Agricultural Drought Assessment using Remote Sensing Data (Case study: Tuyserkan County)

Document Type : Research Article

Authors

• Maedeh Malmir ¹
• Kamran Shayesteh ¹
• Iman Pazhouhan ²

¹ Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

² Department of Natural Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

10.22067/agry.2024.85155.1173

Abstract

Introduction
 
In recent years, with the growing significance of drought and climate change, there is an increasing need for a well-structured plan to implement effective management strategies and monitor drought conditions. Considering the importance of investigating agricultural drought in relation to the yield of agricultural products and the fact that agriculture in Iran has always been affected by the amount and distribution of inappropriate rainfall, and climate change has caused problems in the cultivation conditions in the country by causing anomalies in temperature and precipitation; in recent years, due to the lack of suitable moisture conditions in the soil and the decrease of rainfall in the spring season, the amount of production and the quality of products have suffered serious threats, among these threats is the threat to human food security and, by nature, social and economic problems. Effective monitoring at the right moment can greatly reduce damage to agricultural production. The use of remote sensing and satellite imagery as effective tools for monitoring agricultural drought has gained significant attention from researchers. Remote sensing allows for the study of drought's effects on plant growth, leading to more accurate and impactful results in drought modeling.
 
Materials and Methods
 
Tuyserkan city covers an area of 1,556 square kilometers, 7.98% of the area of Hamedan province, in the west of Iran, and it is located along the Zagros mountain range. In this study, the goal is to evaluate the spatial and temporal patterns of agricultural drought in Tuyserkan County using vegetation coverage indicators derived from satellite data, including the Normalized Difference Vegetation Index (NDVI), the Vegetation Condition Index (VCI), the Plant Health Index (VHI), and the Thermal Condition Index (TCI), over a 20-year period and at seasonal and annual scales. The satellite data used in this study are from MODIS imagery. These images are a suitable tool for drought monitoring due to the power of proper spatial separation and providing bands with different wavelengths. After pre-processing these images using ENVI software, surface temperature, and rainfall data (used by interpolation method) are used as effective data in the drought process in the study area during this period.
 
Results and Discussion
 
The Vegetation Condition Index (VCI) has a significant correlation with different seasons, as well as with the Standardized Precipitation Index (SPI). Therefore, it can be stated with confidence that this index can be used to monitor temporal and spatial changes in agricultural droughts in the study area with acceptable accuracy. In fact, the months from the fourth to the sixth are the best time for the growth and development of plants because whatever the effect of precipitation, it will show itself during this period, and the highest correlation between the SPI and VCI for the fourth to the sixth months. However, in the VHI for the seventh to ninth months, the meaningful correlation could be because of the fact that the vegetation of Tuyserkan is mostly farmland (ending in October) and orchard (with a high amount of walnuts and almonds, which continue until September). Rahimzadeh et al. (2008) found the best correlation between the VCI and SPI for one to three months in the monitoring of droughts in Northwest Iran, and in this study, the correlation between the VCI and SPI for one to nine months was obtained. Generally, the VCI index provides better results for measuring precipitation, especially in areas that are climatically heterogeneous. As a result, the VCI was selected as the best index for monitoring agricultural droughts in the region.
 
Conclusion
Based on the calculations performed, the climate of the region matches the seasonality of the Vegetation Coverage Index (VCI) better. Generally, the results of the VCI and SPI indices largely confirm the results of the NDVI index. As a result, the VCI index was chosen as the best index for monitoring agricultural droughts in Tuyserkan County. Additionally, the results derived from the use of the vegetation index VCI indicate the state of droughts in 2008 and 2014 and the state of precipitation in 2007 and 2018 compared to the study period in the region.



