloud data for tree organ classification and real leaf surface construction. Yun, Ting,Xue, Lianfeng,Yun, Ting,Chen, Bangqian,Li, Weizheng,Sun, Yuan,Yun, Ting,Xue, Lianfeng. 2017

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