No Cover Image

Journal article 285 views 98 downloads

Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery

T. Poblete, J.A. Navas-Cortes, A. Hornero, C. Camino, R. Calderon, Rocio Hernandez-Clemente, B.B. Landa, P.J. Zarco-Tejada

Remote Sensing of Environment, Volume: 295, Start page: 113698

Swansea University Author: Rocio Hernandez-Clemente

  • 63772.pdf

    PDF | Version of Record

    © 2023 The Authors. Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).

    Download (5.21MB)

Check full text

DOI (Published version): 10.1016/j.rse.2023.113698

Abstract

Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens r...

Full description

Published in: Remote Sensing of Environment
ISSN: 0034-4257
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63772
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2023-07-04T09:52:07Z
last_indexed 2023-07-04T09:52:07Z
id cronfa63772
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>63772</id><entry>2023-07-04</entry><title>Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery</title><swanseaauthors><author><sid>0b007e63ef097cd47d6bc60b58379103</sid><firstname>Rocio</firstname><surname>Hernandez-Clemente</surname><name>Rocio Hernandez-Clemente</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-07-04</date><deptcode>FGSEN</deptcode><abstract>Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by &gt;0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.</abstract><type>Journal Article</type><journal>Remote Sensing of Environment</journal><volume>295</volume><journalNumber/><paginationStart>113698</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0034-4257</issnPrint><issnElectronic/><keywords>Hyperspectral, Multispectral, Worldview-2 and -3, Plant traits, Operational plant disease detection, Verticillium dahliae, Xylella fastidiosa, Olive, Almond</keywords><publishedDay>30</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-09-30</publishedDate><doi>10.1016/j.rse.2023.113698</doi><url>http://dx.doi.org/10.1016/j.rse.2023.113698</url><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>The study was partially funded by the European Union's Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 and P18-RT-4184 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from “Programa Estatal de I+D+I Orientada a los Retos de la Sociedad” of Spain and FEDER, Intramural Project 201840E111 from CSIC and ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain.</funders><projectreference/><lastEdited>2023-08-16T12:01:33.4619224</lastEdited><Created>2023-07-04T10:44:16.8058677</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Geography</level></path><authors><author><firstname>T.</firstname><surname>Poblete</surname><order>1</order></author><author><firstname>J.A.</firstname><surname>Navas-Cortes</surname><order>2</order></author><author><firstname>A.</firstname><surname>Hornero</surname><order>3</order></author><author><firstname>C.</firstname><surname>Camino</surname><order>4</order></author><author><firstname>R.</firstname><surname>Calderon</surname><order>5</order></author><author><firstname>Rocio</firstname><surname>Hernandez-Clemente</surname><order>6</order></author><author><firstname>B.B.</firstname><surname>Landa</surname><order>7</order></author><author><firstname>P.J.</firstname><surname>Zarco-Tejada</surname><order>8</order></author></authors><documents><document><filename>63772__28034__b31bc944a7be4ccea7d105d772b6181f.pdf</filename><originalFilename>63772.pdf</originalFilename><uploaded>2023-07-04T10:51:58.8437007</uploaded><type>Output</type><contentLength>5468283</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2023 The Authors. Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 63772 2023-07-04 Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery 0b007e63ef097cd47d6bc60b58379103 Rocio Hernandez-Clemente Rocio Hernandez-Clemente true false 2023-07-04 FGSEN Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data. Journal Article Remote Sensing of Environment 295 113698 Elsevier BV 0034-4257 Hyperspectral, Multispectral, Worldview-2 and -3, Plant traits, Operational plant disease detection, Verticillium dahliae, Xylella fastidiosa, Olive, Almond 30 9 2023 2023-09-30 10.1016/j.rse.2023.113698 http://dx.doi.org/10.1016/j.rse.2023.113698 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University The study was partially funded by the European Union's Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 and P18-RT-4184 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from “Programa Estatal de I+D+I Orientada a los Retos de la Sociedad” of Spain and FEDER, Intramural Project 201840E111 from CSIC and ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain. 2023-08-16T12:01:33.4619224 2023-07-04T10:44:16.8058677 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography T. Poblete 1 J.A. Navas-Cortes 2 A. Hornero 3 C. Camino 4 R. Calderon 5 Rocio Hernandez-Clemente 6 B.B. Landa 7 P.J. Zarco-Tejada 8 63772__28034__b31bc944a7be4ccea7d105d772b6181f.pdf 63772.pdf 2023-07-04T10:51:58.8437007 Output 5468283 application/pdf Version of Record true © 2023 The Authors. Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
spellingShingle Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
Rocio Hernandez-Clemente
title_short Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
title_full Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
title_fullStr Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
title_full_unstemmed Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
title_sort Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery
author_id_str_mv 0b007e63ef097cd47d6bc60b58379103
author_id_fullname_str_mv 0b007e63ef097cd47d6bc60b58379103_***_Rocio Hernandez-Clemente
author Rocio Hernandez-Clemente
author2 T. Poblete
J.A. Navas-Cortes
A. Hornero
C. Camino
R. Calderon
Rocio Hernandez-Clemente
B.B. Landa
P.J. Zarco-Tejada
format Journal article
container_title Remote Sensing of Environment
container_volume 295
container_start_page 113698
publishDate 2023
institution Swansea University
issn 0034-4257
doi_str_mv 10.1016/j.rse.2023.113698
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
url http://dx.doi.org/10.1016/j.rse.2023.113698
document_store_str 1
active_str 0
description Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.
published_date 2023-09-30T12:01:34Z
_version_ 1774383209550708736
score 11.013596

Similar Items