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 in | Remote sensing of environment Vol. 232; p. 111309 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
Elsevier Inc
01.10.2019
Elsevier BV |
Subjects | |
Online Access | Get full text |
ISSN | 0034-4257 1879-0704 |
DOI | 10.1016/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. |
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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 |
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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 |
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