بررسی اثر خشکسالی بر پوشش گیاهی با استفاده از داده های سنجش از دور ماهواره‌ای و هواشناسی (مطالعه موردی: دشت قزوین)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته دکتری مرتعداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

2 دانشیار دانشکده منابع طبیعی دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

3 استاد دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران.

4 استادیار دانشکده منابع طبیعی دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

5 دانشیار دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

اکوسیستم دشت نسبت به تغییرات محیطی بسیار آسیب‌پذیر است و خشکسالی مشهورترین محرک شناخته شده تغییر اکوسیستم است که بعد از وقوع، شناسایی اثرات و پیامدهای آن دشوار است. در این تحقیق به منظور بررسی شیب تغییرات پوشش گیاهی نسبت به خشکسالی از شاخص NDVI تصاویر مودیس و شاخص SPI طی سال‌های 2001 تا 2016 استفاده شد و نقشه تغییرات پوشش گیاهی نسبت به خشکسالی با 5 کلاس تنش خشکسالی شامل کلاس‌های خیلی کم، کم، متوسط، زیاد و خیلی زیاد تهیه شد تا امکان ارزیابی مناسب خشکسالی در مقیاس‌های زمانی مشخص شده فراهم شود. نتایج الگوی شیب تغییرات مکانی پوشش گیاهان نسبت به خشکسالی نشان داد که سراسر دشت متحمل شیب تغییرات پوشش گیاهی است و از شرق به غرب دشت قزوین از میزان شیب تغییرات پوشش گیاهی و حساسیت اراضی در برابر خشکسالی کاسته شده است. از طرفی مساحت‌های کمی از دشت نسبت به وقوع خشکسالی کمتر در معرض خطر هستند و عمده قسمت‌های دشت نسبت به وقوع خشکسالی از حساسیت‌های متوسط تا زیاد برخوردار هستند. به گونه‌ای که بیشترین درصد مساحت در خشکسالی یک ماهه مربوط به کلاس خشکسالی خیلی کم است اما در خشکسالی‌های 3، 6، 9، 12، 24 و 48 ماهه بیشترین درصد مساحت مربوط به کلاس‌های خشکسالی متوسط و زیاد است. نتایج این تحقیق به عبارتی تعیین شیب تغییرات پوشش گیاهی در برابر خشکسالی در سال‌های گذشته و پیش‌بینی این تغییرات در سال‌های آینده می‌تواند در جهت برنامه‌ریزی و استفاده بهینه از منابع، کنترل و مهار تغییرات غیر اصولی در آینده گام مهمی باشد.

کلیدواژه‌ها


عنوان مقاله [English]

The Effects of Drought on Vegetation Using using satellite remote sensing and meteorological data (Case study: Qazvin plain)

نویسندگان [English]

  • Setareh Bagheri 1
  • Reza Tamartash 2
  • Mohammad Jafari 3
  • Mohammad Reza Tatian 4
  • Arash Malekian 5
1 PhD Graduate in rangeland management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
2 Associate Professor, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
3 Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
4 Assistant Professor, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
5 Associate Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Plain ecosystem is highly vulnerable to environmental changes, and drought is the most famous ecosystem change driver that is difficult to identify after its occurrence. In this research, to study the slope of vegetation changes against drought, the NDVI index of MODIS images and the SPI index from 2001 to 2016 were used and the map of vegetation changes against drought with five drought stress classes included very low classes, Low, moderate, high and very high, so that a suitable assessment of the drought can be made at specified time scales. The results of slope pattern of spatial change of vegetation against drought showed that across the plain vegetation changes have declined, and from east to west of Qazvin plain, the slope of vegetation changes and land susceptibility to drought have been reduced. So that the most percentage of area in a one-month drought related to the drought class is very low, but in droughts of 3, 6, 9, 12, 24 and 48 months, the highest percent of the area belonged to moderate and high drought classes. The results of this study, the determination of the level of vegetation changes in against drought in the past years and prediction of these changes in the future years, can be used in the planning and optimal use of resources, control changes in the future.