 
 



 
 

Keywords

• Standardized Precipitation Index
• Thermal Condition Index
• Vegetation Condition Index

Main Subjects

• Climatic zoning and ecological zoning

---

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Asong, Z.E., Wheater, H.S., Bonsal, B., Razavi, S., & Kurkute S. (2018). Historical drought patterns over Canada and their teleconnections with large-scale climate signals. Hydrology and Earth System Sciences, 22(6), 145-159. https://doi.org/10.5194/hess
2. Dosarani, M., Wali, A., Sepehr, A., & Komaki, C. (2015). Drought survey on vegetation using MODIS meter in Razavi Khorasan. Scientific and Research Journal of Desert Ecosystem Engineering, 4(7), 1-8. (In Persian with English abstract)
3. Darvand, S., Khosravi, H., Eskandari Domain, H., & Eskandari Domain, H. (2021). Investigating the trend of changes in NDVI index obtained from Modis sensor images. Iran Watershed Association, Destruction and Restoration of Natural Lands, 1(2), 69-79. (In Persian with English abstract). https://doi.org/20.1001.1.27174425.1399.1.2.7.8
4. Funk, C., & Budd, E. (2009). PhenologicallyTuned MODIS NDVI-based production normally estimates for Zimbabwe, Remote Sensing of Environment. Journal Article, 113(1), 115-125. https://doi.org/10.1016/j.rse.2008.08.015
5. Farzaneh, S., Shah Hosseini, R., & Kordpour, A. )2021(. Studying the estimation of providing an efficient index based on the combination of satellite gravimetric data and ground station information for pre-drought in Iran. Scientific-Research Quarterly of Geographical Information,30(1), 117,12-1. (In Persian with English abstract). https://doi:org/10.22131/sepehr.2021.244447
6. Bahramlou, R., Old, A., Jolani, SH., Samavatian, M., Tabar, A., & Khodayvandi, M,. )2021(. Excerpt of basic statistics of 1400. Hamedan. Ba'ath Square, Meshki Alley, corner of Imran St., 4th floor, Tel: [phone] farsighted: 38234110. Agricultural Jihad Organization of Hamedan Province - Vice President of Planning and Economic Affairs - Department of Statistics, Information Technology and Network Equipment. Doi: www.hm.agri-jahad.ir. (In Persian with English abstract)
7. Gouveia, C., Trigo, R.M., & Dacamra, C. )2009(. Drought and vegetation stress monitoring in Portugal sing satellite data. Journal of Natural Hazards Earth System Sciences,9(1), 185.195. https://doi:org/10.5194/nhess
8. Gidey, E., Dikinya, O., Sebego, R., Segosebe, E., & Zenebe, A. )2018(. Using drought indices to model the statistical relationships between meteorological and agricultural drought in raya and its environs, Northern Ethiopia. Journal of Earth Systems and Environment ,2(1)، 1-15. https://org/10.1007/s41748-018-0055-9
9. Hamzeh, S., Farahani, Z., Mahdavi, S., Chater Abgun, A., & Gholamnia, M. (2017). Temporal and spatial monitoring using remote sensing data in the Central province of Iran. Journal of Spatial Analysis of Environmental Hazards,4 (3), 53-70. (In Persian with English abstract). https://doi: khu.ac.ir on 2023-10-29
10. He, Y., Chen, F., Jia, H., Wang, L., & Bondur, V.G. (2020). Different drought legacies of rain-fed and irrigated croplands in a typical Russian agricultural Region. Remote Sensing,12(11), 1-23. https://org/10.3390/rs12111700
11. Hashemi, A., Yazdan Panah, H., & Momeni, M. (2021). Estimating the main stages of orange tree phenology using remote sensing of a sample of gardens in the southeast of Fars province. Geography and Environmental Planning,32(2), 119-134. (In Persian with English abstract). https://doi.org/10.22108/GEP.2021.125910.1373
12. Jinha, J., Murillo, M., Anjin, C., Mahendra, B., Akash, A., & Juan, L.B. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology,70(1), 15–22. https://doi.org/10.1016/j.copbio.2020.09.003
13. Kogan, F.N., Stark, R., Gitelson, A., Jargalsaikhan, L., Dugrajav, C., & Tsooj, S. (2005). Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. International Journal of Remote Sensing, 25(14), 2889–2896. https://doi.org/10.1080/01431160410001697619

• Karimi, M., & Shahidi, K. (2017). Survey of meteorological, hydrological and agricultural drought using drought indicators in Qarasu watershed. Remote Sensing and Geographic Information System in Natural Resources,9(2), 1-16. (In Persian with English abstract). dio:girs.iaubushehr.ac.ir

