مدیریت بیابان

مدیریت بیابان

بررسی عملکرد شاخص‌های تک متغیره و یکپارچه چند متغیره در پایش خشکسالی کشاورزی (بررسی موردی: حوزه آبخیز کرخه)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 پژوهشگر بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کرمانشاه، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمانشاه، ایران
2 استادیار گروه مدیریت حوزه‌های آبخیز، پژوهشکده حفاظت خاک و آبخیزداری، تهران، ایران.
3 دانشیار پژوهش، بخش تحقیقات جنگل‌ها و مراتع، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کرمانشاه، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمانشاه، ایران.
4 استاد گروه آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران.
چکیده
خشکسالی یکی از مخاطرات اقلیمی است که در سال‌های اخیر، به‌­طور قابل توجهی بر شرایط محیطی و اجتماعی - اقتصادی ایران تأثیر گذاشته است. از این­ رو بررسی و پایش آن، به‌­منظور اطلاع از وقوع خشکسالی و کاهش آسیب ­پذیری، بسیار ضروری و مهم است. در پژوهش حاضر کارایی شاخص‌­های یکپارچه چند متغیره نسبت به شاخص­‌های تک متغیره در پایش خشکسالی کشاورزی بر اساس تصاویر ماهواره‌­ای در حوزه آبخیز کرخه مورد بررسی قرارگرفت. بدین منظور در آغاز شاخص­­‌های سنجش از دوری چند متغیره VHI و NVSWI و شاخص تک متغیره NLSWI محاسبه شد و وضعیت خشکسالی در کل حوزۀ آبخیز کرخه توسط این سه شاخص برای دورۀ زمانی 1380 تا 1401 در اردیبهشت ماه پایش شد. به‌­طور کلی  نتایج نشان داد طی دورۀ آماری 1380 تا 1383، 1385 تا 1389، 1391و 1392 تا 1394 در منطقه مورد بررسی خشکسالی‌های مکرر با شدت کم تا شدید رخ­داده است. سپس عملکرد شاخص­های چند متغیره و تک متغیره با استفاده از شاخص SPI در مقیاس‌­های یک، سه و شش ماهه و شاخص SDI مورد تجزیه و تحلیل قرار گرفت. طبق نتایج صحت­‌سنجی پژوهش حاضر،  بیشترین ضریب همبستگی بین شاخص NVSWI  با SPI-1 و SDI به ترتیب برابر با 0.71 و 0.64 مشاهده شد و همچنین شاخص VHI نیز بعد از شاخص NVSWI، همبستگی نسبتاً خوبی با SPI-1 (0.66) و SDI (0.56) دارد و شاخص تک متغیره  کمترین همبستگی را با SPI-1 (0.42)، SPI-2 (0.28)، SPI-3 (0.21) و SDI (0.40) دارد. بنابراین طبق نتایج شاخص­‌های یکپارچه چند­ متغیره  NVSWI و VHI نسبت به شاخص تک ­متغیره NLSWI  نتایج بهتری را ارائه می­‌دهد. زیرا شاخص‌­های چند ­متغیره  NVSWI و VHI، عواملی مثل وضعیت پوشش گیاهی و دمای سطح زمین را همزمان در پایش خشکسالی منظور می‌­نماید، بنابراین شاخص­‌های چند متغیره نسبت به شاخص­‌های تک متغیره وضعیت خشکسالی را بخوبی پایش می­‌کند.
