Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale
Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Jingcheng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pest management science - Chichester : Wiley, 2000, 72(2016), 2, Seite 335-348 |
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| Übergeordnetes Werk: |
volume:72 ; year:2016 ; number:2 ; pages:335-348 |
| Links: |
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| DOI / URN: |
10.1002/ps.4003 |
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| 520 | |a Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. | ||
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10.1002/ps.4003 doi PQ20160617 (DE-627)OLC1963365356 (DE-599)GBVOLC1963365356 (PRQ)p833-ffc187faf13cbfd60b01500cc90f2240f14020f3510a374212d1979a8f0a95d73 (KEY)0016093820160000072000200335usingsatellitemultispectralimageryfordamagemapping DE-627 ger DE-627 rakwb eng 570 580 630 640 660 DNB 48.54 bkl Zhang, Jingcheng verfasserin aut Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. modified soil‐adjusted vegetation index mapping armyworm maize multispectral remote sensing Huang, Yanbo oth Yuan, Lin oth Yang, Guijun oth Chen, Liping oth Zhao, Chunjiang oth Enthalten in Pest management science Chichester : Wiley, 2000 72(2016), 2, Seite 335-348 (DE-627)309622565 (DE-600)2001705-4 (DE-576)084508841 1526-498X nnns volume:72 year:2016 number:2 pages:335-348 http://dx.doi.org/10.1002/ps.4003 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ps.4003/abstract http://www.ncbi.nlm.nih.gov/pubmed/25761201 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 48.54 AVZ AR 72 2016 2 335-348 |
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10.1002/ps.4003 doi PQ20160617 (DE-627)OLC1963365356 (DE-599)GBVOLC1963365356 (PRQ)p833-ffc187faf13cbfd60b01500cc90f2240f14020f3510a374212d1979a8f0a95d73 (KEY)0016093820160000072000200335usingsatellitemultispectralimageryfordamagemapping DE-627 ger DE-627 rakwb eng 570 580 630 640 660 DNB 48.54 bkl Zhang, Jingcheng verfasserin aut Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. modified soil‐adjusted vegetation index mapping armyworm maize multispectral remote sensing Huang, Yanbo oth Yuan, Lin oth Yang, Guijun oth Chen, Liping oth Zhao, Chunjiang oth Enthalten in Pest management science Chichester : Wiley, 2000 72(2016), 2, Seite 335-348 (DE-627)309622565 (DE-600)2001705-4 (DE-576)084508841 1526-498X nnns volume:72 year:2016 number:2 pages:335-348 http://dx.doi.org/10.1002/ps.4003 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ps.4003/abstract http://www.ncbi.nlm.nih.gov/pubmed/25761201 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 48.54 AVZ AR 72 2016 2 335-348 |
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10.1002/ps.4003 doi PQ20160617 (DE-627)OLC1963365356 (DE-599)GBVOLC1963365356 (PRQ)p833-ffc187faf13cbfd60b01500cc90f2240f14020f3510a374212d1979a8f0a95d73 (KEY)0016093820160000072000200335usingsatellitemultispectralimageryfordamagemapping DE-627 ger DE-627 rakwb eng 570 580 630 640 660 DNB 48.54 bkl Zhang, Jingcheng verfasserin aut Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. modified soil‐adjusted vegetation index mapping armyworm maize multispectral remote sensing Huang, Yanbo oth Yuan, Lin oth Yang, Guijun oth Chen, Liping oth Zhao, Chunjiang oth Enthalten in Pest management science Chichester : Wiley, 2000 72(2016), 2, Seite 335-348 (DE-627)309622565 (DE-600)2001705-4 (DE-576)084508841 1526-498X nnns volume:72 year:2016 number:2 pages:335-348 http://dx.doi.org/10.1002/ps.4003 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ps.4003/abstract http://www.ncbi.nlm.nih.gov/pubmed/25761201 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 48.54 AVZ AR 72 2016 2 335-348 |
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10.1002/ps.4003 doi PQ20160617 (DE-627)OLC1963365356 (DE-599)GBVOLC1963365356 (PRQ)p833-ffc187faf13cbfd60b01500cc90f2240f14020f3510a374212d1979a8f0a95d73 (KEY)0016093820160000072000200335usingsatellitemultispectralimageryfordamagemapping DE-627 ger DE-627 rakwb eng 570 580 630 640 660 DNB 48.54 bkl Zhang, Jingcheng verfasserin aut Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. modified soil‐adjusted vegetation index mapping armyworm maize multispectral remote sensing Huang, Yanbo oth Yuan, Lin oth Yang, Guijun oth Chen, Liping oth Zhao, Chunjiang oth Enthalten in Pest management science Chichester : Wiley, 2000 72(2016), 2, Seite 335-348 (DE-627)309622565 (DE-600)2001705-4 (DE-576)084508841 1526-498X nnns volume:72 year:2016 number:2 pages:335-348 http://dx.doi.org/10.1002/ps.4003 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ps.4003/abstract http://www.ncbi.nlm.nih.gov/pubmed/25761201 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 48.54 AVZ AR 72 2016 2 335-348 |
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10.1002/ps.4003 doi PQ20160617 (DE-627)OLC1963365356 (DE-599)GBVOLC1963365356 (PRQ)p833-ffc187faf13cbfd60b01500cc90f2240f14020f3510a374212d1979a8f0a95d73 (KEY)0016093820160000072000200335usingsatellitemultispectralimageryfordamagemapping DE-627 ger DE-627 rakwb eng 570 580 630 640 660 DNB 48.54 bkl Zhang, Jingcheng verfasserin aut Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. Nutzungsrecht: © 2015 Society of Chemical Industry © 2015 Society of Chemical Industry. modified soil‐adjusted vegetation index mapping armyworm maize multispectral remote sensing Huang, Yanbo oth Yuan, Lin oth Yang, Guijun oth Chen, Liping oth Zhao, Chunjiang oth Enthalten in Pest management science Chichester : Wiley, 2000 72(2016), 2, Seite 335-348 (DE-627)309622565 (DE-600)2001705-4 (DE-576)084508841 1526-498X nnns volume:72 year:2016 number:2 pages:335-348 http://dx.doi.org/10.1002/ps.4003 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ps.4003/abstract http://www.ncbi.nlm.nih.gov/pubmed/25761201 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 48.54 AVZ AR 72 2016 2 335-348 |
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To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. 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using satellite multispectral imagery for damage mapping of armyworm (spodoptera frugiperda) in maize at a regional scale |
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Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale |
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Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. |
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Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. |
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Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data. Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79. A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice. © 2015 Society of Chemical Industry. |
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Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale |
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