Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

Hyperspectral imaging data have been rarely focused on studies of mangrove pests and diseases. With leaf hyperspectral imaging data, this study aims to extract the sensitive spectral and textural features related to information of mangrove pest and disease using successive projection algorithm (SPA)...

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Autores principales:	Xiapeng Jiang, Jianing Zhen, Jing Miao, Demei Zhao, Junjie Wang, Sen Jia
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Mangrove Hyperspectral image Pest and disease severity Successive projection algorithm Random forest regression Leaf trait Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/7cd968ff661f4fcdbd80fd61f5f4d8fb
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spelling	oai:doaj.org-article:7cd968ff661f4fcdbd80fd61f5f4d8fb2021-12-01T04:55:25ZAssessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy1470-160X10.1016/j.ecolind.2021.107901https://doaj.org/article/7cd968ff661f4fcdbd80fd61f5f4d8fb2021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21005665https://doaj.org/toc/1470-160XHyperspectral imaging data have been rarely focused on studies of mangrove pests and diseases. With leaf hyperspectral imaging data, this study aims to extract the sensitive spectral and textural features related to information of mangrove pest and disease using successive projection algorithm (SPA), and to model and visualize leaf traits in response to different pest and disease severity using random forest (RF). The results showed that multiple repetitions of SPA and RF modeling operations could provide a robust set of sensitive features and reliable accuracies of vegetation parameter estimation. Among the five types of features (450 bands of original and first derivative reflectance, 52 vegetation indices, 112 texture features, and all coupling features), the RF models with 33 sensitive features chosen from the coupling of all the 1064 features, 13 sensitive wavelengths with first derivative reflectance, and 30 sensitive wavelengths with first derivative reflectance reported the optimal validation performance (mean R2Val = 0.752, 0.671, and 0.658) in estimating pest and disease severity, leaf SPAD-502, and leaf NBI values, respectively. Moreover, the two leaf trait values increased with decreasing severity of pest and disease based on the leaf trait visualization map using the optimal SPA-RF model. We conclude that the combination of SPA-RF model and hyperspectral imaging had great potential in detecting the spatial distribution of leaf traits under different pest and disease severity. The leaf-level study could lay foundation for early warning and monitoring of mangrove pests and diseases at the landscape or region level.Xiapeng JiangJianing ZhenJing MiaoDemei ZhaoJunjie WangSen JiaElsevierarticleMangroveHyperspectral imagePest and disease severitySuccessive projection algorithmRandom forest regressionLeaf traitEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107901- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mangrove Hyperspectral image Pest and disease severity Successive projection algorithm Random forest regression Leaf trait Ecology QH540-549.5
spellingShingle	Mangrove Hyperspectral image Pest and disease severity Successive projection algorithm Random forest regression Leaf trait Ecology QH540-549.5 Xiapeng Jiang Jianing Zhen Jing Miao Demei Zhao Junjie Wang Sen Jia Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
description	Hyperspectral imaging data have been rarely focused on studies of mangrove pests and diseases. With leaf hyperspectral imaging data, this study aims to extract the sensitive spectral and textural features related to information of mangrove pest and disease using successive projection algorithm (SPA), and to model and visualize leaf traits in response to different pest and disease severity using random forest (RF). The results showed that multiple repetitions of SPA and RF modeling operations could provide a robust set of sensitive features and reliable accuracies of vegetation parameter estimation. Among the five types of features (450 bands of original and first derivative reflectance, 52 vegetation indices, 112 texture features, and all coupling features), the RF models with 33 sensitive features chosen from the coupling of all the 1064 features, 13 sensitive wavelengths with first derivative reflectance, and 30 sensitive wavelengths with first derivative reflectance reported the optimal validation performance (mean R2Val = 0.752, 0.671, and 0.658) in estimating pest and disease severity, leaf SPAD-502, and leaf NBI values, respectively. Moreover, the two leaf trait values increased with decreasing severity of pest and disease based on the leaf trait visualization map using the optimal SPA-RF model. We conclude that the combination of SPA-RF model and hyperspectral imaging had great potential in detecting the spatial distribution of leaf traits under different pest and disease severity. The leaf-level study could lay foundation for early warning and monitoring of mangrove pests and diseases at the landscape or region level.
format	article
author	Xiapeng Jiang Jianing Zhen Jing Miao Demei Zhao Junjie Wang Sen Jia
author_facet	Xiapeng Jiang Jianing Zhen Jing Miao Demei Zhao Junjie Wang Sen Jia
author_sort	Xiapeng Jiang
title	Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
title_short	Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
title_full	Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
title_fullStr	Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
title_full_unstemmed	Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
title_sort	assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7cd968ff661f4fcdbd80fd61f5f4d8fb
work_keys_str_mv	AT xiapengjiang assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy AT jianingzhen assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy AT jingmiao assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy AT demeizhao assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy AT junjiewang assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy AT senjia assessingmangroveleaftraitsunderdifferentpestanddiseaseseveritywithhyperspectralimagingspectroscopy
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Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

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