Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease

Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiapeng Jiang [verfasserIn] Jianing Zhen [verfasserIn] Jing Miao [verfasserIn] Demei Zhao [verfasserIn] Zhen Shen [verfasserIn] Jincheng Jiang [verfasserIn] Changjun Gao [verfasserIn] Guofeng Wu [verfasserIn] Junjie Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 140(2022), Seite 108978-
Übergeordnetes Werk:	volume:140 ; year:2022 ; pages:108978-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2022.108978

Katalog-ID:	DOAJ043301312

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520			\|a Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content.
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allfields	10.1016/j.ecolind.2022.108978 doi (DE-627)DOAJ043301312 (DE-599)DOAJ45c3930080eb40b08c559056ea0072ef DE-627 ger DE-627 rakwb eng QH540-549.5 Xiapeng Jiang verfasserin aut Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology Jianing Zhen verfasserin aut Jing Miao verfasserin aut Demei Zhao verfasserin aut Zhen Shen verfasserin aut Jincheng Jiang verfasserin aut Changjun Gao verfasserin aut Guofeng Wu verfasserin aut Junjie Wang verfasserin aut In Ecological Indicators Elsevier, 2021 140(2022), Seite 108978- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:140 year:2022 pages:108978- https://doi.org/10.1016/j.ecolind.2022.108978 kostenfrei https://doaj.org/article/45c3930080eb40b08c559056ea0072ef kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22004496 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 140 2022 108978-
spelling	10.1016/j.ecolind.2022.108978 doi (DE-627)DOAJ043301312 (DE-599)DOAJ45c3930080eb40b08c559056ea0072ef DE-627 ger DE-627 rakwb eng QH540-549.5 Xiapeng Jiang verfasserin aut Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology Jianing Zhen verfasserin aut Jing Miao verfasserin aut Demei Zhao verfasserin aut Zhen Shen verfasserin aut Jincheng Jiang verfasserin aut Changjun Gao verfasserin aut Guofeng Wu verfasserin aut Junjie Wang verfasserin aut In Ecological Indicators Elsevier, 2021 140(2022), Seite 108978- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:140 year:2022 pages:108978- https://doi.org/10.1016/j.ecolind.2022.108978 kostenfrei https://doaj.org/article/45c3930080eb40b08c559056ea0072ef kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22004496 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 140 2022 108978-
allfields_unstemmed	10.1016/j.ecolind.2022.108978 doi (DE-627)DOAJ043301312 (DE-599)DOAJ45c3930080eb40b08c559056ea0072ef DE-627 ger DE-627 rakwb eng QH540-549.5 Xiapeng Jiang verfasserin aut Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology Jianing Zhen verfasserin aut Jing Miao verfasserin aut Demei Zhao verfasserin aut Zhen Shen verfasserin aut Jincheng Jiang verfasserin aut Changjun Gao verfasserin aut Guofeng Wu verfasserin aut Junjie Wang verfasserin aut In Ecological Indicators Elsevier, 2021 140(2022), Seite 108978- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:140 year:2022 pages:108978- https://doi.org/10.1016/j.ecolind.2022.108978 kostenfrei https://doaj.org/article/45c3930080eb40b08c559056ea0072ef kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22004496 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 140 2022 108978-
allfieldsGer	10.1016/j.ecolind.2022.108978 doi (DE-627)DOAJ043301312 (DE-599)DOAJ45c3930080eb40b08c559056ea0072ef DE-627 ger DE-627 rakwb eng QH540-549.5 Xiapeng Jiang verfasserin aut Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology Jianing Zhen verfasserin aut Jing Miao verfasserin aut Demei Zhao verfasserin aut Zhen Shen verfasserin aut Jincheng Jiang verfasserin aut Changjun Gao verfasserin aut Guofeng Wu verfasserin aut Junjie Wang verfasserin aut In Ecological Indicators Elsevier, 2021 140(2022), Seite 108978- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:140 year:2022 pages:108978- https://doi.org/10.1016/j.ecolind.2022.108978 kostenfrei https://doaj.org/article/45c3930080eb40b08c559056ea0072ef kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22004496 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 140 2022 108978-
allfieldsSound	10.1016/j.ecolind.2022.108978 doi (DE-627)DOAJ043301312 (DE-599)DOAJ45c3930080eb40b08c559056ea0072ef DE-627 ger DE-627 rakwb eng QH540-549.5 Xiapeng Jiang verfasserin aut Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content. Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology Jianing Zhen verfasserin aut Jing Miao verfasserin aut Demei Zhao verfasserin aut Zhen Shen verfasserin aut Jincheng Jiang verfasserin aut Changjun Gao verfasserin aut Guofeng Wu verfasserin aut Junjie Wang verfasserin aut In Ecological Indicators Elsevier, 2021 140(2022), Seite 108978- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:140 year:2022 pages:108978- https://doi.org/10.1016/j.ecolind.2022.108978 kostenfrei https://doaj.org/article/45c3930080eb40b08c559056ea0072ef kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22004496 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 140 2022 108978-
language	English
source	In Ecological Indicators 140(2022), Seite 108978- volume:140 year:2022 pages:108978-
sourceStr	In Ecological Indicators 140(2022), Seite 108978- volume:140 year:2022 pages:108978-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value Ecology
isfreeaccess_bool	true
container_title	Ecological Indicators
authorswithroles_txt_mv	Xiapeng Jiang @@aut@@ Jianing Zhen @@aut@@ Jing Miao @@aut@@ Demei Zhao @@aut@@ Zhen Shen @@aut@@ Jincheng Jiang @@aut@@ Changjun Gao @@aut@@ Guofeng Wu @@aut@@ Junjie Wang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	338074163
id	DOAJ043301312
language_de	englisch
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callnumber-first	Q - Science
author	Xiapeng Jiang
spellingShingle	Xiapeng Jiang misc QH540-549.5 misc Mangrove misc Hyperspectral imaging misc Newly-developed VIs misc Pest and disease misc SPAD value misc Ecology Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
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topic_title	QH540-549.5 Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease Mangrove Hyperspectral imaging Newly-developed VIs Pest and disease SPAD value
topic	misc QH540-549.5 misc Mangrove misc Hyperspectral imaging misc Newly-developed VIs misc Pest and disease misc SPAD value misc Ecology
topic_unstemmed	misc QH540-549.5 misc Mangrove misc Hyperspectral imaging misc Newly-developed VIs misc Pest and disease misc SPAD value misc Ecology
topic_browse	misc QH540-549.5 misc Mangrove misc Hyperspectral imaging misc Newly-developed VIs misc Pest and disease misc SPAD value misc Ecology
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title	Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
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title_full	Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
author_sort	Xiapeng Jiang
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author_browse	Xiapeng Jiang Jianing Zhen Jing Miao Demei Zhao Zhen Shen Jincheng Jiang Changjun Gao Guofeng Wu Junjie Wang
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title_sort	newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
callnumber	QH540-549.5
title_auth	Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
abstract	Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content.
abstractGer	Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content.
abstract_unstemmed	Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content.
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title_short	Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
url	https://doi.org/10.1016/j.ecolind.2022.108978 https://doaj.org/article/45c3930080eb40b08c559056ea0072ef http://www.sciencedirect.com/science/article/pii/S1470160X22004496 https://doaj.org/toc/1470-160X
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Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease

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