برآورد میزان خسارت ناشی از سرمازدگی در درختان گردو (Juglans regia) با استفاده از سنجش‌ازدور

نوع مقاله : مقالات پژوهشی

نویسندگان

الهام گوهری ¹

حسین ترابزاده خراسانی ¹

سعید الوندی ²

مرتضی حیدری مظفر ¹

¹ گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بوعلی سینا، همدان، ایران

² گروه گیاه‌پزشکی، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران

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

چکیده

خسارت‌ سرمازدگی بهاره، از جمله مواردی است که مشمول پرداخت غرامت از سوی صندوق بیمه کشاورزی هستند. سرما‌زدگی ناگهانی از اثرات منفی در میزان باز‌دهی محصولات در باغ­ها است. به‌‌دلیل ارزش اقتصادی نسبتاً بالای محصولات باغی، باید از روش‌های دقیقی در برآورد میزان خسارت استفاده کرد. برخلاف محصولات زراعی که در سطح وسیع و به‌صورت یکنواخت کشت می‌شوند، تعمیم روش‌های نمونه‌برداری در این مطالعه به ‌کل منطقه بسیار دشوار بوده و موجب کاهش دقت مدل‌‌های برآورد خسارت می‌‌گردد. هدف اصلی این تحقیق، ارائه یک شاخص کمّی برای تخمین میزان خسارت وارده به درختان گردو (Juglans regia) بر مبنای سری زمانی تصاویر ماهواره­های Landsat و Sentinel-2 است؛ لذا براساس سری زمانی شاخص پوشش گیاهی تفاضلی نرمال‌شده، شاخص خسارت سرمازدگی بهاره برای باغ‌های گردوی شهرستان تویسرکان محاسبه و در یک دوره زمانی نُه‌ساله (2013 تا 2021) مورد ارزیابی قرار گرفت. سری‌های زمانی شاخص پوشش گیاهی تفاضلی نرمال‌شده مورد استفاده توسط فیلتر ساویتزکی- گلای هموار شده‌اند تا اثرات عواملی مانند ابر، گردوغبار و نویز کاهش یابند. با مقایسه سری ‌زمانی هر سال با سال‌ مرجع و محاسبه سطح بین دو منحنی، شاخص خسارت سرمازدگی بهاره محاسبه شد که بیانگر میزان خسارت‌های وارده به درختان گردو است. ارزیابی عملکرد شاخص خسارت سرمازدگی بهاره نسبت به برآوردهای صندوق بیمه ‌کشاورزی نشان می‌دهد که این شاخص برای همه روستاها از طریق دو مدل چندجمله‌ای درجه 2 و رگرسیون خطی با ضریب‌های همبستگی به‌ترتیب 927/0=r  و 823/0 = r قادر به تخمین خسارت است. در این ‌بین، نقشه‌های توزیع مکانی و خسارت سرمازدگی بهاره، تناسب خوبی با خسارت‌های گزارش‌شده از طرف صندوق بیمه کشاورزی دارند. در نهایت، روش پیشنهادی می‌تواند جایگزین مقرون‌به‌صرفه­ای جهت برداشت داده میدانی خسارت برای درختان گردو با استفاده از تصاویر ماهواره­ای باشد.

کلیدواژه‌ها

تصاویر ماهواره‌ای

خسارت سرمازدگی بهاره

سری زمانی

شاخص پوشش گیاهی

موضوعات

• بیوتکنولوژی و اصلاح درختان و گل های زینتی

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عنوان مقاله English

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

نویسندگان English

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

چکیده English

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.