کلیدواژه‌ها

موضوعات


  1. Abbasi, E., & Etemadi, H. (2023). Bushehr drought monitoring based on SPI and VCI indicators using MODIS sensor images. Geographic Space, 23(82), 179-200. [In Persian]
  2. Arekhi, S., Barzegar Savasarib, M., & Emadaddiana, S. (2022). Investigating the indicators resulting from remote sensing technology in drought assessment using MODIS images (Case Study: Qom, Isfahan, Chaharmahal and Bakhtiari, and Markazi Provinces). Journal of Geography and Environmental Hazards, 3(11), 189-224. DOI:10.22067/geoeh.2021.72253.1102 [In Persian]
  3. Amini Bazyani, S., Zare Abianeh, H., & Akbari, M. (2016). Estimation of temperature and vegetation index of land surface using remote sensing data (case study: Hamadan province), Iranian Water Research Journal, 46(3), 333-348. [In Persian]
  4. Asadi Aghbalaghi, F., Mirabbasi Najafabadi, R., Nasr Esfahani, M.A., & Ghasemi Dastgerdi, A.R. (2017). Development of a new composite drought index (CDI) based on shannon's entropy theory for multivariate assessment of drought in shahrekord plain. Arid regions Geographic Studies, 8(29), 87-102. [In Persian]
  5. Carlson, T.N., Gillies, R.R., & Perry, E.M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9, 161–173. DOI: 1080/02757259409532220
  6. Chang, J., Li, Y., Wang, Y., & Yuan, M. (2016). Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China. Journal of Hydrology, 540, 824-834. DOI: 1016/j.jhydrol.2016.06.064
  7. Chen, S., Zhong, W., Pan, S., Xie, Q., & Kim, T.W. (2020). Comprehensive drought assessment using a modified composite drought index: A case study in Hubei Province, China. Water, 12(2), 462-474. DOI: 3390/w12020462
  8. Cheng, M., Zhong, L., Ma, Y., Wang, X., Li, P., Wang, Z. & Qi, Y. (2023). A new drought monitoring index on the Tibetan Plateau based on multisource data and machine learning methods. Remote Sensing, 15(2), 512-529. DOI: 3390/rs15020512
  9. Darvand, S., Khosravi, H., Eskandari Damaneh, H., & Eskandari Damaneh, H. (2021). Investigating the trend of NDVI changes derived from MODIS sensor imagery (Case Study: Isfahan Province). Degradation and Rehabilitation of Natural Land, 1(2), 69-79. [In Persian]
  10. Ding, Y., He, X., Zhou, Z., Hu, J., Cai, H., Wang, X., Li, L., Xu, J. & Shi, H. (2022). response of vegetation to drought and yield monitoring based on NDVI and SIF. Catena, 219, 106328-106340. DOI: 1016/j.catena.2022.106328
  11. Fazel Dehkordi, L., Azarnivand H., Zare Chahouki M.A., Mahmoudi kohan F., & Khalighi Sigaroodi, Sh.  (2016). Drought monitoring using vegetation index (NDVI) (case study: rangeland of Ilam province), Journal of range and watershd managment (Iranian Journal of Natural Resources), 69(1), 141-154. DOI: 10.22059/jrwm.2016.61739 [In Persian]
  12. Ebrahimzadeh, S., Bazrafshan, J., & Ghorbani, Kh. (2013). Comparative study between satellite and ground-based drought indices using change vector analysis technique (Case Study of Kermanshah Province). Journal of Water and Soil, 27(5), 1034-1045. DOI: 10.22067/jsw.v0i0.20985 [In Persian]
  13. Fassouli, V.P., Karavitis, C.A., Tsesmelis, D.E. & Alexandris, S.G. (2021). Factual Drought Index (FDI): a composite index based on precipitation and evapotranspiration. Hydrological Sciences Journal, 66(11), 1638-1652. DOI: 1080/02626667.2021.1957477
  14. Hamzeh, S., Farahani, Z., Mahdavi, S., Chatrobgoun, O., & Gholamnia, M. (2017). Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran). Journal of Spatial Analysis Environmental Hazards, 4(3), 53-70. [In Persian]
  15. Hao, Z., Singh, V.P., & Xia, Y. (2018). Seasonal drought prediction: Advances, challenges, and future prospects. Reviews of Geophysics56(1), 108-141. DOI: 1002/2016RG000549
