Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

Abstract Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory t...

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Autores principales:	Hamid Hamraz, Marco A. Contreras, Jun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/94cf7ffc6e48403f9ce1178f103a7b24
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id	oai:doaj.org-article:94cf7ffc6e48403f9ce1178f103a7b24
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spelling	oai:doaj.org-article:94cf7ffc6e48403f9ce1178f103a7b242021-12-02T12:32:12ZForest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds10.1038/s41598-017-07200-02045-2322https://doaj.org/article/94cf7ffc6e48403f9ce1178f103a7b242017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07200-0https://doaj.org/toc/2045-2322Abstract Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.Hamid HamrazMarco A. ContrerasJun ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hamid Hamraz Marco A. Contreras Jun Zhang Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
description	Abstract Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.
format	article
author	Hamid Hamraz Marco A. Contreras Jun Zhang
author_facet	Hamid Hamraz Marco A. Contreras Jun Zhang
author_sort	Hamid Hamraz
title	Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
title_short	Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
title_full	Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
title_fullStr	Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
title_full_unstemmed	Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
title_sort	forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/94cf7ffc6e48403f9ce1178f103a7b24
work_keys_str_mv	AT hamidhamraz forestunderstorytreescanbesegmentedaccuratelywithinsufficientlydenseairbornelaserscanningpointclouds AT marcoacontreras forestunderstorytreescanbesegmentedaccuratelywithinsufficientlydenseairbornelaserscanningpointclouds AT junzhang forestunderstorytreescanbesegmentedaccuratelywithinsufficientlydenseairbornelaserscanningpointclouds
_version_	1718394173225172992

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

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