کلیدواژه‌ها [English]

  • Simple Linear regression
  • Least Square Error
  • MODIS
  • MOD13Q1
  • NDVI
  • SPI
[1] Abtahi, F. (2001). Recognition of ecological regions of Qazvin province. Institute of Forestry and Rangeland Research, 274p.
[2] Adamchuk, V.I., Perk, R.L. and Schepers, J.S. (2003). Application of remote sensing in sitespecific management. Precision Agriculture Extension Circular EC 03-702. Lincoln,Nebraska: University of Nebraska Cooperative Extension.
[3] Boken, K., Cracknell, P. and Heathcote, L. (2005). Monitoring and predicting agricultural drought. first.ed. Oxford University press inc., 472p.
[4] Brown, J.R., Kluck, D., McNutt, C. and Hayes, M. (2016). Assessing drought vulnerability using a socioecological framework. Rangelands, 38(4), 162-168.
[5] Damavandi, A.A., Rahimi, M., Yazdani, M.R. and Noroozi, A.A. (2016). Spatial monitoring of agricultural drought through time series of NDVI and LST indices MODIS data (Case study: Markazi province). Scientific - Research Quarterly of Geographical Data (SEPEHR), 25(99), 115-126.
[6] Domonkos, P., Szalai, S. and Zoboki, J. (2001). Analysis of drought severity using PDSI and SPI indices. IdŰjárás ISSN 0324-6329 CODEN IDOJA4, 105, 93-107.
[7] Downing, T.E. and Baker, K. (2000). Drought discourse and vulnerability. In: Wilhite, D.A. (Ed.), Drought: A Global Assessment. Routledge, London, UK, pp. 213-230.
[8] Ebrahimzadeh, S., Bazrafshan, J. and Ghorbani, Kh. (2013). Study of the identification of the variations in plant vegetation using remote sensing and ground-based drought indices (Case study: Kermanshah province). Journal of Agricultural Meteorology, 1(1), 37-48.
[9] Edwards, D.C. and Mckee, T.B. (1997). Characteristics of 20th century drought in the United State at multiple time scales. Journal of the Atmospheric Sciences, 634, 1-30.
[10] Erfanian, M., Vafaei,N. and Rezaeianzade, M. (2014). A new method for drought risk assessment by integrating TRMM monthly rainfall data and the Terra/MODIS NDVI data in Fars province, Iran. Journal of Physical Geography Research Quarterly 46(1), 93-108.
[11] Ezzine, H., Bouziane, A. and Ouazar, D. (2014). Seasonal comparisons of meteorological and agricultural drought indices in morocco using open short time-series data. Int. J. Appl. Earth Obs. Geoinf. 26(1), 36-48.
[12] Giddings, L., Soto, M., Rutherford, B.M. and Maarouf, A. (2005). Standardized precipitation index zones for Mexico, Atmosphera, pp. 33-56.
[13] Gouveia, C., Trigo, R.M. and Dacamra, C.C. (2009). Drought and vegetation stress monitoring in Portugal using satellite data, Nat. Hazards Earth Syst. Sci., 9.
[14] Han, G. and Xu, J. (2007). Vegetation classification in eastern china using time series NDVI Images. Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901N.
[15] Hashemi Nasab, A., Ansary, H. and Sanaei-Nejad, S.H. (2018). Analyzing drought history using fuzzy integrated drought index (FIDI): a case study in the Neyshabour basin, Iran. Arabian Journal of Geosciences, 11, 390, 1-10.
[16] Hayes, M.J. (2000). What is drought? national drought mitigation center, URL:www.drought.unl.edu/whatis/indices.htm.
[17] Himanshu, S.K., Singh, G. and Kharola, N. (2015). Monitoring of drought using satellite data. Int. Res. J. Earth Sci. 3, 66-72.
[18] Ibrahim, Y.Z., Balzter, H., Kaduk, J. and Tucker, C.J. (2015). Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in Sub-SaharanWest Africa from 1982 to 2012. Remote Sens., 7, 5471-5494.
[19] IPCC, (2007). Climate change 2007. Contribution of working groups I and II to the fourth assessment report of the intergovernmental panel on climate change. In: contribution of working groups I and II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge university press Cambridge, United Kingdom, New York, USA.
