Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging

Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Azadeh Abdollahnejad [verfasserIn] Dimitrios Panagiotidis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	photogrammetry spectral analysis texture analysis support vector machines forest health multispectral imaging

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 22, p 3722
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:22, p 3722

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12223722

Katalog-ID:	DOAJ04620900X

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spelling	10.3390/rs12223722 doi (DE-627)DOAJ04620900X (DE-599)DOAJ506b177bae8e4cde97e047182fe41404 DE-627 ger DE-627 rakwb eng Azadeh Abdollahnejad verfasserin aut Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs. photogrammetry spectral analysis texture analysis support vector machines forest health multispectral imaging Science Q Dimitrios Panagiotidis verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 22, p 3722 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:22, p 3722 https://doi.org/10.3390/rs12223722 kostenfrei https://doaj.org/article/506b177bae8e4cde97e047182fe41404 kostenfrei https://www.mdpi.com/2072-4292/12/22/3722 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 22, p 3722
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allfieldsSound	10.3390/rs12223722 doi (DE-627)DOAJ04620900X (DE-599)DOAJ506b177bae8e4cde97e047182fe41404 DE-627 ger DE-627 rakwb eng Azadeh Abdollahnejad verfasserin aut Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs. photogrammetry spectral analysis texture analysis support vector machines forest health multispectral imaging Science Q Dimitrios Panagiotidis verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 22, p 3722 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:22, p 3722 https://doi.org/10.3390/rs12223722 kostenfrei https://doaj.org/article/506b177bae8e4cde97e047182fe41404 kostenfrei https://www.mdpi.com/2072-4292/12/22/3722 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 22, p 3722
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author	Azadeh Abdollahnejad
spellingShingle	Azadeh Abdollahnejad misc photogrammetry misc spectral analysis misc texture analysis misc support vector machines misc forest health misc multispectral imaging misc Science misc Q Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging
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topic_title	Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging photogrammetry spectral analysis texture analysis support vector machines forest health multispectral imaging
topic	misc photogrammetry misc spectral analysis misc texture analysis misc support vector machines misc forest health misc multispectral imaging misc Science misc Q
topic_unstemmed	misc photogrammetry misc spectral analysis misc texture analysis misc support vector machines misc forest health misc multispectral imaging misc Science misc Q
topic_browse	misc photogrammetry misc spectral analysis misc texture analysis misc support vector machines misc forest health misc multispectral imaging misc Science misc Q
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title	Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging
ctrlnum	(DE-627)DOAJ04620900X (DE-599)DOAJ506b177bae8e4cde97e047182fe41404
title_full	Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging
author_sort	Azadeh Abdollahnejad
journal	Remote Sensing
journalStr	Remote Sensing
lang_code	eng
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author_browse	Azadeh Abdollahnejad Dimitrios Panagiotidis
container_volume	12
format_se	Elektronische Aufsätze
author-letter	Azadeh Abdollahnejad
doi_str_mv	10.3390/rs12223722
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title_sort	tree species classification and health status assessment for a mixed broadleaf-conifer forest with uas multispectral imaging
title_auth	Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging
abstract	Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs.
abstractGer	Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs.
abstract_unstemmed	Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs.
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title_short	Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging
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