Document Type : Research Paper
Authors
1
Ph.D. student of Agricultural Entomology, Department of Plant Protection, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
2
Associate Prof., Department of Plant Protection, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
3
Associate Prof., Department of Plant Protection, College of Agriculture, University of Kurdistan, Kurdistan, Iran
4
Associate Prof., Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Kurdistan, Iran
10.22092/ijfrpr.2023.363026.1599
Abstract
Background and objectives: Oak is a common and the most important species in Zagros forests of Iran. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification. Unfortunately, a significant part of those forests suffer from oak decline. Zagros forest covers almost 40% of Iran’s woodland which especially during the last two decades the population of oak trees (Quercus spp.) decreased mainly due to drought, diseases and insect pests. One of the most important pests in western oak forests is green oak leaf roller, Tortrix viridana L., (Lep.: Tortricidae) feed on oak leaves as the main host.
Methodology: This research aimed to assess and predict the degree of damage to the oak forests using remote sensing and GIS methods through regression analysis of the oak foliage remaining ratios of field plots with a vegetation indices of Sentinel-2 data during a year. Field monitoring carried on from April- 27-2020 to May-14-2020. For this purpose, in a two-kilometer-wide vector along the route from Sarovabad city to Bashmaq border, 100 sampling locations were randomly determined using GIS software. In each sampling location, four trees were selected in the four main geographical directions (in order to reduce the effect of directions on distribution of T. viridana population). Then, four branches (length about 100 cm) were randomly cut as a sampling unit, and the number of fifth instar larvae of T. viridana, were counted. The correlation between the number of T. viridana and elevation, slope, distance from the road, distance from the river, distance from residential areas, NDVI and solar radiation index were analyzed using Pearson's correlation test and multiple linear regression. Data normality was checked using the Kolmogorov-Smirnov statistical test. Data were analyzed using SPSS software (Version 26).
Results: The results of Pearson's correlation analysis between the those variables and T. viridana population show the highest and lowest correlation of the T. viridana population with elevation (r= 0.651) and slope (r= -0.015), respectively. According to multiple linear regression, elevation had the highest correlation coefficient with T. viridana population density. In addition, solar radiation index, NDVI and distance from the river were ranked with strong to weak correlation, respectively. Results showed that, the relationship between slope and vegetation density is also significant (P= 0.032; r= 0.214). According to the distribution intensity map of the T. viridana, which was obtained using the multiple linear regression equation, it has been shown that the most distribution of the T. viridana was observed in the southwest of Sarovabad city. Clearly NDVI was convenient for separating different levels of damage.
Conclusion: It can be concluded that the population of T. viridana is obviously high at high altitude and in high tree densities. The results showed that the population density of T. viridana is higher wherever a dense vegetation was occurred. This research provided a feasible and quantitative method in the spatiotemporal prediction of green leaf roller occurrence by remote sensing and GIS. In conclusion, with the availability of population models, it is possible to limit the spread of T. viridana through earlier detection of pest incidence.
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