[20] Ivits, E., Horison, S., Fenshold, R. and Cherlet, M. (2014). Drought footprint on European ecosystems between 1999 nd 2010 assessed by remotely sensed vegetation phenology and productivity. Global Change Biology, 20, 581-593.
[21] Ji, L. and Peters, A.J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87, 85-98.
[22] Jiapaer, G., Liang, S., Yi, Q. and Liu, J. (2015). Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator, Ecol. Indic, 58, 64-76.
[23] Karimi, S., Javdani, R., Fakhri Riani, N. and Sedghi, M. (2013). Evaluation of the relationship between rangeland production capacity and rainfall range (Case study: Iranshahr city). First National Conference on Climatology of Iran, 1-11.
[24] Keersmaecker, W., Lhermitte, S., Hill, M.J., Tits, L., Coppin, P. and Somers, B. (2017). Assessment of regional vegetation response to climate anomalies: a case study for Australia using GIMMS NDVI time series between 1982 and 2006. Remote Sens, 9(34), 1-17.
[25] Khosravi, H., Haydari, E., Shekoohizadegan, S. and Zareie, S. (2017). Assessment the effect of drought on vegetation in desert area using Landsat data. The Egyptian Journal of Remote Sensing and Space Sciences, 20, 3-12.
[26] Liu, Y., Li, Y., Li, S. and Motesharrei, S. (2015). Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sens., 7(10), 13233-13250.
[27] Lloret, F., Lobo, A., Estevan, H., Maisongrande, P., Vayreda, J. and Terradas, J. (2007). Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests. Ecology, 88, 2270-2279.
[28] Llyod Hughes, B. and Saunders, M.A. (2002). Drought climatology for Europe. International Journal of Climatology, 22, 1571-1592.
[29] Maghsoud, F., Malekian, M., Mohseni Saravi, A. and Bazrafshan A. (2017). Monitoring and zoning of meteorological drought characteristics using markov chain model and geostatistical methods (Case study: Qazvin province). Journal of Range and Watershed Management, 69(4), 1075-1099.
[30] McKee, T.B., Doeskin, N.J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In: proceedings of the 8th conference on applied climatology.
[31] Mckee, T.B., Doesken, N.J. and Kleist, J. (1995). Drought monitoring with multiple time scales, the 9th conference on applied climatology, american meteorological society, Boston, 233-236.
[32] Mishra, A.K. and Desai, V.R. (2006). Drought forecasting using feed-forward recursive neural network. Journal of Ecological Modeling, 198(1-2), 127-138.
[33] Momeni, M. and Faal Ghayoumi, A. (2007). Statistical analysis using SPSS. New Book Publishing, Tehran. 304p.
[34] Montaldo, N., Albertson, J.D. and Mancini, M. (2008). Vegetation dynamics and soil water balance in a water-limited mediterranean ecosystem on Sardinia, Italy. Hydrol. Earth Syst. Sci. Discuss, 5, 219–255.
[35] Morid, S., Smakhtin, V. and Moghaddasi, M. (2006). Comparison of seven meteorological indices for drought monitoring in Iran. Int. J. Climatol. 26(7), 971-985.
[36] Naserzadeh, M.H. and Ahmadi, I. (2013). Assessment of meteorological drought indices performance in drought evaluation and zoning in Qazvin province. Journal of Applied researches in Geographical Sciences, 12(2), 141-162.
[37] Nasrollahi, M., Khosravi, H., Moghaddamneia, A. and Malekeian, A. (2015). Assessment of drought hazard index using standardized precipitation index (Case study: Semnan province, Iran). Journal of Agricultural Meteorology, 3(1), 66-57, (in Farsi).
[38] Paulo, A.A., Ferreira, E., Coelho, C. and Pereira, L.S. (2005). Drought class transition analysis through markov and loglinear models, an approach to early warning. Agricultural Water Management, 77(1-3), 59-81.
[39] Pe˜nuelas, J., Rutishauser, T. and Filella, I. (2009). Phenology feedbacks on climate change. Science, 324, 887-888.
[40] Ramchandra, T.V. (2008). Regional land cover mapping using remote sensing data. Journal of Agricultural, food and Environmental sciences, 2(1), 1-15.