  16. Hosseinzadeh, J., Tongo, A., Najafifar, A., & Hosseini, A. (2018). Relationship between soil moisture changes and climatic indices in the Mele-Siah forest site of Ilam Province. Journal of Water and Soil, 32(4), 821-830. DOI: 22067/jsw.v32i4.71927 [In Persian]
  17. Huang, X., Feng, S., Zhao, S., Fan, J., Qin, Z. & Zhao, S., (2023). Assessment of different satellite image-derived drought indices over the contiguous united states: a comparison in different climates, vegetation cover types, and soil layers. Water15(20), 3634-3660. DOI: 3390/w15203634
  18. Iran water resources management. (2024). Retrieved June 12, 2024, from http://www.wrm.ir [In Persian]
  19. Jiao, W.Z., Zhang, L.F., Chang, Q., Fu, D.J., Cen, Y., & Tong, Q.X. (2016). Evaluating an Enhanced Vegetation Condition Index (EVCI) based on VIUPD for drought monitoring in the continental united states. Remote Sensing, 8, 224. DOI: 3390/rs8030224
  20. Karimi, M., & Shahedi, K. (2018). Investigation of meteorological, hydrological and agricultural drought using drought indices (Case study: Gharehsou watershed). RS & GIS for Natural Resources, 9(2), 1-16. [In Persian]
  21. Karimi, M., Shahdi, K., & Khosravi, K. (2016). Investigation of meteorological and hydrological drought using drought indicators in Qarasu basin. Earth and Space Physics, 42(1), 159-170. DOI: 22059/jesphys.2016.54241 [In Persian]
  22. Karimi, M., Shahedi, K., Raziei, T., & Miryaghoubzadeh, M. (2022). Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index. Stochastic Environmental Research and Risk Assessment, 36(11), 3707-3724. DOI: 1007/s00477-022-02220-3
  23. Karimi, M., Shahedi, K., Raziei, T., & Miryaghoobzadeh, M. (2020). Analysis of performance of vegetation indices on agricultural drought using remote sensing technique in Karkheh basin. Iranian Journal of Remote Sensing & GIS, 11(44), 29-46. DOI: 52547/gisj.11.4.29 [In Persian]
  24. Keyantash, J. A., & Dracup, J.A. (2004). An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research, 40(9), 333-341. DOI: 10.1029/2003WR002610, 2004
  25. Khosravi Yeganeh, S., Karampour, M., & Nasiri, B. (2023). Evaluation of vegetation drought in Kermanshah province using infrared images. Journal of Geographical Studies of Mountainous Areas, 3(12), 19-39. DOI: 10.52547/gsma.3.4.19 [In Persian]
  26. Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100. DOI: 1016/0273-1177(95)00079-T
  27. Kogan, F.N. (1997). Global drought watches from space. Bulletin of the American Meteorological Society, 78, 621–636. DOI: 1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
  28. Kogan, F.N. (2000). Early Warning Systems for Drought Preparedness and Drought Management. Lisbon, Portugal: World Meteorological Organization.
  29. Liang, L., Qiu, S., Yan, J., Shi, Y. & Geng, D. (2021). VCI-based analysis on spatiotemporal variations of spring drought in China. International Journal of Environmental Research and Public Health, 18(15), 7967-7981. DOI: 3390/ijerph18157967
  30. Liu, Q., Zhang, S., Zhang, H., Bai, Y. & Zhang, J. (2020). Monitoring drought using composite drought indices based on remote sensing. Science of the total environment, 711, 134585-134605. DOI:1016/j.scitotenv.2019.134585
  31. McKee, T.B. (1995). Drought monitoring with multiple time scales. Paper presented at the 9th Conference of Applied Climatology, Boston.
  32. McKee, T.B., Doesken, N.J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Paper presented at the 8th conference of Applied climatology, Anaheim, California.
