Estimation of Frost Damage in Walnut Trees (Juglans regia) using Remote Sensing

Document Type : Research Article

Authors

E. Gohari ¹

H. Torabzadeh Khorasani ¹

S. Alvandy ²

M. Heidari Mozaffar ¹

¹ Department of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

² Department of Plant Protection, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

https://doi.org/10.22067/jhs.2025.91090.1398

Abstract

Introduction
Walnut is one of the most important economic crops in Iran and worldwide. Among the climatic factors that influence the selection of suitable sites for walnut (Juglans regia) cultivation, spring frost is particularly significant. Most of the walnut planting areas in Iran are mostly in the areas that have damaging colds in spring. The largest area under walnut cultivation in Iran is concentrated in the mountain hills and heights of the Zagros and Alborz mountain ranges. Every year, walnut production is limited by spring frosts, which, in addition to reducing production, can also damage sensitive plant tissues. Frost damage is one of the cases that are subject to compensation from the Agricultural Insurance Fund. Due to the high economic value of this product, an accurate, fast and low-cost estimation is necessary to evaluate the damages caused by this phenomenon on the walnut product. In this regard, in order to plan for the accurate calculation of the amount of compensation to the insurer, the insurer needs comprehensive information, so that it can estimate the performance losses caused by various factors. Remote sensing methods for frost monitoring using vegetation indicators are very efficient and effective. Therefore, this research examines spring frost damage by providing a quantitative index. 
 
Materials and Methods
In this research, frost damage of the Agricultural Insurance Fund was used for walnut trees. The damage assessment was presented over a period of nine years. Vegetation index values extracted from Landsat and Sentinel-2 images have been used to overcome adverse weather conditions. Each of the vegetation indicators has different noises such as light changes during the day, wind speed, temperature, sun angle, etc. In order to smooth the indices, Savitzky-Golay filter was used. This filter was calculated with window size 11 and degree 2. In this filter, choosing the window size and degree is very important, because the small window size and degree cause noise to remain in the data, and if the window size and degree are chosen large, they lead to the loss of information related to vegetation indices. Meanwhile, choosing the reference year for comparison with other years was of particular importance. According to the consistency of the filtered indicators and the damages reported by the Agricultural Insurance Fund, the reference year was determined. The Spring Frost Damage Index index was presented in each year using the filtered values of the relevant vegetation indices, between the reference year and other years. In general, the enclosed area of the two curves between the intersection points at the beginning and the end has indicated the values of the SFDI index in each year.
 
Results and Discussion
The relationship between Spring Frost Damage Index (SFDI) index and damages was done through 2nd degree polynomial model and linear regression. The status of the SFDI index compared to the estimates of the agricultural insurance fund for all villages was investigated through two polynomial models of the 2nd degree and linear regression. The accuracy of the regression model was evaluated with criteria such as: r, RMSE, P-value. The value of r indicates how well a model can predict the value of the dependent variable in percentage terms. The higher the value of r, the better the model. RMSE values indicate how well a regression model can predict the value of the variable. The quantity P-value shows the significance of two independent and dependent variables, and values less than 5% indicate the existence of a relationship between two variables at a significant level of 95%. It is useful to calculate all three quantities for a given model, as each measure provides useful information. Spring frost damage index based on Normalized Difference Vegetation Index vegetation index with correlation coefficients of r = 0.927 and r = 0.823 were able to estimate the damage. Finally, the maps of spatial distribution and spring frost damage showed a good fit with the damage reported by the Agricultural Insurance Fund.
 
Conclusion
The SFDI index has demonstrated a positive role in assessing frost damage. The spatial distribution results of the SFDI index further confirm its effectiveness, showing that as the index value increases, the percentage of frost damage reported by the Agricultural Insurance Fund also rises. Overall, the findings indicate that this index evaluates the impacts of spring frost accurately and efficiently and shows a strong correlation with reductions in crop yield.

Keywords

Satellite images

Spring frost damage

Time series

Vegatation index

Subjects

• Breeding and Biotechnology of Plant and Flower

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

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