Assessing pine processionary moth defoliation using unmanned aerial systems

Pine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of...

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Autores: Cardil Forradellas, Adrián, Vepakomma, Udayalakshmi, Brotons, Lluís
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/69112
Acceso en línea:https://doi.org/10.3390/f8100402
http://hdl.handle.net/10459.1/69112
Access Level:acceso abierto
Palabra clave:Pine processionary moth
Pest
Outbreak
Point clouds
Defoliation
Health monitoring
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spelling Assessing pine processionary moth defoliation using unmanned aerial systemsCardil Forradellas, AdriánVepakomma, UdayalakshmiBrotons, LluísPine processionary mothPestOutbreakPoint cloudsDefoliationHealth monitoringPine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of PPM in forest ecology to better assess outbreak spread patterns and provide guidance on the development of measures targeting the negative impacts of the species on forests, industry and human health. Remote sensing technology mounted on unmanned aerial systems (UASs) with high-resolution image processing has been proposed to assess insect outbreak impacts at local and forest stand levels. Here, we used UAS-acquired RGB imagery in two pine sites to quantify defoliation at the tree-level and to verify the accuracy of the estimates. Our results allowed the identification of healthy, infested and completely defoliated trees and suggested that pine defoliation estimates using UASs are robust and allow high-accuracy (79%) field-based infestation indexes to be derived that are comparable to those used by forest technicians. When compared to current field-based methods, our approach provides PPM impact assessments with an efficient data acquisition method in terms of time and staff, allowing the quantitative estimation of defoliation at tree-level scale. Furthermore, our method could be expanded to a number of situations and scaled up in combination with satellite remote sensing imagery or citizen science approaches.We are grateful to the Newforests European project for supporting this study and funding Cardil’s stay in Montreal (Canada). This work was partly funded by the project “Boscos sans per una societat saludable” funded by “La Caixa” Banking Foundation” and by the Transformative Technologies Program at FPInnovations funded through Natural Resources Canada. This study has also benefited from the Parrot Climate Innovation Grant 2017.MDPI2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/f8100402http://hdl.handle.net/10459.1/69112reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.3390/f8100402Forests, 2017, vol. 8, núm. 10, p. 402cc-by (c) Cardil Forradellas, Adrián et al., 2017info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/691122026-06-24T12:42:17Z
dc.title.none.fl_str_mv Assessing pine processionary moth defoliation using unmanned aerial systems
title Assessing pine processionary moth defoliation using unmanned aerial systems
spellingShingle Assessing pine processionary moth defoliation using unmanned aerial systems
Cardil Forradellas, Adrián
Pine processionary moth
Pest
Outbreak
Point clouds
Defoliation
Health monitoring
title_short Assessing pine processionary moth defoliation using unmanned aerial systems
title_full Assessing pine processionary moth defoliation using unmanned aerial systems
title_fullStr Assessing pine processionary moth defoliation using unmanned aerial systems
title_full_unstemmed Assessing pine processionary moth defoliation using unmanned aerial systems
title_sort Assessing pine processionary moth defoliation using unmanned aerial systems
dc.creator.none.fl_str_mv Cardil Forradellas, Adrián
Vepakomma, Udayalakshmi
Brotons, Lluís
author Cardil Forradellas, Adrián
author_facet Cardil Forradellas, Adrián
Vepakomma, Udayalakshmi
Brotons, Lluís
author_role author
author2 Vepakomma, Udayalakshmi
Brotons, Lluís
author2_role author
author
dc.subject.none.fl_str_mv Pine processionary moth
Pest
Outbreak
Point clouds
Defoliation
Health monitoring
topic Pine processionary moth
Pest
Outbreak
Point clouds
Defoliation
Health monitoring
description Pine processionary moth (PPM) is one of the most destructive insect defoliators in the Mediterranean for many conifers, causing losses of growth, vitality and eventually the death of trees during outbreaks. There is a growing need for cost-effective monitoring of the temporal and spatial impacts of PPM in forest ecology to better assess outbreak spread patterns and provide guidance on the development of measures targeting the negative impacts of the species on forests, industry and human health. Remote sensing technology mounted on unmanned aerial systems (UASs) with high-resolution image processing has been proposed to assess insect outbreak impacts at local and forest stand levels. Here, we used UAS-acquired RGB imagery in two pine sites to quantify defoliation at the tree-level and to verify the accuracy of the estimates. Our results allowed the identification of healthy, infested and completely defoliated trees and suggested that pine defoliation estimates using UASs are robust and allow high-accuracy (79%) field-based infestation indexes to be derived that are comparable to those used by forest technicians. When compared to current field-based methods, our approach provides PPM impact assessments with an efficient data acquisition method in terms of time and staff, allowing the quantitative estimation of defoliation at tree-level scale. Furthermore, our method could be expanded to a number of situations and scaled up in combination with satellite remote sensing imagery or citizen science approaches.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/f8100402
http://hdl.handle.net/10459.1/69112
url https://doi.org/10.3390/f8100402
http://hdl.handle.net/10459.1/69112
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/f8100402
Forests, 2017, vol. 8, núm. 10, p. 402
dc.rights.none.fl_str_mv cc-by (c) Cardil Forradellas, Adrián et al., 2017
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Cardil Forradellas, Adrián et al., 2017
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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