Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method...

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Autores principales:	Jeremy Arkin, Nicholas C. Coops, Lori D. Daniels, Andrew Plowright
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	forest fire fuels LiDAR RPAS fire behaviour individual tree Science Q
Acceso en línea:	https://doaj.org/article/9b6b2fc0ded945b98ee73b591f5b312a
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spelling	oai:doaj.org-article:9b6b2fc0ded945b98ee73b591f5b312a2021-11-25T18:54:38ZEstimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data10.3390/rs132245982072-4292https://doaj.org/article/9b6b2fc0ded945b98ee73b591f5b312a2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4598https://doaj.org/toc/2072-4292The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m<sup>2</sup> and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.Jeremy ArkinNicholas C. CoopsLori D. DanielsAndrew PlowrightMDPI AGarticleforest firefuelsLiDARRPASfire behaviourindividual treeScienceQENRemote Sensing, Vol 13, Iss 4598, p 4598 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forest fire fuels LiDAR RPAS fire behaviour individual tree Science Q
spellingShingle	forest fire fuels LiDAR RPAS fire behaviour individual tree Science Q Jeremy Arkin Nicholas C. Coops Lori D. Daniels Andrew Plowright Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
description	The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m<sup>2</sup> and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.
format	article
author	Jeremy Arkin Nicholas C. Coops Lori D. Daniels Andrew Plowright
author_facet	Jeremy Arkin Nicholas C. Coops Lori D. Daniels Andrew Plowright
author_sort	Jeremy Arkin
title	Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
title_short	Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
title_full	Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
title_fullStr	Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
title_full_unstemmed	Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
title_sort	estimation of vertical fuel layers in tree crowns using high density lidar data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9b6b2fc0ded945b98ee73b591f5b312a
work_keys_str_mv	AT jeremyarkin estimationofverticalfuellayersintreecrownsusinghighdensitylidardata AT nicholasccoops estimationofverticalfuellayersintreecrownsusinghighdensitylidardata AT loriddaniels estimationofverticalfuellayersintreecrownsusinghighdensitylidardata AT andrewplowright estimationofverticalfuellayersintreecrownsusinghighdensitylidardata
_version_	1718410605606469632

Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

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