Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

Detalhes bibliográficos
Autor(a) principal: Guerra-Hernández, Juan
Data de Publicação: 2021
Outros Autores: Diaz-Varela, Ramón A., Alvarez-González, Juan Gabriel, Rodríguez-González, Patrícia Maria
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/22175
Resumo: Research
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spelling Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forestsAlderRPASmulti-spectraldefoliationtexture variables3D point cloudtree health monitoringResearchBackground: Black alder (Alnus glutinosa) forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex (class Oomycetes), “alder Phytopththora”. Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity. Data obtained by unmanned aerial vehicles (UAVs) may be particularly useful for such tasks due to the high resolution, flexibility of acquisition and cost efficiency of this type of data. In this study, A. glutinosa decline was assessed by considering four categories of tree health status in the field: asymptomatic, dead and defoliation above and below a 50% threshold. A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles -red green blue (RGB) data were analysed using classical random forest (RF) and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony. A total of 34 remote sensing variables were considered, including a set of vegetation indices, texture features from the normalized difference vegetation index (NDVI) and a digital surface model (DSM), topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level. Results: The four categories identified by the RF yielded an overall accuracy of 67%, while aggregation of the legend to three classes (asymptomatic, defoliated, dead) and to two classes (alive, dead) improved the overall accuracy to 72% and 91% respectively. On the other hand, the confusion matrix, computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%, 80% and 94% for four-, three- and two-level classifications, respectively. Discussion: The study findings provide forest managers with an alternative robust classification method for the rapid, effective assessment of areas affected and non-affected by the disease, thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forestsSpringer OpenRepositório da Universidade de LisboaGuerra-Hernández, JuanDiaz-Varela, Ramón A.Alvarez-González, Juan GabrielRodríguez-González, Patrícia Maria2021-10-08T13:10:21Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/22175engGuerra-Hernández et al. Forest Ecosystems (2021) 8:61https://doi.org/10.1186/s40663-021-00342-8info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:51:44Zoai:www.repository.utl.pt:10400.5/22175Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:06:39.717005Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
title Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
spellingShingle Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
Guerra-Hernández, Juan
Alder
RPAS
multi-spectral
defoliation
texture variables
3D point cloud
tree health monitoring
title_short Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
title_full Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
title_fullStr Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
title_full_unstemmed Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
title_sort Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
author Guerra-Hernández, Juan
author_facet Guerra-Hernández, Juan
Diaz-Varela, Ramón A.
Alvarez-González, Juan Gabriel
Rodríguez-González, Patrícia Maria
author_role author
author2 Diaz-Varela, Ramón A.
Alvarez-González, Juan Gabriel
Rodríguez-González, Patrícia Maria
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Guerra-Hernández, Juan
Diaz-Varela, Ramón A.
Alvarez-González, Juan Gabriel
Rodríguez-González, Patrícia Maria
dc.subject.por.fl_str_mv Alder
RPAS
multi-spectral
defoliation
texture variables
3D point cloud
tree health monitoring
topic Alder
RPAS
multi-spectral
defoliation
texture variables
3D point cloud
tree health monitoring
description Research
publishDate 2021
dc.date.none.fl_str_mv 2021-10-08T13:10:21Z
2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/22175
url http://hdl.handle.net/10400.5/22175
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Guerra-Hernández et al. Forest Ecosystems (2021) 8:61
https://doi.org/10.1186/s40663-021-00342-8
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Open
publisher.none.fl_str_mv Springer Open
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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