Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispec...
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Autor*in:	Melaine Aubry-Kientz [verfasserIn] Anthony Laybros [verfasserIn] Ben Weinstein [verfasserIn] James Ball [verfasserIn] Toby Jackson [verfasserIn] David Coomes [verfasserIn] Gregoire Vincent [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D)

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 14(2021), Seite 3927-3936
Übergeordnetes Werk:	volume:14 ; year:2021 ; pages:3927-3936

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2021.3069159

Katalog-ID:	DOAJ054130573

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520			\|a Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.
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650		4	\|a 3-D adaptive mean-shift (AMS3D)
653		0	\|a Ocean engineering
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700	0		\|a Gregoire Vincent \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2021.3069159 doi (DE-627)DOAJ054130573 (DE-599)DOAJd64d54fa2f0544dba4d3794bc754cdf8 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Melaine Aubry-Kientz verfasserin aut Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions. Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics Anthony Laybros verfasserin aut Ben Weinstein verfasserin aut James Ball verfasserin aut Toby Jackson verfasserin aut David Coomes verfasserin aut Gregoire Vincent verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 3927-3936 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:3927-3936 https://doi.org/10.1109/JSTARS.2021.3069159 kostenfrei https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 kostenfrei https://ieeexplore.ieee.org/document/9387530/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 3927-3936
spelling	10.1109/JSTARS.2021.3069159 doi (DE-627)DOAJ054130573 (DE-599)DOAJd64d54fa2f0544dba4d3794bc754cdf8 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Melaine Aubry-Kientz verfasserin aut Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions. Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics Anthony Laybros verfasserin aut Ben Weinstein verfasserin aut James Ball verfasserin aut Toby Jackson verfasserin aut David Coomes verfasserin aut Gregoire Vincent verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 3927-3936 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:3927-3936 https://doi.org/10.1109/JSTARS.2021.3069159 kostenfrei https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 kostenfrei https://ieeexplore.ieee.org/document/9387530/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 3927-3936
allfields_unstemmed	10.1109/JSTARS.2021.3069159 doi (DE-627)DOAJ054130573 (DE-599)DOAJd64d54fa2f0544dba4d3794bc754cdf8 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Melaine Aubry-Kientz verfasserin aut Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions. Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics Anthony Laybros verfasserin aut Ben Weinstein verfasserin aut James Ball verfasserin aut Toby Jackson verfasserin aut David Coomes verfasserin aut Gregoire Vincent verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 3927-3936 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:3927-3936 https://doi.org/10.1109/JSTARS.2021.3069159 kostenfrei https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 kostenfrei https://ieeexplore.ieee.org/document/9387530/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 3927-3936
allfieldsGer	10.1109/JSTARS.2021.3069159 doi (DE-627)DOAJ054130573 (DE-599)DOAJd64d54fa2f0544dba4d3794bc754cdf8 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Melaine Aubry-Kientz verfasserin aut Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions. Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics Anthony Laybros verfasserin aut Ben Weinstein verfasserin aut James Ball verfasserin aut Toby Jackson verfasserin aut David Coomes verfasserin aut Gregoire Vincent verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 3927-3936 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:3927-3936 https://doi.org/10.1109/JSTARS.2021.3069159 kostenfrei https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 kostenfrei https://ieeexplore.ieee.org/document/9387530/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 3927-3936
allfieldsSound	10.1109/JSTARS.2021.3069159 doi (DE-627)DOAJ054130573 (DE-599)DOAJd64d54fa2f0544dba4d3794bc754cdf8 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Melaine Aubry-Kientz verfasserin aut Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions. Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics Anthony Laybros verfasserin aut Ben Weinstein verfasserin aut James Ball verfasserin aut Toby Jackson verfasserin aut David Coomes verfasserin aut Gregoire Vincent verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 14(2021), Seite 3927-3936 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:14 year:2021 pages:3927-3936 https://doi.org/10.1109/JSTARS.2021.3069159 kostenfrei https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 kostenfrei https://ieeexplore.ieee.org/document/9387530/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 3927-3936
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 3927-3936 volume:14 year:2021 pages:3927-3936
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), Seite 3927-3936 volume:14 year:2021 pages:3927-3936
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D) Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Melaine Aubry-Kientz @@aut@@ Anthony Laybros @@aut@@ Ben Weinstein @@aut@@ James Ball @@aut@@ Toby Jackson @@aut@@ David Coomes @@aut@@ Gregoire Vincent @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Melaine Aubry-Kientz
spellingShingle	Melaine Aubry-Kientz misc TC1501-1800 misc QC801-809 misc Airborne laser scanning (ALS) misc data fusion misc deepforest misc high-resolution imagery misc hyperspectral misc 3-D adaptive mean-shift (AMS3D) misc Ocean engineering misc Geophysics. Cosmic physics Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
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topic_title	TC1501-1800 QC801-809 Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests Airborne laser scanning (ALS) data fusion deepforest high-resolution imagery hyperspectral 3-D adaptive mean-shift (AMS3D)
topic	misc TC1501-1800 misc QC801-809 misc Airborne laser scanning (ALS) misc data fusion misc deepforest misc high-resolution imagery misc hyperspectral misc 3-D adaptive mean-shift (AMS3D) misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Airborne laser scanning (ALS) misc data fusion misc deepforest misc high-resolution imagery misc hyperspectral misc 3-D adaptive mean-shift (AMS3D) misc Ocean engineering misc Geophysics. Cosmic physics
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title	Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
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title_full	Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
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title_sort	multisensor data fusion for improved segmentation of individual tree crowns in dense tropical forests
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title_auth	Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
abstract	Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.
abstractGer	Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.
abstract_unstemmed	Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.
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title_short	Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
url	https://doi.org/10.1109/JSTARS.2021.3069159 https://doaj.org/article/d64d54fa2f0544dba4d3794bc754cdf8 https://ieeexplore.ieee.org/document/9387530/ https://doaj.org/toc/2151-1535
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Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence &gt;0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. 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Cosmic physics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anthony Laybros</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ben Weinstein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">James Ball</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Toby Jackson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">David Coomes</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gregoire Vincent</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</subfield><subfield code="d">IEEE, 2020</subfield><subfield code="g">14(2021), Seite 3927-3936</subfield><subfield code="w">(DE-627)581732634</subfield><subfield code="w">(DE-600)2457423-5</subfield><subfield code="x">21511535</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:3927-3936</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/JSTARS.2021.3069159</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield 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Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

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