بررسی قابلیت شاخص های مبتنی بر داده های ماهواره ای در طبقه بندی اقلیمی به روش دومارتن

محورهای موضوعی : توسعه سیستم های مکانی

آسیه طیبی ¹ , محمد حسین مختاری ^{2 *}

1 - کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم انسانی، دانشگاه یزد
2 - دانشیار دانشکده منابع طبیعی و کویر شناسی/گروه‌ مدیریت مناطق خشک و بیابانی/دانشگاه یزد

تاریخ دریافت : 1401/04/03 تاریخ پذیرش : 1401/05/15 تاریخ انتشار : 1403/07/08

کلید واژه: اقلیم, مودیس, بارش, دومارتن.,

چکیده مقاله :

شناخت ویژگی­های طبیعی هر منطقه به­خصوص شناسایی طبقه­بندی اقلیمی، در امر برنامه‌ریزی و بهره­برداری بهینه از منابع نقش مهمی دارد و تغییرات در آن منجر به افزایش یا کاهش وسعت اقلیمی می­شود. در تعیین اقلیم می­توان از داده­هایی مانند دما و بارش برای ناحیه اقلیم­ها استفاده کرد؛ اما به دلیل اینکه برخی مناطق، فاقد ایستگاه سینوپتیک زمینی هستند و همچنین به علت بعد مسافت و هزینه، اقدام به استفاده از داده­های ماهواره­ای می­شود که در هر زمان، مکان و بدون هزینه دریافت خواهند شد. هدف از این پژوهش بررسی طبقه­بندی اقلیمی ایران با داده­های ماهواره­ای است که در تطابق با اقلیم نمای دومارتن و در طی سال‌های 1395 تا 1398 بررسی شد. در اين بررسي از دو محصول ماهواره­ای سنجنده مودیس شامل تصاوير شاخص تفاضلي نرمال شده گیاهی و دماي سطح زمين  و از تصاوير بارش جهاني حاصل از الگوریتم IMERG استفاده‌شده است. از ترکیب ­دو شاخص ذکرشده، شاخص دما_ پوشش گیاهی محاسبه شد. پارامترهای مورداستفاده در اقلیم نمای دومارتن شامل متوسط دما و بارش سالانه است که با توجه به ارتباط نزدیکی که با داد­ه­های ماهواره­ای مورداستفاده نشان دادند، به‌عنوان ورودی­های دومارتن ماهواره­ای به کار گرفته شدند. در بررسی همبستگی پیرسون میان متوسط پارامترها، مقادیر بارش ایستگاهی  با داده­های ماهواره ای و دمای ایستگاهی با دمای سطحی به ترتیب 52/0 و 89/0 قرار داشتند. نقشه پهنه­بندی حاصل از داده های زمینی نیز در مقایسه با داده های ماهواره ای حداکثر کاپا = 90/0 را نشان داد؛ که بیشترین سطح اقلیمی در هردو طبقه­بندی متعلق به اقلیم خشک در حدود 70% و کمترین سطح، اقلیم مدیترانه­ای با کمتر از 5% است.

چکیده انگلیسی:

Knowing the natural characteristics of each region, especially identifying the climate classification, plays an important role in planning and optimal use of resources. In determining the climate, data such as temperature and precipitation can be used for the zoning of climates; However, due to the fact that some areas do not have terrestrial synoptic stations, satellite data is used, which will be received at any time, place and without cost. The purpose of this research is to investigate the climate classification of Iran with satellite data, which was checked in accordance with the climate of De martonne and during the years 2016 to 2019, and in it, the MODIS sensor satellite products, LST indices, NDVI and GPM images from TRMM satellite were used. The TVDI index was calculated from the combination of the two mentioned indices. The parameters used in the De martonne climate map include the average temperature and annual precipitation, which were used as the inputs of the satellite De martonne due to the close relationship they showed with the satellite data used. In the Pearson correlation analysis between average parameters, precipitation with GPM and TVDI were in correlation of 0.52 and 0.56, respectively, and meteorological temperature and LST were in correlation of 0.90. The results of these data stated that between the De martonne method of weather station data and the De martonne relationship obtained from satellite data, the minimum correlation was rp=0.55 and the maximum rp=0.89 and the resulting zoning map From the maximum correlation, it showed Kappa=0.90; The highest climate level in both classifications belongs to the dry climate at about 70% and the lowest level is the Mediterranean climate with less than 5%

