Document Type : Research Paper
Authors
1 Assistant Professor, Department of Geography, Payame Noor University, Tehran.
2 Faculty member; Department of Architecture and Urban Planning, Technical and Vocational University; Tehran, Iran.
3 Assistant Professor of Geography,Payame Noor University,
Abstract
Introduction
Drought is one of the environmental disasters that are very common in arid and semi-arid country regions. Rainfall defects have different effects on groundwater, soil moisture, and river flow. Meteorological drought indices are calculated directly from meteorological data such as rainfall and will not be useful in monitoring drought in the absence of data. Therefore, remote sensing techniques can be a useful tool in measuring drought. Drought is a known environmental disaster and has social, economic, and environmental impacts. Lack of rainfall in an area for long periods is known as drought. Drought and rainfall affect the water and agricultural resources of each region.
Materials & Methods
Due to the nature of the problem and the subject under study, the present study is descriptive-analytical with emphasis on quantitative methods. In this study, satellite images of Tera Sensor Modis in 2000 and 2017 were used to verify the existence of wet and drought phenomena. In the next step, by examining the rain gauge and synoptic data of the existing stations and using the standardized precipitation index model of three months (May, June, and April), the sample was selected. Next, we compared temperature status indices (TCI) and vegetation health indices (VHI) in these three months to determine the difference between these indices over the three months. Modira Terra satellite was used to study the vegetation status in the study area. Subsequently, vegetation-free areas were isolated from vegetation areas using the conditions set for the NDVI layer, the experimental method was used to determine the threshold value of this index. For this purpose, different thresholds were tested, with the optimum value of 1 being positive. NDVI is less than 1 free of positive plants and more than free of vegetation. MODIS spectral sensor images for surface temperature variables with a spatial resolution of 1 km, including 31 bands (1080/1180 bandwidth, central bandwidth / 11.017 spatial resolution of 1000 m) and 32 bands - 770/11Central Wavelength Band 032/12 Spatial Resolution Power (1000 m) Selected for months that are almost cloudless. All images are downloaded from the SearchEarthData site and edited. Total rainfall in June, April, and May for 20 years has been provided by the Meteorological Organization of Iran. ARC GIS software and geostatistical methods were used to process Excel data. Pearson correlation coefficient was also used to estimate the correlation between the data.
Results & Discussion
A standard precipitation index is a powerful tool in analyzing rainfall data. This study aimed to compare the relationship between remote sensing indices and meteorological drought indices and to determine the effectiveness of remote sensing indices in drought monitoring. The correlation between the variables with the SPI index was evaluated and calculated. The results of the indicators are different, so a criterion should be used to evaluate the performance of these indicators. SPI index on a quarterly time scale (correlation with vegetation) was selected as the preferred criterion. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. In the short run, this index has the highest correlation with thermal indices at the level of 1%. The correlation between meteorological drought index and plant water content and thermal indices increases with increasing time intervals. The positive correlation between vegetation indices and plant water content with meteorological drought indices shows that the trend of changes is in line. Therefore, the TCI index makes the drought more accurate and is a better method to estimate drought.
Conclusion
The results showed that among the surveyed fish, the most drought trend was observed in the eastern provinces and covers more than 50% of the region. The trend of changes in this slope was statistically significant. According to the results of correlations, the TCI index had a strong correlation with the SPI index with other indices. It can also be concluded that Modis images and processed indices along with climatic indices have the potential to monitor drought. The use of maps derived from drought indices can help improve drought management programs and play a significant role in reducing the effects of drought.
Keywords
Main Subjects
- امیدوار، کمال(1392)، مخاطرات طبیعی،انتشارات دانشگاه یزد، یزد.
- حجازیزاده، زهرا.، جویزاده، سعید(1389)، مقدمهای بر خشکسالی و شاخصهای آن، سمت، تهران.
- رضایی مقدم، محمد حسین، ولی زاده کامران، خلیل، رستم زاده، هاشم، رضایی، علی (1390)، ارزیابی کارایی دادههای سنجندهی MODIS در برآورد خشکسالی (مطالعهی موردی: حوضهی آبریز دریاچه ارومیه). جغرافیا و پایداری محیط،2(4):37-52.
- رضایی بنفشه، مجید، رضایی، علی، فریدپور، مجتبی (1394). تحلیل خشکسالی کشاورزی استان آذربایجانشرقی با تاکید بر سنجش از دور و شاخص وضعیت پوشش گیاهی، دانش آب و خاک، 25(1)، 113-123.
- زارع خورمیزی، هادی، غفاریان مالمیری، حمیدرضا (1396). پایش خشکسالی و تأثیر آن بر پوشش گیاهی با استفاده از فناوریهای سنجش از دور بررسی موردی: استان یزد، سالهای 1994 تا 2014، مدیریت بیابان، 5(10)، 68-86.
- خورانی، اسداله، جمالی، زهرا (1395). اثر تغییر اقلیم بر شدت و مدت خشکسالی در ایستگاههای خشک و نیمهخشک (بندرعباس و شهرکرد) تحت مدل HADCM3. نشریه علمی جغرافیا و برنامه ریزی، 20(57)، 115-131.
- جوادی، زهرا، فلاح قالهری، غلامعباس، انتظاری، علیرضا (1393). نقش پارامترهای آب هوایی بر عملکرد محصول بادام مطالعه موردی: سبزوار، پژوهشهای اقلیم شناسی، 1393(17)، 125-141.
- دلفیان فرح، یزدان پناه مسعود، فروزانی معصومه، یعقوبی جعفر(۱۳۹۶)، بررسی رفتارهای مدیریتی کشاورزان در هنگام خشکسالی به عنوان پاسخ های پیشگیرانه: مورد مطالعه شهرستان دهلران، تحلیل فضایی مخاطرات محیطی، ۴ (۴) :۷۹-۹۲.
