A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data

Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high...
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Autor*in:	Wuming Zhang [verfasserIn] Peng Wan [verfasserIn] Tiejun Wang [verfasserIn] Shangshu Cai [verfasserIn] Yiming Chen [verfasserIn] Xiuliang Jin [verfasserIn] Guangjian Yan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 11(2019), 2, p 211
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:2, p 211

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs11020211

Katalog-ID:	DOAJ073819689

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520			\|a Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
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allfields	10.3390/rs11020211 doi (DE-627)DOAJ073819689 (DE-599)DOAJba784cf40f89462ea68e416a22714972 DE-627 ger DE-627 rakwb eng Wuming Zhang verfasserin aut A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation Science Q Peng Wan verfasserin aut Tiejun Wang verfasserin aut Shangshu Cai verfasserin aut Yiming Chen verfasserin aut Xiuliang Jin verfasserin aut Guangjian Yan verfasserin aut In Remote Sensing MDPI AG, 2009 11(2019), 2, p 211 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:11 year:2019 number:2, p 211 https://doi.org/10.3390/rs11020211 kostenfrei https://doaj.org/article/ba784cf40f89462ea68e416a22714972 kostenfrei https://www.mdpi.com/2072-4292/11/2/211 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2019 2, p 211
spelling	10.3390/rs11020211 doi (DE-627)DOAJ073819689 (DE-599)DOAJba784cf40f89462ea68e416a22714972 DE-627 ger DE-627 rakwb eng Wuming Zhang verfasserin aut A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation Science Q Peng Wan verfasserin aut Tiejun Wang verfasserin aut Shangshu Cai verfasserin aut Yiming Chen verfasserin aut Xiuliang Jin verfasserin aut Guangjian Yan verfasserin aut In Remote Sensing MDPI AG, 2009 11(2019), 2, p 211 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:11 year:2019 number:2, p 211 https://doi.org/10.3390/rs11020211 kostenfrei https://doaj.org/article/ba784cf40f89462ea68e416a22714972 kostenfrei https://www.mdpi.com/2072-4292/11/2/211 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2019 2, p 211
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allfieldsGer	10.3390/rs11020211 doi (DE-627)DOAJ073819689 (DE-599)DOAJba784cf40f89462ea68e416a22714972 DE-627 ger DE-627 rakwb eng Wuming Zhang verfasserin aut A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation Science Q Peng Wan verfasserin aut Tiejun Wang verfasserin aut Shangshu Cai verfasserin aut Yiming Chen verfasserin aut Xiuliang Jin verfasserin aut Guangjian Yan verfasserin aut In Remote Sensing MDPI AG, 2009 11(2019), 2, p 211 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:11 year:2019 number:2, p 211 https://doi.org/10.3390/rs11020211 kostenfrei https://doaj.org/article/ba784cf40f89462ea68e416a22714972 kostenfrei https://www.mdpi.com/2072-4292/11/2/211 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2019 2, p 211
allfieldsSound	10.3390/rs11020211 doi (DE-627)DOAJ073819689 (DE-599)DOAJba784cf40f89462ea68e416a22714972 DE-627 ger DE-627 rakwb eng Wuming Zhang verfasserin aut A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation Science Q Peng Wan verfasserin aut Tiejun Wang verfasserin aut Shangshu Cai verfasserin aut Yiming Chen verfasserin aut Xiuliang Jin verfasserin aut Guangjian Yan verfasserin aut In Remote Sensing MDPI AG, 2009 11(2019), 2, p 211 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:11 year:2019 number:2, p 211 https://doi.org/10.3390/rs11020211 kostenfrei https://doaj.org/article/ba784cf40f89462ea68e416a22714972 kostenfrei https://www.mdpi.com/2072-4292/11/2/211 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2019 2, p 211
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topic_facet	tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation Science Q
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authorswithroles_txt_mv	Wuming Zhang @@aut@@ Peng Wan @@aut@@ Tiejun Wang @@aut@@ Shangshu Cai @@aut@@ Yiming Chen @@aut@@ Xiuliang Jin @@aut@@ Guangjian Yan @@aut@@
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author	Wuming Zhang
spellingShingle	Wuming Zhang misc tree stem extraction misc terrestrial laser scanning misc segment-based classification misc connected component segmentation misc Science misc Q A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data
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topic_title	A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data tree stem extraction terrestrial laser scanning segment-based classification connected component segmentation
topic	misc tree stem extraction misc terrestrial laser scanning misc segment-based classification misc connected component segmentation misc Science misc Q
topic_unstemmed	misc tree stem extraction misc terrestrial laser scanning misc segment-based classification misc connected component segmentation misc Science misc Q
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title	A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data
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title_full	A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data
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title_sort	novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data
title_auth	A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data
abstract	Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
abstractGer	Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
abstract_unstemmed	Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
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title_short	A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data
url	https://doi.org/10.3390/rs11020211 https://doaj.org/article/ba784cf40f89462ea68e416a22714972 https://www.mdpi.com/2072-4292/11/2/211 https://doaj.org/toc/2072-4292
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up_date	2024-07-03T19:47:26.620Z
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A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data

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