[41] Rahimi, D., Movahedi, S. and Barghi, H. (2009). The effect of drought intensity with normal precipitation index (Case study: Sistan and Baluchestan province). Journal of Geography and Environmental Planning, 20(36), 56-43.
[42] Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFCT Type III Final Report, 371p.
[43] Salahi, B., Nohegar, A. and Behrouzi, M. (2016). The modeling of precipitation and future droughts of Mashhad plain using stochastic time series and standardized precipitation index (SPI). Int. J. Environ. Res., 10(4), 625-636.
[44] Salimi fard, M., Sanaei, S.H., sepehr A. and Sabet dizavandi., L. (2018). Drought monitoring based on satellite index (SDI) and TRMM data. (Case study: Khorasan Razavi province). Nivar Journal of Meteorological Organization, 42(102-103), 19-30.
[45] Sepulcre-Canto, G., Horion, S., Singleton, A., Carrao, H. and Vogt, J. (2012). Development of a combined drought indicator to detect agricultural drought in Europe. Natural Hazards and Earth System Sciences, 12, 3519-3531.
[46] Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., Rippey, B., Tinker, R., Palecki, M., Stooksbury, D., Miskus, D. and Stephens, S. (2002). The drought monitor. Bulletin of the American Meteorological Society, 83, 1181-1190.
[47] Tagesson, T., Fensholt, R., Guiro, I., Rasmussen, M.O., Huber, S., Mbow, C., Garcia, M., Horion, S., Sandholt, I., Holm-Rasmussen, B., Gottsche, F.M., Ridler, M.E., Olen, N., Lundegard Olsen, J., Ehammer, A., Madsen, M., Olesen, F.S. and Ardo, J. (2015). Ecosystem properties of semiarid savanna grassland in west Africa and its relationship with environmental variability, Glob. Chang. Biol, 21(1), 250-264.
[48] Thanh, N.T. (2018). A proposal to evaluate drought characteristics using multiple climate models for multiple timescales. Climate, 6(79), 1-16.
[49] Tirivarombo, S. and Hughes, D.A. (2011). Regional droughts and food security relationships in the Zambezi river basin. Phys. Chem. Earth, 36, 977-983.
[50] Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. remote sensing of environment, 8, 127-150.
[51] Tucker, C.J., Justice, C.O. and Prince, S.D. (1986). Monitoring the grasslands of the Sahel 1984-1985. Int. Journal Remote Sens. 7(11), 1571-1581.
[52] Trigo, R.M., Gouveia, C. and Barriopedro, D. (2010). The intense 2007-2009 drought in the fertile crescent: impacts and associated atmospheric circulation. Agric. For. Meteorol. 150, 1245-1257.
[53] Vicente-Serrano, S.M., López-Moreno, J.I., Beguería, S., Lorenzo-Lacruz, J., Sanchez-Lorenzo, A., García-Ruiz, J.M., Azorin-Molina, C., Móran-Tejeda, E., Revuelto, J., Trigo, R., Coelho, F. and Espejo, F. (2014). Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ. Res. Lett. 9, 044001.
[54] Wan, Z., Wang, P. and Li, X. (2004). Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International. Journal Remote Sensing, 25(1), 61-72.
[55] Wang, H., Lin, H. and Liu, D. (2014). Remotely sensed drought index and its responses to meteorological drought in southwest China. Remote Sensing Letters, 5(5), 413-422.
[56] Weerasinghe, V.P.A., Gamanayake, B.G.N.N. and Kadupitiya, H.K. (2017). Agricultural drought assessment using MODIS satellite data in Kurunegala district, Sri Lanka. International Journal of Scientific & Engineering Research, 8(8), 614-621.
[57] Wilhite, D., Svoboda, M. and Hayes, M. (2007). Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resources Management, 21, 763-774.
[58] WMO. (1985). Standardized precipitation index - user guide, WMO No 1090. World Meteorological Organization, Geneva.
[59] WMO. (2012). Standardized pecipitation index-user guide; WMO-No. 1090; WMO: Geneva, Switzerland.
[60] Ziolkowska, J.R. (2016). Socio-economic implications of drought in the agricultural sector and the state economy. Economies, 4(3), 19.