- فتح نیا، امان اله، رجایی، سعید، برزو، فرزانه (1396). پیشبینی احتمال تکرار دورههای خشکسالی و اثر آن بر پوشش گیاهی در استان گلستان. نشریه علمی جغرافیا و برنامه ریزی، 21(60)، 179-196.
- فرزندی، محبوبه، رضائی پژند، حجت، ثنایی نژاد، سید حسین (1393). ترمیم و گسترش 127 سال آمار دمای ماهانه مشهد، پژوهشهای اقلیم شناسی، 1393(17)، 111-123.
- نوری، هدایتالله، نوروزی، اصغر (1395)، مبانی برنامهریزی محیطی برای توسعه پایدار روستایی، دانشگاه اصفهان.
- کاظم پور چورسی، سیما، عرفانیان، مهدی، عبادی نهاری، زهرا (1398)، ارزیابی دادههای ماهوارهای MODIS و TRMM در پایش خشکسالی حوضه آبریز دریاچه ارومیه، جغرافیا و برنامه ریزی محیطی، 30(2)، 17-34.
- موغلی، مرضیه (1399). پایش تغییرات پوشش گیاهی در اثر خشکسالی در حوضه آبریز درودزن با استفاده از تصاویر MODIS. جغرافیای طبیعی، 13(49)، 85-107.
- Bayarjargal, Y., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C., & Tucker, C. J (2006). A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sensing of Environment, 105(1): 9-22.
- Brown, I., Poggio, L., Gimona, A. et al (2011). Climate change, drought risk and land capability for agriculture: implications for land use in Scotland. Reg Environ Change 11, 503–518.
- Bhuiyan, C (2004). various droughts for monitoring drought condition in Aravalli terrain of India. In Proceedings of the XXth ISPRS Conference.Int. Soc. Photogramm. Remote Sensing, Istanbul.
- Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., & Sutera, A (2003). Spatial variability of drought: an analysis of the SPI in Sicily. Water resources management, 17(4): 273-296.
- Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Gregoire, J. M (2001). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77: Pp. 22–33.
- Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., Huang, Y., (2012). A comprehensive drought monitoring method integrating MODIS and TRMM data, International Journal of Applied Earth Observation and Geoformation, Vol 23, Pp 245-253.
- FAO (2013). Drought Facts-Food and Agriculture, fao.org.
- Gu, L., Hanson, P. J., Post, W. M., Kaiser, D. P., Yang, B., Nemani, R., ... & Meyers, T(2008).The 2007 eastern US spring freeze: increased cold damage in a warming world?. BioScience, 58(3): 253-262.
- Huete, A., (2004). Remote Sensing for Natural Resources Management and Enviromental Monitoring, Manual of remote sensing 3 ed., Vol 4, Univercity of Arizona.
- Liu, Q., Zhang, S., Zhang, H., Bai, Y., & Zhang, J. (2019). Monitoring drought using composite drought indices based on remote sensing. Science of The Total Environment, 134585.
- McKee, T. B.; Doesken, N. J.; and Kleist, J (1993). The relationship of drought frequency and duration11 to time scales. Preprints, 8th Conference on Applied Climatology, Anaheim.
- Rahimzadeh, F., Asgari, A., & Fattahi, E (2009). Variability of extreme temperature and precipitation in Iran during recent decades. International Journal of Climatology: A Journal of the Royal Meteorological Society, 29(3): 329-343.
- Orimoloye, Johanes A, Belle, Olusola O. Ololade (2021). Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation, Journal of Environmental Management Volume 284, 15 April 2021, 112028.
- Sheffield, J., Goteti, G., Wen, F.H., Wood, E.F., 2004. A simulated soil moisture based drought analysis for the United States. J. Geophys. Res. 109, D24108.
- Stow, D.A. & 23 others (2004), “Remote sensing of vegetation and land-cover change in arctic tundra ecosystems”, Remote Sensing of Environment, 89: 281-308.
- Tsakiris, G., & Vangelis, H (2004). Towards a drought watch system based on spatial SPI. Water resources management, 18(1): 1-12.
- Tian, W., Liu, X., Liu, C., & Bai, P. (2018). Investigation and simulations of changes in the relationship of precipitation-runoff in drought years. Journal of Hydrology, 565, 95–105.
- Tirivarombo, S., Osupile, D., & Eliasson, P. (2018). Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI). Physics and Chemistry of the Earth, Parts A/B/C. V 106, P 1-10.
- Wilhite, D.A., Sivakumar, M.V.K., Pulwarty, R., (2014). Managing drought risk in a changing climate: the role of national drought policy. Weather Clim. Extremes 3, 4–13.
- Zhao, H., Xu, Z., Zhao, J., & Huang, W. (2017). A drought rarity and evapotranspiration-based index as a suitable agricultural drought indicator. Ecological Indicators, 82, 530–538.
- Zhou, J., Li, Q., Wang, L., Lei, L., Huang, M., Xiang, J., ... & Zhu, G. (2019). Impact of climate change and land-use on the propagation from meteorological drought to hydrological drought in the eastern Qilian Mountains. Water, 11(8), 1602.
- Zhang, A., Jia, G., (2013). Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data, Remote Sensing of Environment, Vol 134, Pp 12-23.
- Zhang, P., Zhang, J., Chen, M., (2017). Economic impacts of climate change on agriculture: The importance of additional climatic variables other than temperature and precipitation, Journal of Environmental Economics and Management, Vol 83, Pp 8- 31.