منابع و مأخذ:

1. Aisyah A, Kusratmoko E, Wibowo A.2019.The Spread Of Dry Area Based On TVDI Index (Temperature Vegetation Dryness Index). In IOP Conference Series: Earth and Environmental Science (Vol. 311, No. 1, p. 012006). IOP Publishing , pp 1-6. Doi:10.1088/1755-1315/311/1/012006.
2. Bai Y, Yang Y, Jiang H.2019. Intercomparison of AVHRR GIMMS3g, terra MODIS, and SPOT-VGT NDVI products over the Mongolian plateau. Remote Sensing, 11(17), pp 2-30. https://doi.org/10.3390/rs11172030.
3. Chapman S, Watson J.E, Salazar A, Thatcher M, McAlpine C.A.2017.The impact of urbanization and climate change on urban temperatures: a systematic review. Landscape ecology,32(10), pp 1921-1935. https://doi.org/10.1007/s10980-017-0561-4.
4. Cheng S, Huang J.2016. Enhanced soil moisture drying in transitional regions under a warming climate, Journal of Geophysical Research: Atmospheres,121(6),2542-2555. https://doi.org/10.1002/2015JD024559.
5. Carlson TN, Capehart WJ, Gillies RR.1995. A new look at the simplified method for remote sensing of daily evapotranspiration, Remote Sensing of Environment, 54(2), pp 161-167. https://doi.org/10.1016/0034-4257(95)00139-R.
6. De Raadt A, Warrens MJ, Bosker RJ, Kiers HA.2019. Kappa coefficients for missing data, Educational and psychological measurement, 79(3), pp 558-576. https://doi.org/10.1177/0013164418823249.
7. Dai X.G, Wang P.2017.A new classification of large-scale climate regimes around the Tibetan Plateau based on seasonal circulation patterns. Advances in Climate Change Research, 8(1), pp 26-36. https://doi.org/10.1016/j.accre.2017.01.001.
8. Emamian A, Rashki A, Kaskaoutis D.G, Gholami A, Opp C,Middleton N.2021.Assessing vegetation restoration potential under different land uses and climatic classes in northeast Iran. Ecological Indicators, 122, 107325. https://doi.org/10.1016/j.ecolind.2020.107325.
9. Gholami V, Ahmadi Jolandan M, Torkaman J.2017. Evaluation of climate change in northern Iran during the last four centuries by using dendroclimatology. Natural Hazards, 85(3), pp 1835-1850. DOI: https://doi.org/10.6092/issn.2281-4485/8202.
10. Haghighi Khomami M, Tajaddod M.J, Ravanbakhsh M, Jamalzad Fallah F.2021. Vegetation classification based on wetland index using object based classification of satellite images (Case study: Anzali wetland), RS & GIS for Natural Resources (Vol. 12/ Issue 3), pp 1-17. http://dorl.net/dor/20.1001.1.26767082.1400.12.3.1.6. (In Persian).
11. Hosseini-Moghari S. M, Tang Q.2020.Validation of GPM IMERG V05 and V06 precipitation products over Iran. Journal of Hydrometeorology, 21(5), pp 1011-1037. DOI: https://doi.org/10.1175/JHM-D-19-0269.1.
12. Jebli I, Belouadha F.Z, Kabbaj M.I, Tilioua A.2021. Prediction of solar energy guided by pearson correlation using machine learning. Energy, 224, 120109, pp 1-20. https://doi.org/10.1016/j.energy.2021.120109.
13. Kwon Y. J, Ryu S, Cho J, Lee Y.W, ParkN. W, Chung C.Y, Hong S.2020. Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data. Asia-Pacific Journal of Atmospheric Sciences, 56(2), pp 275-289. Doi: 10.1007/s13143-020-00174-6.
14. Lu, C, Ye J, Fang G, Huang X, Yan M.2021. Assessment of GPM IMERG satellite precipitation estimation under complex climatic and topographic conditions. Atmosphere, 12(6). https://doi.org/10.3390/atmos12060780.
