Тип публикации: статья из журнала

Год издания: 2025

Идентификатор DOI: 10.3390/f16010160

Аннотация: <jats:p>In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affeПоказать полностьюcted forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important.</jats:p>

Журнал: Forests

Выпуск журнала: Т. 16, № 1

Номера страниц: 160

ISSN журнала: 19994907

• Goroshko Andrey A. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Sultson Svetlana M. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Ponomarev Evgenii I. (Federal Research Center “Krasnoyarsk Science Center, Siberian Branch, Russian Academy of Sciences”, 660036 Krasnoyarsk, Russia)
• Demidko Denis A. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Slinkina Olga A. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Mikhaylov Pavel V. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Tatarintsev Andrey I. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Kulakova Nadezhda N. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Khizhniak Natalia P. (Scientific Laboratory of Forest Health, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

• Ядро РИНЦ (eLIBRARY.RU)