Siberian Silk Moth Outbreak Dynamics in Dark-Coniferous Forests of the Altai

2018 ◽  
Vol 11 (1) ◽  
pp. 26-34 ◽  
Author(s):  
V. I. Kharuk ◽  
S. T. Im ◽  
M. N. Yagunov

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 115
Author(s):  
Svetlana M. Sultson ◽  
Andrey A. Goroshko ◽  
Sergey V. Verkhovets ◽  
Pavel V. Mikhaylov ◽  
Valery A. Ivanov ◽  
...  

This research is dedicated to solving an urgent problem associated with the large-scale destruction of taiga forests by Siberian silk moth (Dendrolimus sibiricus) outbreaks. The dynamics of the damage to dark coniferous forest stands induced by the Siberian silk moth outbreaks in mid-altitude mountains were studied. A hypothesis was formulated based on the fundamental influence of the orography on the phytophage’s dispersal within the landscape, along with the climate, which acts as a secondary predictor—a catalyst for outbreaks. The study was carried out using Landsat−8 satellite imagery time-series (from 2018 to 2020). The data were verified using a field forest pathological survey of the territory. An assessment of the defoliated forest area and damage association with the landscape was carried out using an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model. The assessment was aimed to detail the forecast parameters for an outbreak development in mid-altitude mountains using the orographic features—altitude, terrain slope, and slope aspect. Early warnings of phytophagous insect outbreaks in mountain southern taiga should be focused on the permanent monitoring of dark coniferous stands of the mossy group of forest types, covering altitude levels from 400 to 600 m, located on gentle terrains and slopes of up to 15 degrees. The greatest vulnerability to phytophage impacts was characterized as areas located at altitudes from 400 to 600 m. The upper limit of D. sibiricus distribution was 900 m above sea level. The results obtained provide comprehensive information on the Siberian silk moth potential reserves within the study area with the possibility of extrapolation to similar territories. The data will make it possible to model pest outbreaks based on orography and improve the forest pathological monitoring methods at the regional level.


2016 ◽  
Vol 9 (6) ◽  
pp. 711-720 ◽  
Author(s):  
V. I. Kharuk ◽  
D. A. Demidko ◽  
E. V. Fedotova ◽  
M. L. Dvinskaya ◽  
U. A. Budnik

2021 ◽  
Vol 839 (5) ◽  
pp. 052007
Author(s):  
S M Sultson ◽  
P V Mikhaylov ◽  
S V Verkhovets ◽  
A A Goroshko ◽  
N P Melnichenko

2018 ◽  
Vol 30 (1) ◽  
pp. 173-193 ◽  
Author(s):  
Yeongjun Cho ◽  
Hasong Kim ◽  
Hyeonho Myeong ◽  
Jungwon Park ◽  
Janggeun Oh

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