Assessing <i>Sirex noctilio</i> 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: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/3/537 |
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| author | Andrés Hirigoyen José Villacide |
| author_facet | Andrés Hirigoyen José Villacide |
| author_sort | Andrés Hirigoyen |
| collection | DOAJ |
| 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 <i>Sirex noctilio</i> in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in <i>Neuquén</i>, 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 | Article |
| id | doaj-art-cbfdff1fd87c4a3db008927fafe14d7e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-cbfdff1fd87c4a3db008927fafe14d7e2025-08-20T02:12:33ZengMDPI AGRemote Sensing2072-42922025-02-0117353710.3390/rs17030537Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning ModelsAndrés Hirigoyen0José Villacide1Sistema Forestal, Instituto Nacional de Investigación Agropecuaria, INIA Las Brujas, Ruta 48 km 10, Rincón del Colorado, Canelones 90100, UruguayGrupo de Ecología de Poblaciones de Insectos, IFAB-INTA Bariloche, Modesta Victoria 4450, San Carlos de Bariloche 8400, Rio Negro, ArgentinaEarly 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 <i>Sirex noctilio</i> in pine plantations. A Random Forest (RF) model was applied to analyze Planetscope satellite images of Sirex-affected areas in <i>Neuquén</i>, 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.https://www.mdpi.com/2072-4292/17/3/537random forestplantation foresttree health assessmentlarge-scale pest surveillancedamage classification |
| spellingShingle | Andrés Hirigoyen José Villacide Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models Remote Sensing random forest plantation forest tree health assessment large-scale pest surveillance damage classification |
| title | Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models |
| title_full | Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models |
| title_fullStr | Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models |
| title_full_unstemmed | Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models |
| title_short | Assessing <i>Sirex noctilio</i> Fabricius (Hymenoptera: Siricidae) Damage in Pine Plantations Using Remote Sensing and Predictive Machine Learning Models |
| title_sort | assessing i sirex noctilio i fabricius hymenoptera siricidae damage in pine plantations using remote sensing and predictive machine learning models |
| topic | random forest plantation forest tree health assessment large-scale pest surveillance damage classification |
| url | https://www.mdpi.com/2072-4292/17/3/537 |
| work_keys_str_mv | AT andreshirigoyen assessingisirexnoctilioifabriciushymenopterasiricidaedamageinpineplantationsusingremotesensingandpredictivemachinelearningmodels AT josevillacide assessingisirexnoctilioifabriciushymenopterasiricidaedamageinpineplantationsusingremotesensingandpredictivemachinelearningmodels |