Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we atte...

ver descrição completa

Detalhes bibliográficos
Autores: Otsu, Kaori|||0000-0001-7835-0949, Pla, Magda|||0000-0002-7060-6783, Vayreda Duran, Jordi|||0000-0002-9538-7361, Brotons, Lluís|||0000-0002-4826-4457
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:201104
Acesso em linha:https://ddd.uab.cat/record/201104
https://dx.doi.org/urn:doi:10.3390/s18103278
Access Level:acceso abierto
Palavra-chave:Forest defoliation
Thaumetopoea pityocampa
Vegetation index
Unmanned aerial vehicle (UAV)
Change detection
id ES_3fc516609c9d33f79ee2d199fc791e01
oai_identifier_str oai:ddd.uab.cat:201104
network_acronym_str ES
network_name_str España
repository_id_str
spelling Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imageryOtsu, Kaori|||0000-0001-7835-0949Pla, Magda|||0000-0002-7060-6783Vayreda Duran, Jordi|||0000-0002-9538-7361Brotons, Lluís|||0000-0002-4826-4457Forest defoliationThaumetopoea pityocampaVegetation indexUnmanned aerial vehicle (UAV)Change detectionThe pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools. 22018-01-0120182018-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/201104https://dx.doi.org/urn:doi:10.3390/s18103278reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2011042026-06-06T12:50:31Z
dc.title.none.fl_str_mv Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
title Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
spellingShingle Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
Otsu, Kaori|||0000-0001-7835-0949
Forest defoliation
Thaumetopoea pityocampa
Vegetation index
Unmanned aerial vehicle (UAV)
Change detection
title_short Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
title_full Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
title_fullStr Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
title_full_unstemmed Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
title_sort Calibrating the severity of forest defoliation by pine processionary moth with landsat and UAV imagery
dc.creator.none.fl_str_mv Otsu, Kaori|||0000-0001-7835-0949
Pla, Magda|||0000-0002-7060-6783
Vayreda Duran, Jordi|||0000-0002-9538-7361
Brotons, Lluís|||0000-0002-4826-4457
author Otsu, Kaori|||0000-0001-7835-0949
author_facet Otsu, Kaori|||0000-0001-7835-0949
Pla, Magda|||0000-0002-7060-6783
Vayreda Duran, Jordi|||0000-0002-9538-7361
Brotons, Lluís|||0000-0002-4826-4457
author_role author
author2 Pla, Magda|||0000-0002-7060-6783
Vayreda Duran, Jordi|||0000-0002-9538-7361
Brotons, Lluís|||0000-0002-4826-4457
author2_role author
author
author
dc.subject.none.fl_str_mv Forest defoliation
Thaumetopoea pityocampa
Vegetation index
Unmanned aerial vehicle (UAV)
Change detection
topic Forest defoliation
Thaumetopoea pityocampa
Vegetation index
Unmanned aerial vehicle (UAV)
Change detection
description The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.
publishDate 2018
dc.date.none.fl_str_mv 2
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/201104
https://dx.doi.org/urn:doi:10.3390/s18103278
url https://ddd.uab.cat/record/201104
https://dx.doi.org/urn:doi:10.3390/s18103278
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406683015413760
score 15,301603

Registos relacionados