  33. Miralizadeh, A., Hejabi, S., & Kouchakzadeh, M. (2022). Evaluation of remote sensing indicators in drought conditions (case study: Urmia plain). Journal of Meteorology and Atmospheric Sciences, 5(2), 132-141. DOI: 22034/jmas.2023.404574.1202 [In Persian]
  34. Mirmousavi, S.H., & Kareimei, H. (2013). Effect of drought on vegetation cover using MODIS sensing images case: Kurdistan province. Geography and Development, 11(31), 57–76. DOI: 22111/gdij. 2013.794 [In Persian]
  35. Mishra, A.K., & Singh, V.P. (2010). A review of drought concepts. Journal of Hydrology, 391(1), 202-216. DOI: 1016/j.jhydrol.2010.07.012
  36. Mohammadi, Sh., Habashi, Kh., & Pourmanafi, S. (2018). Monitoring and prediction land use/ land cover changes and its relation to drought (Case study: sub-basin Parsel B2, Zayandeh Rood watershed). RS & GIS for Natural Resource, 9(1), 24-39. [In Persian]
  37. Mullapudi, A., Vibhute, A.D., Mali, S. & Patil, C.H. (2023). A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities. Applied Geomatics, 15(1), 1-13. DOI: 1007/s12518-022-00484-6
  38. Nalbantis I, Tsakiris G. (2009). Assessment of hydrological drought revisited. Water Resources Management. 23(5), 881-897. DOI: 10.1007/s11269-008-9305-1
  39. Navabi, N., Moghaddasi, M., & Gangi, N. (2021). Assessment of agricultural drought monitoring using various indices based on ground-based and remote sensing data (Case Study: Lake Urima Basin). Watershed Engineering and Management, 13(1), 1-12. DOI: 22092/ijwmse.2020.126860.1684 [In Persian]
  40. Niazi, Y., Talebi, A., Mokhtari, M.H., & Vazifedoust, M. (2017). Assessing the efficiency of Vegetation Drought Index (VDI) and Temperature Drought Index (TDI) based on satellite images in central Iran. Journal of Arid Biome, 7(1), 79-94. DOI: 29252/aridbiom.7.1.79 [In Persian]
  41. Rajsekhar, D., Singh, V.P., & Mishra, A.K. (2015). Multivariate drought index: An information theory-based approach for integrated drought assessment. Journal of Hydrology, 526, 164-182. DOI: 1016/j.jhydrol.2014.11.031
  42. Rezaei Banafsheh, M., Rezaei, A., & Faridpor, M. (2015) Analyzing agricultural drought in east Azarbaijan province emphasizing remote sensing technique and vegetation condition index. Water and Soil Science, 25(1), 113–123. [In Persian]
  43. Rezaei Moghadam, M.H., Valizadeh Kamran, K.H., Rostamzadeh, H., & Rezaei, A. (2012) Evaluating the adequacy of MODIS in the assessment of drought (case study: Urmia lake basin). Geography and Environmental Sustainability, 2(5), 37–52. [In Persian]
  44. Sandeep, P., Reddy, G.O., Jegankumar, R. & Kumar, K.A. (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, p.107033. DOI: 1016/j.ecolind.2020.107033
  45. Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment. 79, 213–224. DOI: 1016/S0034-4257(01)00274-7
  46. Shen, R., Huang, A., Li, B. & Guo, J. (2019). Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 79, 48-57. DOI: 1016/j.jag.2019.03.006
  47. Solaimani, K. Darvishi, Sh., & Shokrian, F. (2019). Analysis of agricultural drought using remote sensing indices (Case study: Marivan city). RS & GIS for Natural Resources, 10(2), 15-35. [In Persian]
  48. Sur, C., Park, S.Y., Kim, T.W. & Lee, J.H. (2019). Remote sensing-based agricultural drought monitoring using hydrometeorological variables. KSCE Journal of Civil Engineering, 23(12), 5244-5256. DOI: 1007/s12205-019-2242-0
  49. Tucker, C.J. (1989). Comparing SMMR and AVHRR data for drought monitoring. International Journal of Remote Sensing, 10, 1663- 1672. DOI: 1080/01431168908903997
  50. Wang, K., Li, T. & Wei, J. (2019). Exploring drought conditions in the three river headwaters region from 2002 to 2011 using multiple drought indices. Water, 11(2), 190-210. DOI: 3390/w11020190
  51. Winkler, K., Gessner, U., & Hochschild, V. (2017). Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000–2016: Rainfall anomalies and vegetation condition in the Context of ENSO. Remote Sensing, 9, 831-858. DOI: 3390/rs9080831
  52. Wu, H., Hayes, M.J., Weiss, A., & Hu, Q. (2001). An evaluation of the standardized precipitation index, the China-z index and the statistical z-score. International Journal of Climatology, 21, 745–758. DOI: 1002/joc.658
  53. Zhang, Y., Xie, D., Tian, W., Zhao, H., Geng, S., Lu, H., Ma, G., Huang, J. & Choy Lim Kam Sian, K.T. (2023). Construction of an integrated drought monitoring model based on deep learning algorithms. Remote Sensing, 15(3), 667-687. DOI: 3390/rs15030667
دوره 12، شماره 3 - شماره پیاپی 31
6 مقاله
پاییز 1403
صفحه 99-122

  • تاریخ دریافت 30 تیر 1403
  • تاریخ بازنگری 23 شهریور 1403
  • تاریخ پذیرش 24 شهریور 1403