بررسی و پیش بینی تاثیرات خشکسالی بر تغییرات دریاچه مهارلو و کاربری‌های اطراف آن با استفاده از سنجش از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشیار بخش جغرافیا، دانشکده اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز، شیراز، ایران

10.30488/ccr.2022.354398.1084

چکیده

در سالهای اخیر، پدیده خشکسالی بسیاری از حوضه‌های آبخیز ایران را تحت تاثیر قرار داده و همه ساله باعث تغییرات زیادی در کاربری اراضی و مساحت دریاچه‌ها می شود. این پژوهش سعی دارد که با استفاده از شاخص‌های سنجش از دور خشکسالی در سال‌های 2000، 2010 و 2020، وضعیت خشکسالی و ارتباط آن با تغییرات کاربری اراضی در حوضه ابخیز دریاچه مهارلو در جنوب ایران را مورد بررسی قرار دهد. بدین صورت که ابتدا با استفاده از شاخص‌های EVI، NDVI، VCI وضعیت خشکسالی تعیین شد، سپس نقشه‌های کاربری اراضی برای سال‌های 2000 و 2020 با استفاده از تصاور ماهواره ای لندست ETM+8 تهیه شد. در ادامه با استفاده از روش رگرسیون مهمترین شاخص‌های خشکسالی موثر برای کاربری‌های اراضی تعیین گردید. همچنین با استفاده از زنحیره مارکوف و کومارکوف وضعیت شاخص‌های خشکسالی و کاربری اراضی برای سال 2040 پیش بینی شد. با توجه به نتایج شاخص‌ها مشخص شد که بخش‌های جنوبی بیشتر از دیگر بخش‌ها در خطر خشکسالی قرار دارد. نتایج حاصل از بررسی تغییرات کاربری اراضی نشان داد که در سال 2020 نسبت به سال 2000 اراضی بیشتری در کلاس‌های شور و بایر قرار گرفته اند. نتایج نشان داد که بر اساس روش رگرسیون مهمترین شاخص‌ها در ارتباط با کاربری اراضی شاخص NDVI می‌باشد. نتایج حاصل از زنجیره مارکوف و کومارکوف برای پیش بینی شاخص‌ها نشان داد که این روش دارای دقت قابل قبولی می‌باشد. بطوریکه ضریب کاپا برای شاخص VCI 0.98 می‌باشد که حاکی از دقت بالای مدل در پیش بینی خشکسالی می‌باشد. همچنین نقشه‌های پیش بینی شده توسط این روش نشان داد که در سال 2040 مقادیر این شاخص کاهش می یابد که نشان دهنده خشکسالی بیشتر در منطقه خواهد بود. با مقایسه ای که بین طبقات این شاخص‌ها و مقادیر این شاخص‌ها در سال 2020 و 2000 انجام شد مشخص شد که در سال 2040 احتمال کاهش مقادیر این شاخص و در نتیجه افزایش بیشتر خشکسالی در منطقه وجود دارد. این تغییرات بر روی وضعیت کاربری‌ها تاثیر زیادی خواهد داشت. بطوریکه پیش بینی می شود که مقادیر آب‌ها در این دریاچه با توجه به کاهش مقادیر شاخص خشکسالی NDVI کاهش یابد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating and predicting the effects of drought on the changes of Maharlo Lake and its surrounding uses using remote sensing

نویسندگان [English]

  • saeed negahban
  • marzieyeh mokarram
Associate Professor of Geography Department, Faculty of Economics, Management and Social Sciences, Shiraz University
چکیده [English]

In recent years, the phenomenon of drought has affected many watersheds in Iran and causes many changes in land use and lake area every year. This research tries to investigate the drought situation and its relationship with land use changes in the Abkhiz basin of Maharlo lake in the south of Iran by using the drought remote sensing indicators in the years 2000, 2010 and 2020. In this way, the drought condition was determined using EVI, NDVI, and VCI indicators, then land use maps for the years 2000 and 2020 were prepared using Landsat ETM+8 satellite imagery. In the following, using the regression method, the most important effective drought indicators for land uses were determined. Also, using Markov and Komarkov chains, the state of drought and land use indicators for 2040 was predicted. According to the results of the indicators, it was found that the southern parts are more at risk of drought than other parts. The results of the survey of land use changes showed that in 2020, compared to 2000, more lands were placed in the salty and barren classes. The results showed that according to the regression method, the most important indicators in relation to land use is the NDVI index. The results of Markov and Komarkov chain for predicting indicators showed that this method has acceptable accuracy. Thus, the Kappa coefficient for the VCI index is 0.98, which indicates the high accuracy of the model in predicting drought. Also, the maps predicted by this method showed that in 2040, the values of this index will decrease, which will indicate more drought in the region.

کلیدواژه‌ها [English]

