Next Article in Journal
Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
Previous Article in Journal
Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

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

UR 629 Ecologies des Forêts Méditerranéennes (URFM), INRA, 84000 Avignon, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1580; https://doi.org/10.3390/rs11131580
Received: 28 May 2019 / Revised: 26 June 2019 / Accepted: 1 July 2019 / Published: 3 July 2019
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
  |  
PDF [5179 KB, uploaded 3 July 2019]
  |     |  

Abstract

The spatial distribution of Leaf Area Density (LAD) in a tree canopy has fundamental functions in ecosystems. It 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 of these methods and showed that the bias-corrected Maximum Likelihood Estimator was the most efficient. However, it ignored (i) wood volumes, (ii) vegetation sub-grid clumping, (iii) the instrument effective footprint, and (iv) was limited to a single viewpoint. In practice, retrieving LAD is not straightforward, because vegetation is not randomly distributed in sub-grids, beams are divergent, and forestry plots are sampled from more than one viewpoint to mitigate occlusion. In the present article, we extend the previous formulation to (i) account for both wood volumes and hits, (ii) rigorously include correction terms for vegetation and instrument characteristics, and (iii) integrate multiview data. Two numerical experiments showed that the new approach entailed reduction of bias and errors, especially in the presence of wood volumes or when multiview data are available for poorly-explored volumes. In addition to its conciseness, completeness, and efficiency, this new formulation can be applied to multiview TLS—and also potentially to UAV LiDAR scanning—to reduce errors in LAD estimation. View Full-Text
Keywords: LAD; LAI; effective footprint; LiDAR; Maximum Likelihood Estimation; MLE; multiple view points; TLS; voxel; wood LAD; LAI; effective footprint; LiDAR; Maximum Likelihood Estimation; MLE; multiple view points; TLS; voxel; wood
Figures

Graphical abstract

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top