Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery
Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi...
Ausführliche Beschreibung
Autor*in: |
Benjamin Allen [verfasserIn] Michele Dalponte [verfasserIn] Ari Hietala [verfasserIn] Hans Ørka [verfasserIn] Erik Næsset [verfasserIn] Terje Gobakken [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
In: Silva Fennica - Finnish Society of Forest Science, 2019, 56(2022), 2 |
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Übergeordnetes Werk: |
volume:56 ; year:2022 ; number:2 |
Links: |
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DOI / URN: |
10.14214/sf.10606 |
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Katalog-ID: |
DOAJ013623338 |
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10.14214/sf.10606 doi (DE-627)DOAJ013623338 (DE-599)DOAJ349124c65e914df8aa06f484ae4f94f6 DE-627 ger DE-627 rakwb eng SD1-669.5 Benjamin Allen verfasserin aut Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies Forestry Michele Dalponte verfasserin aut Ari Hietala verfasserin aut Hans Ørka verfasserin aut Erik Næsset verfasserin aut Terje Gobakken verfasserin aut In Silva Fennica Finnish Society of Forest Science, 2019 56(2022), 2 (DE-627)320575039 (DE-600)2016943-7 22424075 nnns volume:56 year:2022 number:2 https://doi.org/10.14214/sf.10606 kostenfrei https://doaj.org/article/349124c65e914df8aa06f484ae4f94f6 kostenfrei https://www.silvafennica.fi/article/10606 kostenfrei https://doaj.org/toc/2242-4075 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 56 2022 2 |
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10.14214/sf.10606 doi (DE-627)DOAJ013623338 (DE-599)DOAJ349124c65e914df8aa06f484ae4f94f6 DE-627 ger DE-627 rakwb eng SD1-669.5 Benjamin Allen verfasserin aut Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies Forestry Michele Dalponte verfasserin aut Ari Hietala verfasserin aut Hans Ørka verfasserin aut Erik Næsset verfasserin aut Terje Gobakken verfasserin aut In Silva Fennica Finnish Society of Forest Science, 2019 56(2022), 2 (DE-627)320575039 (DE-600)2016943-7 22424075 nnns volume:56 year:2022 number:2 https://doi.org/10.14214/sf.10606 kostenfrei https://doaj.org/article/349124c65e914df8aa06f484ae4f94f6 kostenfrei https://www.silvafennica.fi/article/10606 kostenfrei https://doaj.org/toc/2242-4075 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 56 2022 2 |
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10.14214/sf.10606 doi (DE-627)DOAJ013623338 (DE-599)DOAJ349124c65e914df8aa06f484ae4f94f6 DE-627 ger DE-627 rakwb eng SD1-669.5 Benjamin Allen verfasserin aut Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies Forestry Michele Dalponte verfasserin aut Ari Hietala verfasserin aut Hans Ørka verfasserin aut Erik Næsset verfasserin aut Terje Gobakken verfasserin aut In Silva Fennica Finnish Society of Forest Science, 2019 56(2022), 2 (DE-627)320575039 (DE-600)2016943-7 22424075 nnns volume:56 year:2022 number:2 https://doi.org/10.14214/sf.10606 kostenfrei https://doaj.org/article/349124c65e914df8aa06f484ae4f94f6 kostenfrei https://www.silvafennica.fi/article/10606 kostenfrei https://doaj.org/toc/2242-4075 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 56 2022 2 |
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10.14214/sf.10606 doi (DE-627)DOAJ013623338 (DE-599)DOAJ349124c65e914df8aa06f484ae4f94f6 DE-627 ger DE-627 rakwb eng SD1-669.5 Benjamin Allen verfasserin aut Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies Forestry Michele Dalponte verfasserin aut Ari Hietala verfasserin aut Hans Ørka verfasserin aut Erik Næsset verfasserin aut Terje Gobakken verfasserin aut In Silva Fennica Finnish Society of Forest Science, 2019 56(2022), 2 (DE-627)320575039 (DE-600)2016943-7 22424075 nnns volume:56 year:2022 number:2 https://doi.org/10.14214/sf.10606 kostenfrei https://doaj.org/article/349124c65e914df8aa06f484ae4f94f6 kostenfrei https://www.silvafennica.fi/article/10606 kostenfrei https://doaj.org/toc/2242-4075 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 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_4367 GBV_ILN_4700 AR 56 2022 2 |
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Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery |
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detection of root, butt, and stem rot presence in norway spruce with hyperspectral imagery |
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Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery |
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Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies |
abstractGer |
Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies |
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Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies |
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|
score |
7.4015017 |