کلیدواژه‌ها English

Satellite images

Spring frost damage

Time series

Vegatation index

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

مراجع

1. Allevato, E., Saulino, L., Cesarano, G., Chirico, G.B., D'Urso, G., Bolognesi, S.F., Rita, A., Rossi, S., Saracino, A., & Bonanomi, G. (2019). Canopy damage by spring frost in European beech along the Apennines: effect of latitude, altitude and aspect. Remote Sensing of Environment, 225, 431-440. https://doi.org/10.1016/j.rse.2019.03.023
2. Arghan, A., Khajehhoseini, N., Meshkinfar, A., & Moafi, N. (2016). Evaluation of the rural economic potential in city development (case study: Tuyserkan). Turkish Online Journal of Design Art and Communication, 6, 1102-1108.
3. Aslamarz, A.A., Vahdati, K., Rahemi, M., Hassani, D., & Leslie, C. (2010). Supercooling and cold-hardiness of acclimated and deacclimated buds and stems of Persian walnut cultivars and selections. HortScience, 45(11), 1662-1667. https://doi.org/10.21273/HORTSCI.45.11.1662
4. Azari Arani, E. (2012). Assessment of the damage caused to the rice crop due to frost using remote sensing and GIS (Mazandaran province). M.Sc. Thesis, Khajeh Nasiruddin Tousi University, Tehran University, Tehran, Iran. 1-123. (in Persian).
5. Cao, R., Chen, J., Shen, M., & Tang, Y. (2015). An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agricultural and Forest Meteorology, 200, 9-20. https://doi.org/10.1016/j.agrformet.2014.09.009
6. Chen, X., Vierling, L., Rowell, E., & DeFelice, T. (2004). Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment, 91(1), 14-26. https://doi.org/10.1016/j.rse.2003.11.003
7. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., & Colombo, R. (2014). Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing,6(7), 6549-6565. https://doi.org/10.3390/rs6076549
8. Cogato, A., Meggio, F., Collins, C., & Marinello, F. (2020). Medium-resolution multispectral data from Sentinel-2 to assess the damage and the recovery time of late frost on vineyards. Remote Sensing, 1896(11), 12. https://doi.org/10.3390/rs12111896
9. Diao, C. (2020). Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages. Remote Sensing of Environment, 248, 111960. https://doi.org/10.1016/j.rse.2020.111960
10. Díaz, M. L., Soresi, D. S., Basualdo, J., Cuppari, S. J., & Carrera, A. (2019). Transcriptomic response of durum wheat to cold stress at reproductive stage. Molecular biology reports, 46(2), 2427-2445. https://doi.org/10.1007/s11033-019-04704-y
11. Duddu, H.S., Pajic, V., Noble, S.D., Tanino, K.K., & Shirtliffe, S.J. (2018). Image-based rapid estimation of frost damage in canola (Brassica napus). Canadian Journal of Remote Sensing, 44(2), 169-175. https://doi.org/10.1080/07038992.2018.1462660
12. Fallah, M., Rasouli, M., Hassani, D., Lawson, S.S., Sarikhani, S., & Vahdati, K. (2022). Tracing superior late-leafing genotypes of persian walnut for managing late-spring frost in walnut orchards. Horticulturae, 8(11), 1003. https://doi.org/10.3390/horticulturae8111003
13. Gamon, J.A., Kovalchuck, O., Wong, C.Y.S., Harris, A., & Garrity, S.R. (2015). Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors. Biogeosciences, 12(13), 4149-4159. https://doi.org/10.5194/bg-12-4149-2015
14. Grömping, U. (2015). Variable importance in regression models. Wiley Interdisciplinary Reviews: Computational Statistics, 7(2), 137-152. https://doi.org/10.1002/wics.1346
15. Hassani, D., Sarikhani, S., Dastjerdi, R., Mahmoudi, R., Soleimani, A., & Vahdati, K. (2020). Situation and recent trends on cultivation and breeding of Persian walnut in Iran. Scientia Horticulturae, 270, 109369. https://doi.org/10.1016/j.scienta.2020.109369
16. Ji, L., Zhang, L., Rover, J., Wylie, B.K., & Chen, X. (2014). Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 20-27. https://doi.org/10.1016/j.isprsjprs.2014.06.013
17. Khadivi, A., Montazeran, A., & Yadegari, P. (2019). Superior spring frost resistant walnut (Juglans regia) genotypes identified among mature seedling origin trees. Scientia Horticulturae, 253, 147-153. https://doi.org/10.1016/j.scienta.2019.04.041