  • remote sensing
  • drought indicators
  • land use changes
  • Markov and Komarkov chain
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  1. Ascott, M.J., Bloomfield, J.P., Karapanos I, et al (2020). Managing groundwater supplies subject to drought: perspectives on current status and future priorities from England (UK). Hydrogeol J 2020 293 29:921–924. https://doi.org/10.1007/S10040-020-02249-0
  2. Chawla I, Karthikeyan L, Mishra AK (2020). A review of remote sensing applications for water security: Quantity, quality, and extremes. J Hydrol 585:124826. https://doi.org/10.1016/J.JHYDROL.2020.124826
  3. Dar SA, Rashid I, Bhat SU (2021). Land system transformations govern the trophic status of an urban wetland ecosystem: Perspectives from remote sensing and water quality analysis. L Degrad Dev 32:4087–4104. https://doi.org/10.1002/LDR.3924
  4. De Necker L, Brendonck L, Van Vuren J, et al (2021). Aquatic Invertebrate Community Resilience and Recovery in Response to a Supra-Seasonal Drought in an Ecologically Important Naturally Saline Lake. Water 2021, Vol 13, Page 948 13:948. https://doi.org/10.3390/W13070948
  5. Dube T, Mutanga O, Seutloali K, et al (2015) Water quality monitoring in sub-Saharan African lakes: a review of remote sensing applications. http://dx.doi.org/102989/1608591420151014994 40:1–7. https://doi.org/10.2989/16085914.2015.1014994
  6. Esandari1 H, Borji M, Khosravi H, et al (2016) Change Detection of of Bakhtegan and Tashk Basin during 2001-2013. Int J For Soil Eros 6:
  7. Ghasemi MM, Pakparvar M, Mokarram M (2021) Preparation of landforms using geomorphon method and its relationship with drought in the east and south of Fars province. Quant Geomorphol Res 10:. https://doi.org/10.22034/GMPJ.2021.279116.1262
  8. Khwarahm NR, Qader S, Ararat K, Fadhil Al-Quraishi AM (2021). Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Earth Sci Informatics 14:393–406. https://doi.org/10.1007/S12145-020-00541-X/FIGURES/6
  9. Li J, Bai Y, Alatalo JM (2020) Impacts of rural tourism-driven land use change on ecosystems services provision in Erhai Lake Basin, China. Ecosyst Serv 42:101081. https://doi.org/10.1016/J.ECOSER.2020.101081
  10. Li Y, Zhao G, Shah D, et al (2021) NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data. Remote Sens 2021, Vol 13, Page 565 13:565. https://doi.org/10.3390/RS13040565
  11. H. B, A. B, G.A. S (2016). Analysis Of Changes In The Bakhtegan Lake Water Body Under The Influence Of Natural And Human Factors. 12:1–11
  12. Mashee FK, Ali A-RB, Jasim MS (2020) Spatial monitoring for degradation Al-Razzaza Lake by analysis temporal of remote sensing data using geographic information system techniques. Eurasian J Biosci 14:4777–4781. https://doi.org/10.24996/IJS.2017.58.3A
  13. Mello K de, Taniwaki RH, Paula FR de, et al (2020) Multiscale land use impacts on water quality: Assessment, planning, and future perspectives in Brazil. J Environ Manage 270:110879. https://doi.org/10.1016/J.JENVMAN.2020.110879
  14. Messina NJ, Couture RM, Norton SA, et al (2020) Modeling response of water quality parameters to land-use and climate change in a temperate, mesotrophic lake. Sci Total Environ 713:136549. https://doi.org/10.1016/J.SCITOTENV.2020.136549
  15. Mirgol B, Nazari M, Etedali HR, Zamanian K (2021) Past and future drought trends, duration, and frequency in the semi-arid Urmia Lake Basin under a changing climate. Meteorol Appl 28:e2009. https://doi.org/10.1002/MET.2009
  16. Nobre RLG, Caliman A, Cabral CR, et al (2020) Precipitation, landscape properties and land use interactively affect water quality of tropical freshwaters. Sci Total Environ 716:137044. https://doi.org/10.1016/J.SCITOTENV.2020.137044
  17. Quiring SM, Ganesh S (2010) Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150:330–339. https://doi.org/10.1016/J.AGRFORMET.2009.11.015
  18. Rahnama MR (2021) Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustain Cities Soc 64:102548. https://doi.org/10.1016/J.SCS.2020.102548
  19. Safarianzengir V, Sobhani B, Madadi A, Yazdani M (2021) Monitoring, analyzing and estimation of drought rate using new fuzzy index in cities of west and southwest of Iran, located in the north of the Persian gulf. Environ Dev Sustain 23:7454–7468. https://doi.org/10.1007/S10668-020-00925-5/FIGURES/7
  20. Singh S, Bhardwaj A, Verma VK (2020) Remote sensing and GIS based analysis of temporal land use/land cover and water quality changes in Harike wetland ecosystem, Punjab, India. J Environ Manage 262:110355. https://doi.org/10.1016/J.JENVMAN.2020.110355
  21. Tan J, Yu D, Li Q, et al (2020) Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Sci Reports 2020 101 10:1–9. https://doi.org/10.1038/s41598-020-66168-6
  22. Tanda AS (2021) Native Bees Are Important and Need Immediate Conservation Measures: A Review †. 1–15. https://doi.org/10.3390/xxxxx
  23. Tucker CJ, Pinzon JE, Brown ME, et al (2010) An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. https://doi.org/101080/01431160500168686 26:4485– https://doi.org/10.1080/01431160500168686
  24. Wang M, Duan L, Wang J, et al (2020) Determining the width of lake riparian buffer zones for improving water quality base on adjustment of land use structure. Ecol Eng 158:106001. https://doi.org/10.1016/J.ECOLENG.2020.106001
  25. Xu D, Lyon SW, Mao J, et al (2020) Impacts of multi-purpose reservoir construction, land-use change and climate change on runoff characteristics in the Poyang Lake basin, China. J Hydrol Reg Stud 29:100694. https://doi.org/10.1016/J.EJRH.2020.100694
  26. Zhou L, Dang X, Sun Q, Wang S (2020) Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustain Cities Soc 55:102045. https://doi.org/10.1016/J.SCS.2020.102045
  27. (2021) EarthExplorer. https://earthexplorer.usgs.gov/. Accessed 11 Nov 2021