Conference Paper/Proceeding/Abstract 307 views
Monitoring Phytophthora Disease Symptoms Through Very-High-Resolution Multispectral and Thermal Drone Imagery
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...
| Published in: | IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium |
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| ISBN: | 978-1-6654-2793-7 978-1-6654-2792-0 |
| ISSN: | 2153-6996 2153-7003 |
| Published: |
IEEE
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62026 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |