Analysis of the relationship between vegetation and precipitation in Iran

Document Type : Original Article

Authors

1 phd of climatology at Sistan and Baluchestan university

2 phd student of climatology at Mohaghegh Ardabili university

Abstract

Awareness of vegetation and its health represents the climate of each place. Access to field data is usually difficult and limited to study and monitor vegetation on a global and regional scale. Also, estimating vegetation in the usual way, which includes an overall estimate of vegetation, is both time-consuming and does not provide very accurate information. Therefore, remote sensing is a very useful method by providing a wide view of the area, which is superior to other methods. In this study, the 16-day data of the Normal Vegetation Differential Index (NDVI) of MODIS Aqua Iran were downloaded from the NASA website between 3/20/2003 to 3/20/2019. Then, based on nearly 15 billion pixels, the long-term and annual average of NDVI Iran was calculated. Considering that NDVI values more than 0.2 indicate vegetation, the average annual vegetation of Iran was calculated. Finally, the relationship between vegetation and rainfall in Iran was investigated. The results showed that in 1387, due to water shortage and meteorological drought conditions, Iran's vegetation reached its lowest value, while in 1397, due to heavy rainfall and wet conditions, Iran's vegetation reached the highest amount in the period The study has arrived. The results also showed that in the last two decades, due to increased rainfall, the range of vegetation in Iran has increased.

Keywords


  1. علوی­پناه، سید کاظم (1385) کاربرد سنجش ­از دور در علوم زمین، انتشارات دانشگاه تهران، چاپ دوم، سال 1385.

    1. 2. علیجانی، بهلول (1389) آب و هوای ایران، انتشارات پیام­نور، تهران، چاپ دهم، سال 1389.
    2. 3. فاتحی­مرج، احمد؛ باقری­نیا، مژگان(1390) بررسی خشکسالی مرتعی غرب ایران با استفاده از تصاویر ماهواره­ای MODIS در سال­های 1386 تا 1389، علوم و مهندسى آبخیزدارى ایران، شماره16، صص22-13.
    3. مسعودیان، سید ابوالفضل(1390) آب و هوای ایران، مشهد، انتشارات شریعه توس، سال 1390.
    4. منتظری، مجید؛ کفایت مطلق امیدرضا(1396). فصل­بندی پوشش زمین در ایران به کمک نمایه NDVI. فصل‌نامه تحقیقات جغرافیایی، 32(4)، 138-147.
    5. منتظری، مجید؛ کفایت مطلق امیدرضا(1397). واکاوی میانگین بلندمدت پوشش گیاهی ایران به کمک نمایه NDVI. جغرافیا و برنامه ریزی محیطی، 71(3)، 1-14.
    6. میرموسوی، سیدحسین؛ کریمی، حمیده(1392) مطالعه­ی اثر خشکسالی بر روی پوشش­گیاهی با استفاده از تصاویر سنجنده­ی MODIS مورد: استان کردستان، جغرافیا و توسعه، شماره 31، صص 76-57.
    7. هادیان، فاطمه؛ سید زین­الدین حسینی؛ منصوره سیدحسنی (1393)، پاییش تغییرات پوشش گیاهی با استفاده از اطلاعات بارندگی و تصاویر ماهواره­ای N0AA AVHRR در استان کرمانشاه، نشریه مرتع­داری، شماره 1، صص 62-46.
    8. Albarakat, R., and Lakshmi, V. (2019). Comparison of normalized difference vegetation index derived from Landsat, MODIS, and AVHRR for the Mesopotamian marshes between 2002 and 2018. Remote sensing, 11(10), 1245.
    9. Chen, M., Parton, W.J., Hartman, M.D., Del Grosso, S.J., Smith, W.K., Knapp, A. K., ... & Gao, W. (2019). Assessing precipitation, evapotranspiration, and NDVI as controls of US Great Plains plant production. Ecosphere, 10(10), e02889.
    10. Chen, T., Niu, Q., Wang, Y., Zhang, L. P., & Du, B. (2011). Percentage of vegetation cover change monitoring in Wuhan region based on remote sensing. Procedia Environmental Sciences, 10, 1466-1472.

    12.Cheng-lin, L., & Jian-jun, W. (2008, July). Crop drought monitoring using MODIS NDDI over mid-territory of China. In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium (Vol. 3, pp. III-883). IEEE.

    1. Chuai, X. W., Huang, X. J., Wang, W. J., & Bao, G. (2013). NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. International journal of climatology, 33(7), 1696-1706.
    2. Didan, K., Munoz, A. B., Solano, R., & Huete, A. (2015). MODIS vegetation index user’s guide (MOD13 series). University of Arizona: Vegetation Index and Phenology Lab.
    3. Ding, M., Zhang, Y., Liu, L., Zhang, W., Wang, Z., & Bai, W. (2007). The relationship between NDVI and precipitation on the Tibetan Plateau. Journal of Geographical Sciences, 17(3), 259-268.
    4. Gillespie, T. W., Ostermann-Kelm, S., Dong, C., Willis, K. S., Okin, G. S., & MacDonald, G. M. (2018). Monitoring changes of NDVI in protected areas of southern California. Ecological Indicators, 88, 485-494.
    5. Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of remote sensing, 19(8), 1533-1543.
    6. https://search.earthdata.nasa.gov/search
    7. Huete, A. R., Liu, H. Q., Batchily, K. V., & Van Leeuwen, W. J. D. A. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote sensing of environment, 59(3), 440-451.
    8. Numata, I., Roberts, D. A., Sawada, Y., Chadwick, O. A., Schimel, J.P., & Soares, J.V. (2007). Regional characterization of pasture changes through time and space in Rondonia, Brazil. Earth Interactions, 11(14), 1-25.
    9. Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote sensing of environment, 48(2), 119-126.
    10. Ren, Y., Liu, J., Liu, S., Wang, Z., Liu, T., & Shalamzari, M. J. (2022). Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019. Remote Sensing, 14(3), 687.
    11. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote sensing of environment, 55(2), 95-107.
    12. Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y. A., Mushore, T. D., & Gupta, A. (2020). Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sensing, 12(15), 2433.
    13. Schultz, P. A., & Halpert, M. S. (1993). Global correlation of temperature, NDVI and precipitation. Advances in Space Research, 13(5), 277-280.
    14. Song, C. (2005). Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?. Remote sensing of environment, 95(2), 248-263.
    15. Wang, J., Rich, P.M., & Price, K.P. (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International journal of remote sensing, 24(11), 2345-2364.
    16. Wang, X., & Xie, H. (2009). New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua. Journal of Hydrology, 371(1-4), 192-200.
    17. Wang, Z. P., Zhang, X. Z., He, Y.T., Li, M., Shi, P.L., Zu, J.X., & Niu, B. (2018). Responses of normalized difference vegetation index (NDVI) to precipitation changes on the grassland of Tibetan Plateau from 2000 to 2015. Ying yong sheng tai xue bao= The journal of applied ecology, 29(1), 75-83.
    18. Zhang, Y. X., Li, X. B., & Chen, Y. H. (2003). Overview of field and multi-scale remote sensing measurement approaches to grassland vegetation coverage. Advances in Earth Science, 18(1), 85-093.