In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest

Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of...

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Published inRemote sensing of environment Vol. 232; p. 111309
Main Authors Wang, Yunsheng, Pyörälä, Jiri, Liang, Xinlian, Lehtomäki, Matti, Kukko, Antero, Yu, Xiaowei, Kaartinen, Harri, Hyyppä, Juha
Format Journal Article
LanguageEnglish
Published New York Elsevier Inc 01.10.2019
Elsevier BV
Subjects
Online AccessGet full text
ISSN0034-4257
1879-0704
DOI10.1016/j.rse.2019.111309

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Abstract Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing intermediate and suppressed trees. At a tree-level, when AGB was predicted from tree height and DBH, the relative root mean square error (RMSE%) of AGB estimates in easy, medium, and difficult plots were 10.1%, 14.6%, and 20.4%, respectively, based on manually measured tree parameters from TLS point clouds. The manual measurements from ULS point clouds provided RMSE%s of 30.4%, 51.1%, and 76.9% in easy, medium, and difficult plots, respectively. These results indicate the magnitude of errors introduced by the data, and for ULS, by the tree height–DBH allometry used. When using automated in-house algorithms of the Finnish Geospatial Research Institute (FGI), the RMSE%s of tree-level AGB estimates using TLS were 11.5%, 17.8%, and 43.9% in easy, medium, and difficult plots, respectively. When using ULS, the corresponding errors were 31.7%, 57.7%, and 73.3%, respectively. Such results suggest that the automated algorithms perform similarly to manual processing in the tree-level ABG estimation from terrestrial and aerial point clouds, specifically in easy and medium forest stands. The incomplete tree digitization in the data was the main factor affecting the accuracy of tree-centric AGB estimates. Fusion of the terrestrial and aerial observation perspectives provided promising results. When manual TLS-based DBH and manual ULS-based tree height were used, the RMSE% was reduced to 8.6%–12.7%, compared to 10.1%–20.4% when only TLS-manual tree parameters were used and 30.3%–76.9% when only ULS-manual tree parameters were used. •Automated and manual methods perform similarly in point-cloud-based AGB estimates.•The main obstacle stems from incomplete digitization, rather than automated methods.•Terrestrial point clouds miss large-size trees in complicated forests.•Aerial point clouds lack direct DBH estimates and robust height-DBH allometry.•Fusion of terrestrial and aerial data improves tree- and plot-level AGB estimates.
AbstractList Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing intermediate and suppressed trees. At a tree-level, when AGB was predicted from tree height and DBH, the relative root mean square error (RMSE%) of AGB estimates in easy, medium, and difficult plots were 10.1%, 14.6%, and 20.4%, respectively, based on manually measured tree parameters from TLS point clouds. The manual measurements from ULS point clouds provided RMSE%s of 30.4%, 51.1%, and 76.9% in easy, medium, and difficult plots, respectively. These results indicate the magnitude of errors introduced by the data, and for ULS, by the tree height–DBH allometry used. When using automated in-house algorithms of the Finnish Geospatial Research Institute (FGI), the RMSE%s of tree-level AGB estimates using TLS were 11.5%, 17.8%, and 43.9% in easy, medium, and difficult plots, respectively. When using ULS, the corresponding errors were 31.7%, 57.7%, and 73.3%, respectively. Such results suggest that the automated algorithms perform similarly to manual processing in the tree-level ABG estimation from terrestrial and aerial point clouds, specifically in easy and medium forest stands. The incomplete tree digitization in the data was the main factor affecting the accuracy of tree-centric AGB estimates. Fusion of the terrestrial and aerial observation perspectives provided promising results. When manual TLS-based DBH and manual ULS-based tree height were used, the RMSE% was reduced to 8.6%–12.7%, compared to 10.1%–20.4% when only TLS-manual tree parameters were used and 30.3%–76.9% when only ULS-manual tree parameters were used. •Automated and manual methods perform similarly in point-cloud-based AGB estimates.•The main obstacle stems from incomplete digitization, rather than automated methods.•Terrestrial point clouds miss large-size trees in complicated forests.•Aerial point clouds lack direct DBH estimates and robust height-DBH allometry.•Fusion of terrestrial and aerial data improves tree- and plot-level AGB estimates.
Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing intermediate and suppressed trees. At a tree-level, when AGB was predicted from tree height and DBH, the relative root mean square error (RMSE%) of AGB estimates in easy, medium, and difficult plots were 10.1%, 14.6%, and 20.4%, respectively, based on manually measured tree parameters from TLS point clouds. The manual measurements from ULS point clouds provided RMSE%s of 30.4%, 51.1%, and 76.9% in easy, medium, and difficult plots, respectively. These results indicate the magnitude of errors introduced by the data, and for ULS, by the tree height–DBH allometry used. When using automated in-house algorithms of the Finnish Geospatial Research Institute (FGI), the RMSE%s of tree-level AGB estimates using TLS were 11.5%, 17.8%, and 43.9% in easy, medium, and difficult plots, respectively. When using ULS, the corresponding errors were 31.7%, 57.7%, and 73.3%, respectively. Such results suggest that the automated algorithms perform similarly to manual processing in the tree-level ABG estimation from terrestrial and aerial point clouds, specifically in easy and medium forest stands. The incomplete tree digitization in the data was the main factor affecting the accuracy of tree-centric AGB estimates. Fusion of the terrestrial and aerial observation perspectives provided promising results. When manual TLS-based DBH and manual ULS-based tree height were used, the RMSE% was reduced to 8.6%–12.7%, compared to 10.1%–20.4% when only TLS-manual tree parameters were used and 30.3%–76.9% when only ULS-manual tree parameters were used.
ArticleNumber 111309
Author Hyyppä, Juha
Liang, Xinlian
Lehtomäki, Matti
Kukko, Antero
Wang, Yunsheng
Pyörälä, Jiri
Yu, Xiaowei
Kaartinen, Harri
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Keywords Sample plot
In situ
Reference
LiDAR
ALS
Field measurement
Biomass
Point cloud
Unmanned aerial vehicle
Forest
Laser scanning
TLS
AGB
Individual tree
Language English
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Snippet Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the...
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SubjectTerms aboveground biomass
AGB
Algorithms
Allometry
ALS
Automation
Biomass
Boreal forests
computer software
data collection
Data processing
Digitization
Estimates
Evaluation
Field measurement
Forest
Forest biomass
forest stands
Forests
In situ
Individual tree
Information processing
Laser scanning
Lasers
LiDAR
Parameters
Point cloud
remote sensing
Root-mean-square errors
Sample plot
Scanning
Software development
Stems
Taiga
Terrestrial environments
TLS
tree height
Trees
Unmanned aerial vehicle
Unmanned aerial vehicles
Title In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest
URI https://dx.doi.org/10.1016/j.rse.2019.111309
https://www.proquest.com/docview/2306798384
https://www.proquest.com/docview/2286860790
Volume 232
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