تأثیر طوفانهای گردوغبار نمکی در سلامت گیاهان در حوضة شرقی دریاچة ارومیه | ||
| نشریه سنجش از دور و GIS ایران | ||
| مقاله 7، دوره 15، شماره 4 - شماره پیاپی 60، 1402، صفحه 101-118 اصل مقاله (3.26 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.48308/gisj.2023.103099 | ||
| نویسندگان | ||
| فریبا گلریحان1؛ خلیل ولیزاده کامران* 2؛ داوود مختاری2؛ علی اکبر رسولی2 | ||
| 1دانشجوی دکتری گروه جغرافیا، واحد مرند، دانشگاه آزاد اسلامی مرند، مرند، ایران | ||
| 2استاد دانشکدة برنامهریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| دریاچة ارومیه یکی از بزرگترین دریاچههای آب شور در جهان است که متأسفانه درحال خشکشدن است. این مسئله خطرها و نگرانیهای بسیاری را بهویژه در ارتباط با گردوغبارهای نمکی در پهنههای خشکشدة آن، بهوجود آورده است. ازاینرو، در این پژوهش، سعی شد ارتباط پوشش گیاهی و گردوغبار در شهرستانهای اطراف دریاچة ارومیه بررسی شود. درمورد گیاهان، شوری باعث بینظمیهای فیزولوژیک، تنش رشد، فتوسنتز، پروتئین، تنفس، تولید انرژی، پیری زودرس و کاهش آب در گیاه میشود. با توجه به این تأثیرات، سعی شد با استفاده از شاخصهای مرتبط، شامل NDVI، CIre، GCI، CRI2، NDWI، NDII، MSI و PSRI سلامت کلی گیاهان ارزیابی شود. این شاخصها میزان آب گیاه، تنشهای آبی گیاه، ظرفیت فتوسنتز، رشد گیاهان و کمبود آب، میزان کلروفیل، نیتروژن و رنگدانهها را که به انرژی و سلامت گیاه مربوط میشود، ارزیابی میکند. طبق این شاخصها، سلامت گیاهان بهطور کلی در وضعیت مطلوبی قرار دارد و اغلب بیشترین ارزش عددی شاخصها به باغات اختصاص داشت. با استفاده از تصاویر لندست و سنتینلـ 2 و شاخص NDVI، تغییرات پوشش گیاهی منطقه در بازة زمانی 2010 تا 2020 تعیین و سپس با استفاده از پایگاه دادة MERRA-2، میزان غلظت گردوغبار نیز درمورد این بازة زمانی استخراج شد. نتایج نشاندهندة این بود که میانگین NDVI، در منطقة مورد مطالعه، از روندی ثابت با میانگین کلی 2957/0 پیروی میکند و گاه براَثر تأثیرگذاری برخی عوامل بیرونی، مانند گردوغبار، بر میزان آن افزوده و یا از آن کاسته میشود. برایناساس بیشترین میزان (3495/0) میانگین NDVI به سال 2018 و کمترین میزان (2579/0) به سال 2013 تعلق دارد. همچنین برای بررسی میزان ارتباط پوشش گیاهی و گردوغبار، از دو روش رگرسیون خطی و لگاریتمی استفاده شد و نتایج نشان داد، براساس رگرسیون خطی (7703/0) و لگاریتمی (7915/0)، بیشترین ضریب تبیین بین دو شاخص یادشده در ماه مه بوده است. مطالعة جامع شاخصهای سلامت گیاهی و ارتباط آن با رویدادهای طوفانهای گردوغبار از مزایای این روش پیشنهادی بهشمار میرود. | ||
| کلیدواژهها | ||
| گردوغبار؛ شاخصهای سلامت گیاه؛ شوری؛ دریاچة ارومیه | ||
| عنوان مقاله [English] | ||
| The Effect of Salt Dust Storms on the Health of Plants in the Eastern Basin of Urmia Lake | ||
| نویسندگان [English] | ||
| Fariba Gilreyhan1؛ Khalil Valizadeh Kamran2؛ Davood Mokhtari2؛ Ali Akbar Rasouli2 | ||
| 1Ph.D. Student, Dep. of Geography, Marand Branch, Marand Islamic Azad University, Marand, Iran | ||
| 2Prof. of Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran | ||
| چکیده [English] | ||
