Preprint Article Version 1 This version is not peer-reviewed

Accounting for Wood, Foliage Properties and Laser Effective Footprint in Estimations of Leaf Area Density from multiview-LiDAR Data

Version 1 : Received: 23 May 2019 / Approved: 24 May 2019 / Online: 24 May 2019 (07:53:44 CEST)

A peer-reviewed article of this Preprint also exists.

Pimont, F.; Soma, M.; Dupuy, J. Accounting for Wood, Foliage Properties, and Laser Effective Footprint in Estimations of Leaf Area Density from Multiview-LiDAR Data. Remote Sens. 2019, 11, 1580. Pimont, F.; Soma, M.; Dupuy, J. Accounting for Wood, Foliage Properties, and Laser Effective Footprint in Estimations of Leaf Area Density from Multiview-LiDAR Data. Remote Sens. 2019, 11, 1580.

Journal reference: Remote Sensing 2019, 11
DOI: 10.3390/rs11131580

Abstract

The amount and spatial distribution of foliage in a tree canopy have fundamental functions in ecosystems as they affect energy and mass fluxes through photosynthesis and transpiration. They are usually described by the Leaf Area Index (LAI) and the Leaf Area Density (LAD), which can be measured through a variety of methods, including voxel-based methods applied to LiDAR point clouds. A theoretical study recently compared the numerical errors arising from different voxel-based estimation methods for Plant Area Density (PAD) based on Beer’s law-based, contact frequency and Maximum-Likelihood Estimation, showing that the bias-corrected Maximum Likelihood Estimator was theoretically the most efficient. However, this earlier study i) ignored wood volumes; ii) neglected vegetation clumping inside the voxel; iii) ignored instrument characteristics in terms of effective footprint, iv) was limited to a single viewpoint. In practice, retrieving LAD from PAD is not straightforward, vegetation is not randomly distributed in volumes of interest, beams are divergent and forestry plots are usually sampled from more than one viewpoint, to mitigate the effect of occlusion. In the present short communication, we extend the previous efficient formulation to actual field conditions to i) account for the presence of both wood volumes and wood hits, ii) rigorously include correction terms for vegetation and instrument characteristics, iii) integrate multiview data. A numerical comparison with other methods commonly used to combine information from different viewpoints led to error reduction, especially in poorly-explored volumes, which are frequent in actual canopies. Beyond its concision, completeness and efficiency, this new formulation -which can be applied to multiview TLS, but also UAV LiDAR scanning - can help reducing errors in LAD estimation.

Subject Areas

bias; efficiency; element size; LAD; LAI; leaf and wood separation; LiDAR; multiple viewpoints; point cloud; TLS; UAV; voxel

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