14. Karimi, M., & Shahidi, K. (2018). Survey of meteorological, hydrological and agricultural drought using drought indicators in Qarasu watershed. Remote Sensing and Geographic Information System in Natural Resources,10(2), 1-19. (In Persian with English abstract). dio:girs.iaubushehr.ac.ir
15. Lucas, V., Oldoni, I., Del Arco, S., Michelle Cristina, A,. Picoli, R,. & José, G.F. (2020). LEM + dataset: For agricultural remote sensing applications. Earth Observation and Geoinformatics Division, National Institute for Space Research, São Josédos Campos, Brazil,33(1), 1-7. https://doi.org/10.1016/j.dib.2020.106553
16. Mather, P.M., & Koch, M. (2010). Computer processing of remotely-sensed images an introduction. John Wiley and Sons,12(1), 29-66. https://org/10.1002/9780470666517.ch2
17. Mirmousavi, H., & Karimi,M. (2013). Studying the effect of drought on vegetation using MODIS sensor images in Kurdistan province. Geography and Development,31(11), 57-76. (In Persian with English abstract). https://doi.org/sid.ir/paper/77245/fa
18. Ministry of Communications and Information Technology, Iran Space Agency, Remote Sensing. )2019(. (In Persian with English abstract). https://doi:rs.isa.ir
19. Masoumeh, T. )2021(. Time series evaluation of the health index of agricultural products using remote sensing data in Cheram city, master's thesis in the field of natural resources science and engineering - land evaluation and preparation. Khatam Al Anbia Behbehan University of Technology. 45. (In Persian with English abstract)
20. Rahimzadeh, P., Darvishsefat, A. Khalili, A, & Makhdom, M. )2008(. Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments,72(1), 1086-1096. (In Persian with English abstract). https://doi.org/10.1016/j.jaridenv.2007.12.004
21. Soleimani, K., Darvishi, S., & Shokrian, F. )2019(. Analysis of agricultural drought using remote sensing indicators in Marivan city. Remote Sensing and Geographic Information System in Natural Resources,10(2), 1-19. (In Persian with English abstract). doi:girs.iaubushehr.ac.ir
22. Schwarz, M., Landmann, T., Cornish, N., Wetzel, K. F., Siebert, S., & Franke, J. )2020(. A spatially transferable drought hazard and drought risk modeling approach based on remote sensing data. Remote Sensing, 12(2), 237-245. https://doi.org/10.3390/rs12020237
23. Sandeep, P., Obi Reddy, G.P., Jegankumar, R., & Arun Kumar, K.C. )2021(. Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets. Ecological Indicators,121(1), 1-16. https://doi.org/10.1016/j.ecolind.2020.107033
24. Thenkabail, P. S., Gamage, M.S.D.N., & Smakhtin, V.U. )2004(. The use of note sensing data for drought assessment and monitoring in southwest Asia. IWMI Research Report 85,25,1-35. https://doi.org/10.3910/2009.086
25. Thomás, F.R., Frederico,T.D. & Jéssica, R.G. )2015(.Preliminary analysis of drought in 2012 in semi-arid of alagoas using indices of vegetation through sensor modis. Journal of Hyperspectral Remote Sensing, 5(1), 1-12. dio:10.29150/jhrs.v5.1.p001-012
26. Veisi, V., Qavam, M., & Bazarafshan, M. )2018(. The effects of drought on changes in pasture cover with an emphasis on telemetry indicators in the Salfchagan Nizar watershed. Scientific Journal of Pasture and Desert Research in Iran,62(3), 1-12. (In Persian with English abstract). https://doi.org/10.22092/ijrdr.2019.120018
27. Vijith, H., & Dodge-Wan, D. )2020(. Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo. Remote Sensing Applications: Society and Environment,)35(, 100311. https://doi.org/10.1016/j.rsase.2020.100311.
28. West, H., Quinn, N., & Horswell, M. )2019(. Remote sensing for drought monitoring and impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment,)232(, 111291. https://org/10.1016/j.rse.2019
29. Yuhas, A.N., & Schuderi, L.A. )2009(. MODIS derived NDVI characterization of drought induced evergreen die off in western North America. Journal of Geographical Research,47(1), 1-94. https://doi.org/10.1111/j.1745-871.2008.0055