| Urmia Lake is one of the largest saltwater lakes in the world, which unfortunately is drying up and has caused many dangers and concerns, especially in relation to salt dust in its dried areas. Therefore, in this research, we tried to investigate the relationship between vegetation and dust in the cities around Lake Urmia. Salinity in plants causes physiological disorders; salt stress causes growth, photosynthesis, protein, respiration, energy production, premature senescence, water reduction in plants. Considering these effects, it was tried to evaluate the overall health of plants by using related indicators including NDVI, CIre, GCI, CRI2, NDWI, NDII, MSI, PSRI. These indicators evaluate the amount of plant water, plant water stress, photosynthesis capacity, plant growth and water deficit, the amount of chlorophyll, nitrogen and pigments, which are related to plant energy and health. According to these indicators, the health of plants is generally in a favorable condition, and mostly the highest numerical values of the indicators were assigned to gardens. Using Landsat and Sentinel 2 images and the NDVI index, the vegetation changes of the region were determined in the period from 2010 to 2020, and then using the MERRA-2 database, the amount of dust concentration was also extracted for the mentioned years. The results showed that the average NDVI in the studied area follows a constant trend with an overall average of 0.2957 and sometimes it increases or decreases due to the influence of external factors such as dust. Based on this, the highest (0.3495) average NDVI is related to 2018 and the lowest (0.2579) is related to 2013. Also, two methods of linear and logarithmic regression were used to investigate the relationship between vegetation cover and dust, and the results showed that based on the linear (0.7703) and logarithmic (0.7915) regression, the highest coefficient of explanation between the two mentioned indicators was in May. | ||
| کلیدواژهها [English] | ||
| Dust, Plant health indicators, Salinity, Urmia Lake | ||
| مراجع | ||
|
Abbaszadeh Mazerji, Z., Sadeghi, S. & Hosseinzadeh, S.R., 2012, Evaluation of the Accuracy of the Usual Methods of Estimating Climatic Data of the Studied Area: Temperature and Precipitation in the Kashf River Catchment of Mashhad, The First National Hydrometeorological Conference, Kerman, University of Postgraduate Studies of Industry and Advanced Technology, Kerman. Ahmady-Birgani, H., Ravan, P., Schlosser, J.S., Cuevas-Robles, A., AzadiAghdam, M. & Sorooshian, A., 2020, On the Chemical Nature of Wet Deposition over a Major Desiccated Lake: Case Study for Urmia Lake Basin, Atmospheric Research, 234, P. 104762. https://doi.org/10.1016/j.atmosres.2019.104762 Alavipanah, S. K. (2013). Thermal Remote Sensing and Its Application in the Earth Sciences (2nd ed., p. 1387). University of Tehran. Bahrami, H.A.; Jalali, M., Darvishi Belorani, A. & Azizi, R., 2012, Spatial-Temporal Modeling of Dust Storms in Khuzestan Province, Iran Remote Sensing and GIS, 5(2), PP. 95-114. Bayat, R., Jafari, S., Red Spring, B. & Charkhabi, A.H., 2015, Studying the Impact of Micro-Pollens on Vegetation Changes (Case Study: Shadgan Wetland, Khuzestan), Remote Sensing and Geographic Information System in Natural Resources, 7(2), PP. 17-32. Behrooz, R.D., Esmaili-Sari, A., Bahramifar, N., Kaskaoutis, D.G., Saeb, K. & Rajaei, F., 2017, Trace-Element Concentrations and Water-Soluble Ions in Size-Segregated Dustborne and Soil Samples in Sistan, Southeast Iran, Aeolian Res., 