Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models

Autores
Hirigoyen, Andrés; Villacide, Jose Maria
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring.
EEA Bariloche
Fil: Hirigoyen, Andrés. Instituto Nacional de Investigación Agropecuaria (INIA) Las Brujas. Sistema Forestal; Uruguay
Fil: Villacide, Jose Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina
Fil: Villacide, Jose Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina
Fuente
Remote Sensing 17 (3) : 537 (February 2025)
Materia
Sirex
Forest Pests
Remote Sensing
Pinus
Damage
Mathematical Models
Plagas Forestales
Teledetección
Daños
Modelos Matemáticos
Sirex noctilio
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
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spelling Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning ModelsHirigoyen, AndrésVillacide, Jose MariaSirexForest PestsRemote SensingPinusDamageMathematical ModelsPlagas ForestalesTeledetecciónDañosModelos MatemáticosSirex noctilioEarly detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring.EEA BarilocheFil: Hirigoyen, Andrés. Instituto Nacional de Investigación Agropecuaria (INIA) Las Brujas. Sistema Forestal; UruguayFil: Villacide, Jose Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; ArgentinaFil: Villacide, Jose Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; ArgentinaMDPI2025-02-13T10:56:51Z2025-02-13T10:56:51Z2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21240https://www.mdpi.com/2072-4292/17/3/5372072-4292https://doi.org/10.3390/rs17030537Remote Sensing 17 (3) : 537 (February 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestalesinfo:eu-repograntAgreement/INTA/2023-PE-L03-I033, Gestión Sostenible de los sistemas forestales naturales y cultivados para el desarrollo de los territorios y la provisión de servicios ecosistémicos en Patagonia Andinainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:47:08Zoai:localhost:20.500.12123/21240instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:47:08.562INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
title Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
spellingShingle Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
Hirigoyen, Andrés
Sirex
Forest Pests
Remote Sensing
Pinus
Damage
Mathematical Models
Plagas Forestales
Teledetección
Daños
Modelos Matemáticos
Sirex noctilio
title_short Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
title_full Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
title_fullStr Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
title_full_unstemmed Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
title_sort Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
dc.creator.none.fl_str_mv Hirigoyen, Andrés
Villacide, Jose Maria
author Hirigoyen, Andrés
author_facet Hirigoyen, Andrés
Villacide, Jose Maria
author_role author
author2 Villacide, Jose Maria
author2_role author
dc.subject.none.fl_str_mv Sirex
Forest Pests
Remote Sensing
Pinus
Damage
Mathematical Models
Plagas Forestales
Teledetección
Daños
Modelos Matemáticos
Sirex noctilio
topic Sirex
Forest Pests
Remote Sensing
Pinus
Damage
Mathematical Models
Plagas Forestales
Teledetección
Daños
Modelos Matemáticos
Sirex noctilio
dc.description.none.fl_txt_mv Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring.
EEA Bariloche
Fil: Hirigoyen, Andrés. Instituto Nacional de Investigación Agropecuaria (INIA) Las Brujas. Sistema Forestal; Uruguay
Fil: Villacide, Jose Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina
Fil: Villacide, Jose Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB). Grupo de Ecología de Poblaciones de Insectos; Argentina
description Early detection and monitoring of invasive forest pests are crucial for effective pest management, particularly in preventing large-scale damage, reducing eradication costs, and improving overall control effectiveness. This study investigates the potential of machine learning models and remote sensing at various spatiotemporal scales to assess forest damage caused by the woodwasp Sirex noctilio in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in Neuquén, Argentina. The model’s results were validated through accuracy analysis and the Kappa method to ensure robustness. Our findings demonstrate that the RF model accurately classified Sirex damage levels, with classification accuracy improving progressively over time (overall accuracy of 87% for five severity categories and 98% for two severity categories). This allowed for a clearer distinction between healthy and Sirex-infested trees, as well as a more refined categorization of damage severity. This study highlights the potential of machine learning models to accurately assess tree health and quantify pest damage in plantation forests, offering valuable tools for large-scale pest monitoring.
publishDate 2025
dc.date.none.fl_str_mv 2025-02-13T10:56:51Z
2025-02-13T10:56:51Z
2025-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/21240
https://www.mdpi.com/2072-4292/17/3/537
2072-4292
https://doi.org/10.3390/rs17030537
url http://hdl.handle.net/20.500.12123/21240
https://www.mdpi.com/2072-4292/17/3/537
https://doi.org/10.3390/rs17030537
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestales
info:eu-repograntAgreement/INTA/2023-PE-L03-I033, Gestión Sostenible de los sistemas forestales naturales y cultivados para el desarrollo de los territorios y la provisión de servicios ecosistémicos en Patagonia Andina
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 17 (3) : 537 (February 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.558318

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