Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 3; s. 352 - 19
Hlavní autoři:	Neuville, Romain, Bates, Jordan Steven, Jonard, François
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	MDPI AG 20.01.2021
Témata:	Accurate estimation Aircraft detection algorithms Antennas Application contexts Biologie végétale (sciences végétales, sylviculture, mycologie...) canopy Clustering algorithms Computer science data collection Density-Based Spatial Clustering Diameter-at-breast heights Earth sciences & physical geography Ecosystems Engineering, computing & technology Environmental sciences & ecology forest stands Forestry forests HDBSCAN Hierarchical clustering Ingénierie, informatique & technologie LiDAR Life sciences Light detection and ranging Machine learning Omission and commission errors Optical radar PCA Physical, chemical, mathematical & earth Sciences Physique, chimie, mathématiques & sciences de la terre Phytobiology (plant sciences, forestry, mycology...) Point cloud principal component analysis Sciences de la terre & géographie physique Sciences de l’environnement & écologie Sciences du vivant Sciences informatiques Segmentation procedure Semantics Site-specific parameters tree and stand measurements Tree diameter Tree stem segmentation trees UAV Unmanned aerial vehicles (UAV)
ISSN:	2072-4292, 2072-4292
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Abstract	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.
AbstractList	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose. Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Author	Jonard, François Neuville, Romain Bates, Jordan Steven
Author_xml	– sequence: 1 givenname: Romain orcidid: 0000-0003-1949-1332 surname: Neuville fullname: Neuville, Romain – sequence: 2 givenname: Jordan Steven orcidid: 0000-0002-1349-7189 surname: Bates fullname: Bates, Jordan Steven – sequence: 3 givenname: François orcidid: 0000-0002-8562-2073 surname: Jonard fullname: Jonard, François
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Snippet	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned...
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SubjectTerms	Accurate estimation Aircraft detection algorithms Antennas Application contexts Biologie végétale (sciences végétales, sylviculture, mycologie...) canopy Clustering algorithms Computer science data collection Density-Based Spatial Clustering Diameter-at-breast heights Earth sciences & physical geography Ecosystems Engineering, computing & technology Environmental sciences & ecology forest stands Forestry forests HDBSCAN Hierarchical clustering Ingénierie, informatique & technologie LiDAR Life sciences Light detection and ranging Machine learning Omission and commission errors Optical radar PCA Physical, chemical, mathematical & earth Sciences Physique, chimie, mathématiques & sciences de la terre Phytobiology (plant sciences, forestry, mycology...) Point cloud principal component analysis Sciences de la terre & géographie physique Sciences de l’environnement & écologie Sciences du vivant Sciences informatiques Segmentation procedure Semantics Site-specific parameters tree and stand measurements Tree diameter Tree stem segmentation trees UAV Unmanned aerial vehicles (UAV)
Title	Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning
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