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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/21240
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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)2025-09-04T09:50:54Zoai: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:l2025-09-04 09:50:55.256INTA 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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