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Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery

Alberto Hornero, I. Marengo, N. Faria, Rocio Hernandez-Clemente

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Swansea University Authors: Alberto Hornero, Rocio Hernandez-Clemente

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DOI (Published version): 10.1109/igarss46834.2022.9884701

Abstract

Oak decline is a complex syndrome that increasingly affects the survival of oak species worldwide. Spectral-based physiological plant traits (PTs) indicators have successfully quantified pigment degradation and vegetation structure. The specific response of PTs to decline diseases has been answered...

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Published in: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
ISBN: 978-1-6654-2793-7 978-1-6654-2792-0
ISSN: 2153-6996 2153-7003
Published: IEEE 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62026
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first_indexed 2022-11-24T11:21:00Z
last_indexed 2023-01-13T19:23:12Z
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spelling 2022-12-29T13:21:28.5413314 v2 62026 2022-11-24 Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery 3140d9cb2dde2c093d42d5bf3b85d05e Alberto Hornero Alberto Hornero true false 0b007e63ef097cd47d6bc60b58379103 Rocio Hernandez-Clemente Rocio Hernandez-Clemente true false 2022-11-24 FGSEN Oak decline is a complex syndrome that increasingly affects the survival of oak species worldwide. Spectral-based physiological plant traits (PTs) indicators have successfully quantified pigment degradation and vegetation structure. The specific response of PTs to decline diseases has been answered by using high spectral- and spatial-resolution hyperspectral and thermal sensors onboard airborne platforms in the context of oak disease detection and monitoring. However, the capacity for early detection using a limited number of spectral bands, with miniaturised sensors of lower sensitivity, is unknown, seeking outstanding operability through cost-effective platforms, which is critical to detect irreversible damage timely. We evaluate the use of multispectral and thermal imagery onboard a drone together with a 3-D radiative transfer model (RTM) approach to assess a predictive symbolic classification model of Phytophthora-infected holm and cork oak areas located in Ourique (southern Portugal). The field survey comprised more than 390 trees across disease severity classes with varying disease-incidence levels and species. The classification model showed up to 83% overall accuracy ( k=0.46 ) for decline detection. The proposed model allowed us to efficiently identify the physiological state of the forest canopy so that disease progression can be detected and mapped rapidly, which is essential for fighting oak decline when silvicultural practices, such as tree removal and clearing, can still prevent the spread of dieback processes. Therefore, our study demonstrates that the tandem use of multispectral and thermal sensors, together with an RTM and AI approach, helps us predict the impact of this particularly damaging disease on oak trees. Conference Paper/Proceeding/Abstract IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium IEEE 978-1-6654-2793-7 978-1-6654-2792-0 2153-6996 2153-7003 28 9 2022 2022-09-28 10.1109/igarss46834.2022.9884701 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2022-12-29T13:21:28.5413314 2022-11-24T11:17:32.9615265 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Alberto Hornero 1 I. Marengo 2 N. Faria 3 Rocio Hernandez-Clemente 4
title Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
spellingShingle Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
Alberto Hornero
Rocio Hernandez-Clemente
title_short Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
title_full Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
title_fullStr Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
title_full_unstemmed Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
title_sort Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
author_id_str_mv 3140d9cb2dde2c093d42d5bf3b85d05e
0b007e63ef097cd47d6bc60b58379103
author_id_fullname_str_mv 3140d9cb2dde2c093d42d5bf3b85d05e_***_Alberto Hornero
0b007e63ef097cd47d6bc60b58379103_***_Rocio Hernandez-Clemente
author Alberto Hornero
Rocio Hernandez-Clemente
author2 Alberto Hornero
I. Marengo
N. Faria
Rocio Hernandez-Clemente
format Conference Paper/Proceeding/Abstract
container_title IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
publishDate 2022
institution Swansea University
isbn 978-1-6654-2793-7
978-1-6654-2792-0
issn 2153-6996
2153-7003
doi_str_mv 10.1109/igarss46834.2022.9884701
publisher IEEE
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
document_store_str 0
active_str 0
description Oak decline is a complex syndrome that increasingly affects the survival of oak species worldwide. Spectral-based physiological plant traits (PTs) indicators have successfully quantified pigment degradation and vegetation structure. The specific response of PTs to decline diseases has been answered by using high spectral- and spatial-resolution hyperspectral and thermal sensors onboard airborne platforms in the context of oak disease detection and monitoring. However, the capacity for early detection using a limited number of spectral bands, with miniaturised sensors of lower sensitivity, is unknown, seeking outstanding operability through cost-effective platforms, which is critical to detect irreversible damage timely. We evaluate the use of multispectral and thermal imagery onboard a drone together with a 3-D radiative transfer model (RTM) approach to assess a predictive symbolic classification model of Phytophthora-infected holm and cork oak areas located in Ourique (southern Portugal). The field survey comprised more than 390 trees across disease severity classes with varying disease-incidence levels and species. The classification model showed up to 83% overall accuracy ( k=0.46 ) for decline detection. The proposed model allowed us to efficiently identify the physiological state of the forest canopy so that disease progression can be detected and mapped rapidly, which is essential for fighting oak decline when silvicultural practices, such as tree removal and clearing, can still prevent the spread of dieback processes. Therefore, our study demonstrates that the tandem use of multispectral and thermal sensors, together with an RTM and AI approach, helps us predict the impact of this particularly damaging disease on oak trees.
published_date 2022-09-28T04:21:19Z
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score 11.012678

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