Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models
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 sensi...
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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MDPI
2025
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| Online Access: | http://hdl.handle.net/20.500.12123/21240 https://www.mdpi.com/2072-4292/17/3/537 https://doi.org/10.3390/rs17030537 |
| _version_ | 1855038370827206656 |
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| author | Hirigoyen, Andrés Villacide, Jose Maria |
| author_browse | Hirigoyen, Andrés Villacide, Jose Maria |
| author_facet | Hirigoyen, Andrés Villacide, Jose Maria |
| author_sort | Hirigoyen, Andrés |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA21240 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA212402025-02-13T10:58:22Z Assessing Sirex noctilio Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models Hirigoyen, Andrés Villacide, Jose Maria Sirex Forest Pests Remote Sensing Pinus Damage Mathematical Models Plagas Forestales Teledetección Daños Modelos Matemáticos Sirex noctilio 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 2025-02-13T10:56:51Z 2025-02-13T10:56:51Z 2025-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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 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) application/pdf MDPI Remote Sensing 17 (3) : 537 (February 2025) |
| spellingShingle | Sirex Forest Pests Remote Sensing Pinus Damage Mathematical Models Plagas Forestales Teledetección Daños Modelos Matemáticos Sirex noctilio Hirigoyen, Andrés Villacide, Jose Maria 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 |
| 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_short | 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 |
| topic | Sirex Forest Pests Remote Sensing Pinus Damage Mathematical Models Plagas Forestales Teledetección Daños Modelos Matemáticos Sirex noctilio |
| url | http://hdl.handle.net/20.500.12123/21240 https://www.mdpi.com/2072-4292/17/3/537 https://doi.org/10.3390/rs17030537 |
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