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

Ting Yun,Feng An,Lin Cao,Yuan Sun,Weizheng Li,Lianfeng Xue

doi:10.3390/rs8110942

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.

Highlights

Leaf area is very important for scientists in their understanding and modelling of gas-vegetation exchange processes, such as photosynthesis, evaporation and transpiration, rainfall interception, carbon flux and nutrient cycle, and is critical to evaluate biological parameters of plant or forest.Leaf area can be estimated in situ using “direct” or “indirect” methods.Direct methods of leaf area estimation involve manually counting and measuring leaves with a leaf area meter or image scanner [1] and using image analysis software
Classification accuracies of ≥ 90% were achieved for the point cloud of the sakura tree, but typical misclassification existed in the Fatsia japonica and michelia tree datasets, and some points belonging to thick and large branches were misclassified in regions where the local geometry was similar to the leaf surface
The novel method proposed in this study produced the true leaf area based on terrestrial laser scanner (TLS) scanning data

Summary

Introduction

Leaf area is very important for scientists in their understanding and modelling of gas-vegetation exchange processes, such as photosynthesis, evaporation and transpiration, rainfall interception, carbon flux and nutrient cycle, and is critical to evaluate biological parameters of plant or forest.Leaf area can be estimated in situ using “direct” or “indirect” methods.Direct methods of leaf area estimation involve manually counting and measuring leaves with a leaf area meter or image scanner [1] and using image analysis software. Leaf area is very important for scientists in their understanding and modelling of gas-vegetation exchange processes, such as photosynthesis, evaporation and transpiration, rainfall interception, carbon flux and nutrient cycle, and is critical to evaluate biological parameters of plant or forest. Leaf area can be estimated in situ using “direct” or “indirect” methods. Direct methods of leaf area estimation involve manually counting and measuring leaves with a leaf area meter or image scanner [1] and using image analysis software. The correlation between petiole length and leaf weight has been used to deduce leaf area [2]. These methods are time consuming and inefficient when working with a large number of samples.

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