18. Khazaei, S., Jenabi, E., Zobdeh, Z., Biglari, M., Afshari, M., Gholamaliee, B., & Bashirian, S. (2019). Pattern of trauma related to falling from walnut tree as an occupational injury: A cross-sectional study in the west of Iran. Archives of Trauma Research, 8(2), 99-103.
19. Khoramshahi, L. (2012). Tiofer's effect on developing spring cold resistance in walnut trees. M.Sc. Thesis, Faculty of Agriculture, Boali Sina University, Hamedan, Iran. p. 1-98. (in Persian).
20. Li, W., Huang, J., Yang, L., Chen, Y., Fang, Y., Jin, H., Sun, H., & Huang, R. (2021). A practical remote sensing monitoring framework for late frost damage in wine grapes using multi-source satellite data. Remote Sensing,13(16), 3231. https://doi.org/10.3390/rs13163231
21. Maji, S., Chakraborty, P., Bhowmick, M., Dutta, S.K., Nath, R., & Chakraborty, P.K. (2015). Diurnal variation in spectral properties of potato under different dates of planting and N-doses. Environment & Ecology, 33(1), 478-483.
22. Murphy, M.E., Boruff, B., Callow, J.N., & Flower, K.C. (2020). Detecting frost stress in wheat: A controlled environment hyperspectral study on wheat plant components and implications for multispectral field sensing. Remote Sensing, 12(3), 477. https://doi.org/10.3390/rs12030477
23. Nejadsadeghi, L., Maali-Amiri, R., Zeinali, H., Ramezanpour, S., & Sadeghzade, B. (2014). Comparative analysis of physio-biochemical responses to cold stress in tetraploid and hexaploid wheat. Cell Biochemistry and Biophysics, 70(1), 399-408. https://doi.org/article/10.1007/s12013-014-9925-4
24. Rasolzadegan, Y. (1996). Fruit Growing in Temperate Regions. Publications of Isfahan University of Technology, Isfahan, Iran.  p. 965-978. (in Persian).
25. Sadogh, S.(2014). Geomorphological limitations and physical growth of Tuysarkan city using geographic information systems (GIS) and digital elevation model (DEM). 121-142. (in Persian).
26. Savitzky, A., & Golay, M.J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639. https://doi.org/10.1021/ac60214a047
27. She, B., Huang, J.F., Guo, R.F., Wang, H.B., & Wang, J. (2015). Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data. Journal of Zhejiang University-SCIENCE B, 16(2), 131. https://doi.org/10.1631/jzus.B1400150
28. Skendžić, S., Zovko, M., Lešić, V., Pajač Živković, I., & Lemić, D. (2023). Detection and evaluation of environmental stress in winter wheat using remote and proximal sensing methods and vegetation indices—A review. Diversity, 15(4), 481. https://doi.org/10.3390/d15040481
29. Ulsig, L., Nichol, C.J., Huemmrich, K.F., Landis, D.R., Middleton, E.M., Lyapustin, A.I., Mammarella, I., Levula, J., & Porcar-Castell, A. (2017). Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series. Remote Sensing, 9(1), 49. https://doi.org/10.3390/rs9010049
30. Vahdati, K., Massah Bavani, A.R., Khosh-Khui, M., Fakour, P., & Sarikhani, S. (2019). Applying the AOGCM-AR5 models to the assessments of land suitability for walnut cultivation in response to climate change: A case study of Iran. PLoS One, 14(6), e0218725. https://doi.org/10.1371/journal.pone.0218725
31. Wang, S., Chen, J., Rao, Y., Liu, L., Wang, W., & Dong, Q. (2020). Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 221-235. https://doi.org/10.1016/j.isprsjprs.2020.08.014
32. Wang, H.F., Huo, Z.G., Zhou, G.S., Liao, Q.H., Feng, H.K., & Wu, L. (2016). Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis. Plant Physiology and Biochemistry, 98, 39-45. https://doi.org/10.1016/j.plaphy.2015.10.032
33. Wang, P., Tang, J., Ma, Y., Wu, D., Yang, J., Jin, Z., & Huo, Z. (2021). Mapping threats of spring frost damage to tea plants using satellite-based minimum temperature estimation in China. Remote Sensing, 2713(14), 13. https://doi.org/10.3390/rs13142713
34. Zhu, X., & Liu, D. (2015). Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 222-231. https://doi.org/10.1016/j.isprsjprs.2014.08.014

Zhu, Y., Yang, G., Yang, H., Zhao, F., Han, S., Chen, R., Zhang, C., Yang, X., Liu, M., Cheng, J., & Zhao, C. (2021). Estimation of apple flowering frost loss for fruit yield based on gridded meteorological and remote sensing data in Luochuan, Shaanxi province, China. Remote Sensing, 13(9), 1630. https://doi.org/10.3390/rs13091630