25, PP. 87-105. https://doi.org/10.1016/j.aeolia.2017.04.001 Borlina, C.S. & Rennó, N.O., 2017, The Impact of a Severe Drought on Dust Lifting in California's Owens Lake Area, Sci. Rep., 7, P. 1784. https://doi.org/10.1038/s41598-017-01829-7 Dadashi-Roudbari, A., Ahmadi, M. & Shakiba, A., 2020, Seasonal Study of Dust Deposition and Fine Particles (PM 2.5) in Iran Using MERRA-2 Data, Iranian Journal of Geophysics, PP. 43-59. Fan, B., Guo, L., Li, N., Chen, J., Lin, H., Zhang, X., Shen, M., Rao, Y., Wang, C. & Ma, L., 2014, Earlier Vegetation Green-Up Has Reduced Spring Dust Storms, Scientific Reports, 4, P. 6749. DOI: 10.1038/srep06749 Gao, T., Han, J., Wang, Y., Pei, H. & Lu, Sh., 2011, Impacts of Climate Abnormality on Remarkable Dust Storm Increase of the Hunshdak Sandy Lands in Northern China during 2001–2008, Meteorological Applications, PP. 265-278. https://doi.org/10.1002/met.251 Ghadimi, M., Zare, A., Maqqel, M. & Sahibi, M.R., 2018, Evaluation of the Effects of Dust on the Spectral Behavior of Plants Using Remote Sensing Data, Journal of Mapping Sciences and Techniques, 8(4), PP. 176-163. Gitelson, A.A., Viña, A., Ciganda, V., Rundquist, D. & Arkebauer, T.J., 2005, Remote Estimation of Canopy Chlorophyll Content in Crops, Geophysical Research Letter, 32, P. L08403. https://doi.org/10.1029/2005GL022688 Huang, M., Peng, G., Zhang, J. & Zhang, Sh., 2006, Application of Artificial Neural Networks to the Prediction of Dust Storms in Northwest China, Global and Planetary Change, 52, PP. 216-224. https://doi.org/10.1016/j.gloplacha.2006.02.006 Indoitu, R., Kozhoridze, G., Batyrbaeva, M., Vitkovskaya, I., Orlovsky, N., Blumberg, D. & Orlovsky, L., 2015, Dust Emission and Environmental Changes in the Dried Bottom of the Aral Sea, Aeolian Res., 17, PP. 101-115. https://doi.org/10.1016/j.aeolia.2015.02.004 Issanova, G., Abuduwaili, J., Galayeva, O., Semenov, O. & Bazarbayeva, T., 2015, Aeolian Transportation of Sand and Dust in the Aral Sea region, Int. J. Environ. Sci. Technol., 12, PP. 3213-3224. https://doi.org/10.1007/s13762-015-0753-x Khusfi, Z.E., Roustaei, F., Ebrahimi Khusfi, M. & Naghavi, S., 2019, Investigation of the Relationship between Dust Storm Index, Climatic Parameters, and Normalized Difference Vegetation Index Using the Ridge Regression Method in Arid Regions of Central Iran, Arid Land Research and Management, 34(3), PP. 1-25. Khusfi, Z.E., Khosroshahi, M., Roustaei, F. & Mirakbari, M., 2020, Spatial and Seasonal Variations of Sand-Dust Events and Their Relation to Atmospheric Conditions and Vegetation Cover in Semi-Arid Regions of Central Iran, Geoderma, 365, P. 114225. https://doi.org/10.1016/j.geoderma.2020.114225 Lanfredi, M.; Coppola, R.; Simoniello, T.; Coluzzi, R.; D'Emilio, M.; Imbrenda, V.; Macchiato, M. Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions. Remote Sens. 2015, 7, 8154-8179. https://doi.org/10.3390/rs70608154 Löw, F., Navratil, P., Kotte, K., Schöler, H.F. & Bubenzer, O., 2013, Remote-Sensing-Based Analysis of Landscape Change in the Desiccated Seabed of the Aral Sea—A Potential Tool for Assessing the Hazard Degree of Dust and Salt Storms, Environ. Monit. Assess., 185, PP. 8303-8319. https://doi.org/10.1007/s10661-013-3174-7 Merzlyak M.N., Gitelson A.A., Chivkunova O.B., Rakitin V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 1999;106:135–141. doi: 10.1034/j.1399-3054.1999.106119.x. Molod, A., Takacs, L., Suarez, M. & Bacmeister, J., 2015, Development of the GEOS-5 Atmospheric General Circulation Model: Evolution from MERRA to MERRA2, Geoscientific Model Development, 8, PP. 1339-1356. https://doi.org/10.5194/gmd-8-1339-2015 Moore, J.N., 2016, Recent Desiccation of Western Great Basin Saline Lakes: Lessons from Lake Abert, Oregon, USA, Sci. Total Environ., 554, PP. 142-154. https://doi.org/10.1016/j.scitotenv.2016.02.16 Nabipour, Y., Arian Far, A. & Samadian, M., 2015, Investigating Changes in Vegetation Cover on Groundwater Pollution in Urmia Plain Using RS and GIS, 6th National Conference on Water Resources Management of Iran, Kurdistan, University of Kurdistan. Nagler, P.L., Inoue, Y., Glenn, E.P., Russ, A.L., Daughtry, C.S.T., 2003. Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sens. Environ. 87, 310–325. https://doi.org/10.1016/J.RSE.2003.06.001 Osinowo, A.A., Okogbue, E.C., Eresanya, E.O. & Akande, O.S., 2017, Evaluation of Wind Potential and Its Trends in the MID-ATLANTIC, Modeling Earth Systems and Environment, 3(4), P. 45. https://doi.org/10.1007/s40808-017-0399-4 Pettorelli, N., Weladji, R.B., Holand, Ø, Mysterud, A., Breie, H. & Stenseth, N.Ch., 2005, The Relative Role of Winter and Spring Conditions: Linking Climate and Landscape-Scale Plant Phenology to Alpine Reindeer Body Mass, Biol. Lett., 1(1), PP. 24-6. 10.1098/rsbl.2004.0262 Pourhashemi, S., Broghni, M., Zanganeh Asadi, M.A. & Amir Ahmadi, A., 2014, Analysis of the Relationship between Vegetation Cover and the Occurrence of Dust in Razavi Khorasan Province Using Geographic Information System and Remote Sensing, Remote Sensing and Geographic Information System in Natural Resources, 6(4), PP. 33-45. Raispour, K. & Khosravi, M., 2018, Analysis of Long-Term Behavior of Aerosol Optical Depth (AOD) in Sistan Plain Using MERRA-2 Model, International Conference on Dust in Southwest Asia, Zabul, Zabul University. Rashki, A., Kaskaoutis, D.G., Eriksson, P.G., Qiang, M. & Gupta, P., 2012, Dust Storms and Their Horizontal Dust Loading in the Sistan Region, Iran, Aeolian Res., 5, PP. 51-62. https://doi.org/10.1016/j.aeolia.2011.12.001 Rashki, A., Eriksson, P.G., Rautenbach, C.D.W., Kaskaoutis, D.G., Grote, W. & Dykstra, J., 2013, Assessment of Chemical and Mineralogical Characteristics of Airborne Dust in the Sistan Region, Iran, Chemosphere, 90, PP. 227-236. https://doi.org/10.1016/j.chemosphere.2012.06.059 Real-Rangel, R., Pedrozo-Acuña, A., Breña-Naranjo, J.A. & Alcocer-Yamanaka, V., 2017, Evaluation of the Hydroclimatological Variables Derived from GLDAS-1, GLDAS-2 and MERRA-2 in Mexico, E-Proceedings of the 37th IAHR World Congress, August 13-18, Kuala Lumpur, Malaysia. Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J.T., Liu, E., Bosilovich, M.G. & Woollen, J., 2011, MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications, Journal of Climate, 24, PP. 3624-3648. https://doi.org/10.1175/JCLI-D-11-00015.1 Schwanghart, W. & Schutt, B., 2008, Meteorological causes of Harmattan dust in West Africa, Geomorphology, 95(3-4), PP. 412-428. https://doi.org/10.1016/j.geomorph.2007.07.002 Sofue, Y., Hoshino, B., Demura, Y., Kai, K., Baba, K., Nduati, E., Kondoh, A. & Sternberg, T., 2018, Satellite Monitoring of Vegetation Response to Precipitation and Dust Storm Outbreaks in Gobi Desert Regions, Land, 7(1), P. 19. https://doi.org/10.3390/land7010019 Sohrabi, T.S., Wali, A.A., Ranjbar Ferdavi, A. & Mousavi, S.H., 2017, Quantitative Analysis of Vegetation Feedback on the Occurrence of Dust in Arid Ecosystems (Case Study: Isfahan Province), Rangeland and Watershed, Journal of Natural Resources of Iran, 71(41), PP. 973-985. Strong, C.L., Parsons, K., McTainsh, G.H. & Sheehan, A., 2011, Dust Transporting Wind Systems in the Lower Lake Eyre Basin, Australia: A Preliminary Study, Aeolian Res., 2, PP. 205-214. https://doi.org/10.1016/j.aeolia.2010.11.001 Sweeney, M.R., Zlotnik, V.A., Joeckel, R.M. & Stout, J.E., 2016, Geomorphic and Hydrologic Controls of Dust Emissions during Drought from Yellow Lake Playa, West Texas, USA, J. Arid Environ., 133, PP. 37-46. https://doi.org/10.1016/j.jaridenv.2016.05.007 Tan, M., 2016, Exploring the Relationship between Vegetation and Dust-Storm Intensity (DSI) in China, Journal of Geographical Sciences, 26(4), PP. 387-396. https://doi.org/10.1007/s11442-016-1275-2 Teillet, P.M., Staenz, K. & Willams, D.J., 1997, Effects of Spectral, Spatial, and Radiometric Characteristics on Remote Sensing Vegetation Indices of Forested Regions, Remote Sensing of Environment, 61, PP. 139-149. https://doi.org/10.1016/S0034-4257(96) 00248-9 Thenkabail, P.S., Gamage, M.S.D.N. & Samakhtin, V.U., 2002, Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization, Photogrammetric Engineering and Remote Sensing, 68, PP. 607-621. Tucker, C.J., 1979, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8, PP. 127-150. https://doi.org/10.1016/0034-4257(79)90013-0 Xi, X. & Sokolik, I.N., 2016, Quantifying the Anthropogenic Dust Emission from Agricultural Land Use and Desiccation of the Aral Sea in Central Asia, J. Geophys. Res., 121, PP. 12-270. https://doi.org/10.1002/2016JD025556 Yan, Y., Xu, X., Xin, X., Yang, G., Wang, X., Yan, R. & Chen, B., 2011, Effect of Vegetation Coverage on Aeolian Dust Accumulation in a Semiarid Steppe of Northern China, Catena, 87(3), PP. 351-356. https://doi.org/10.1016/j.catena.2011.07.002 Yasmi, H., 2016, Evaluation of the Accuracy of Oven Temperature Estimation of Global Temperature Databases on Iran, Master's Thesis, Supervisor Mohammad Darend, University of Kurdistan. Zeng, X., Lu, F., Fang, X., Wang, Y. & Guo, L., 1998, A Study of Dust Storm in China Using Satellite Data in Optical Remote Sensing of the Atmosphere and Clouds, SPIE, 3(301), PP. 163-168. 10.1117/12.317741 Zhang, X. & Yan, Guangjian & Li, Q. & Li, Zhao-Liang & Wan, Huawei & Guo, Z.. (2006). Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model. International Journal of Remote Sensing. 27. 5359-5372. 10.1080/01431160600658107. | ||
|
آمار تعداد مشاهده مقاله: 6,462 تعداد دریافت فایل اصل مقاله: 2,828 |
||