Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands

The foliage density <inline-formula><tex-math notation="LaTeX">$(u_l)$</tex-math></inline-formula> and the leaf angle distribution (LAD) are important properties that impact radiation transmission, interception, absorption and, therefore, photosynthesis. Their estim...

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Autores principales:	Ameni Mkaouar, Abdelaziz Kallel, Zouhaier Ben Rabah, Thouraya Sahli Chahed
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Leaf angle distribution (LAD) leaf area density leaf area index (LAI) leaf properties TLS voxel-based method Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/f12f54c2640c432e8acb83dc43099656
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spelling	oai:doaj.org-article:f12f54c2640c432e8acb83dc430996562021-11-18T00:00:13ZJoint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands2151-153510.1109/JSTARS.2021.3120521https://doaj.org/article/f12f54c2640c432e8acb83dc430996562021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9576621/https://doaj.org/toc/2151-1535The foliage density <inline-formula><tex-math notation="LaTeX">$(u_l)$</tex-math></inline-formula> and the leaf angle distribution (LAD) are important properties that impact radiation transmission, interception, absorption and, therefore, photosynthesis. Their estimation in a forested scene is a challenging task due to their interdependence in addition to the large variability in the forest structure and the heterogeneity of the vegetation. In this work, we propose to jointly estimate both of them using terrestrial laser scanner (TLS) point cloud for different forest stands. Our approach is based on direct/inverse radiative transfer modeling. The direct model was developed to simulate TLS shots within a vegetation scene having known foliage properties (i.e., <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and LAD) resulting in a 3-D point cloud of the observed scene. Then, the inverse model was developed to jointly estimate <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and LAD decomposing the 3-D point cloud into voxels. The problem turns out to a high-dimensional cost function to optimize. To do it, the shuffled complex evolution method has been adopted. Our approach is validated with results derived from several simulated homogeneous and heterogeneous vegetation canopies as well as from actual TLS point cloud acquired from Estonian Birch, Pine, and Spruce stands. Our findings revealed that our estimates were considerably close to the actual <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and leaf inclination distribution function (LIDF) values with (<inline-formula><tex-math notation="LaTeX">$\text{Biais}_{u_l} \in [0.001 \; 0.006]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{u_l} \in [0.019 \; 0.045]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LIDF}} \in [ 0.019 \; 0.038]$</tex-math></inline-formula>) for homogeneous dataset and (<inline-formula><tex-math notation="LaTeX">$\text{Biais}_{u_l} \in [0.001 \; 0.045]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{u_l} \in [0.023 \; 0.078]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LIDF}} \in [ 0.011 \; 0.018]$</tex-math></inline-formula>) for heterogeneous dataset with different tree crown geometries (i.e., conical and elliptical). In the actual case (Birch, Pine, and Spruce stands), our approach with the traditional and novel techniques, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LAI}}$</tex-math></inline-formula> are 0.526 and 0.105, respectively. The results outperform those of the baseline technique (i.e., assuming spherical LAD) with <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LAI}}=2.651$</tex-math></inline-formula>.Ameni MkaouarAbdelaziz KallelZouhaier Ben RabahThouraya Sahli ChahedIEEEarticleLeaf angle distribution (LAD)leaf area densityleaf area index (LAI)leaf propertiesTLSvoxel-based methodOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11095-11115 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Leaf angle distribution (LAD) leaf area density leaf area index (LAI) leaf properties TLS voxel-based method Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Leaf angle distribution (LAD) leaf area density leaf area index (LAI) leaf properties TLS voxel-based method Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Ameni Mkaouar Abdelaziz Kallel Zouhaier Ben Rabah Thouraya Sahli Chahed Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
description	The foliage density <inline-formula><tex-math notation="LaTeX">$(u_l)$</tex-math></inline-formula> and the leaf angle distribution (LAD) are important properties that impact radiation transmission, interception, absorption and, therefore, photosynthesis. Their estimation in a forested scene is a challenging task due to their interdependence in addition to the large variability in the forest structure and the heterogeneity of the vegetation. In this work, we propose to jointly estimate both of them using terrestrial laser scanner (TLS) point cloud for different forest stands. Our approach is based on direct/inverse radiative transfer modeling. The direct model was developed to simulate TLS shots within a vegetation scene having known foliage properties (i.e., <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and LAD) resulting in a 3-D point cloud of the observed scene. Then, the inverse model was developed to jointly estimate <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and LAD decomposing the 3-D point cloud into voxels. The problem turns out to a high-dimensional cost function to optimize. To do it, the shuffled complex evolution method has been adopted. Our approach is validated with results derived from several simulated homogeneous and heterogeneous vegetation canopies as well as from actual TLS point cloud acquired from Estonian Birch, Pine, and Spruce stands. Our findings revealed that our estimates were considerably close to the actual <inline-formula><tex-math notation="LaTeX">$u_l$</tex-math></inline-formula> and leaf inclination distribution function (LIDF) values with (<inline-formula><tex-math notation="LaTeX">$\text{Biais}_{u_l} \in [0.001 \; 0.006]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{u_l} \in [0.019 \; 0.045]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LIDF}} \in [ 0.019 \; 0.038]$</tex-math></inline-formula>) for homogeneous dataset and (<inline-formula><tex-math notation="LaTeX">$\text{Biais}_{u_l} \in [0.001 \; 0.045]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{u_l} \in [0.023 \; 0.078]$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LIDF}} \in [ 0.011 \; 0.018]$</tex-math></inline-formula>) for heterogeneous dataset with different tree crown geometries (i.e., conical and elliptical). In the actual case (Birch, Pine, and Spruce stands), our approach with the traditional and novel techniques, <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LAI}}$</tex-math></inline-formula> are 0.526 and 0.105, respectively. The results outperform those of the baseline technique (i.e., assuming spherical LAD) with <inline-formula><tex-math notation="LaTeX">$\text{RMSE}_{\text{LAI}}=2.651$</tex-math></inline-formula>.
format	article
author	Ameni Mkaouar Abdelaziz Kallel Zouhaier Ben Rabah Thouraya Sahli Chahed
author_facet	Ameni Mkaouar Abdelaziz Kallel Zouhaier Ben Rabah Thouraya Sahli Chahed
author_sort	Ameni Mkaouar
title	Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
title_short	Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
title_full	Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
title_fullStr	Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
title_full_unstemmed	Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands
title_sort	joint estimation of leaf area density and leaf angle distribution using tls point cloud for forest stands
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f12f54c2640c432e8acb83dc43099656
work_keys_str_mv	AT amenimkaouar jointestimationofleafareadensityandleafangledistributionusingtlspointcloudforforeststands AT abdelazizkallel jointestimationofleafareadensityandleafangledistributionusingtlspointcloudforforeststands AT zouhaierbenrabah jointestimationofleafareadensityandleafangledistributionusingtlspointcloudforforeststands AT thourayasahlichahed jointestimationofleafareadensityandleafangledistributionusingtlspointcloudforforeststands
_version_	1718425259833556992

Joint Estimation of Leaf Area Density and Leaf Angle Distribution Using TLS Point Cloud for Forest Stands

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