15. Liang, L, ZHAO SH,QIN ZHHEKX, Chong CH EN,LUO YX, ZHOU XD.2014. Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001 to 2010, Journal of Integrative Agriculture,13(7),1501-1508. https://doi.org/10.1016/S2095-3119(14)60813-3.
16. Maghfiroh A.M.R, Zainudin Z, Chandra A. B, Sulistyorini A.2021.Analysis of the effect of noise on blacksmith performance using linear regression. Gravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika, 7(1), Vol. 7, No. 1, Page 55-62. DOI:10.30870/gravity.v7i1.9453.
17. Mijani N, Hamzeh S, Karimi Firozjaei M.2019. Quantifying the effect of surface parameters and climatic conditions on land surface temperature using reflective and thermal remote sensing data, RS & GIS for Natural Resources (Vol. 10/ Issue 1), pp 36-59. https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=710367. (In Persian).
18. Mazidi A, Enayatpour M, Hosseini SS.2021. Determining the climate of Kerman province using ambrothermic curve methods, Dumartin drought coefficient, Amberjeh facade climate. Journal of Geography and Human Relations, 14 (4), pp 35-43. https://doi.10.22034/GAHR.2021.287987.1565. (In Persian).
19. MazzoglioP, Laio F, Balbo S, Boccardo P, Disabato F.2019. Improving an extreme rainfall detection system with GPM IMERG data, Remote Sensing, 11(6), 677. https://doi.org/10.3390/rs11060677.
20. Madani K, AghaKouchak A, Mirchi A.2016. Iran’s socio-economic drought: challenges of a water-bankrupt nation, Iranian Studies, 49(6), 997-1016. https://doi.org/10.1080/00210862.2016.1259286.
21. Mokhtari M, Ahmad B, Hoveidi H, Busu I.2013. Sensitivity analysis of metric–based evapotranspiration algorithm. international journal of environmental research (ijer), 7(2), pp 407-422. https://www.sid.ir/en/journal/viewpaper.aspx?id=368836.
22. Nagelkerke N.J.D.1991.A note on a general definition of the coefficient of determination.Biometrika, 78(3), pp 691-692. https://doi.org/10.1093/biomet/78.3.691.
23. Olafsson H, Rousta I.2021. Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing. European Journal of Remote Sensing, 54(1), pp 351-363. https://doi.org/10.1080/22797254.2021.1931462.
24. Piri I, Khanamani A, Shojaei S, Fathizad H.2017. Determination of the best geostatistical method for climatic zoning in Iran. Applied Ecology and Environmental Research, 15(1), pp 93-103. DOI: http://dx.doi.org/10.15666/aeer/1501_093103.
25. Patel NR, Anapashsha R, Kumar S, Saha SK, Dadhwal VK.2009. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status, International Journal of Remote Sensing, 30(1), pp 23-39. https://doi.org/10.1080/01431160802108497.
26. Shi Y, Zhang Y.2018.Remote sensing retrieval of urban land surface temperature in hot-humid region. Urban climate, 24, pp 299-310. https://doi.org/10.1016/j.uclim.2017.01.001.
27. Willmott CJ, Matsuura K.2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), pp 79-82.https://doi .10.3354/cr030079.
28. Zare khormizi H, Ghafarian Malamiri H.R.2020. Investigation of phenological components changes of Iranian vegetation in response to climate change using NDVI products of AVHRR sensor from 1982 to 2018, RS & GIS for Natural Resources (Vol. 11/ Issue 4) , pp 87-112. https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=819418. (In Persian).
29. Zhang X, Zhao H, Yang J.2019.Spatial downscaling of land surface temperature in combination with TVDI and elevation. International Journal of Remote Sensing,40(5-6), PP 1875-1886.https://doi.org/10.1080/01431161.2018.1489164.
30. Zhang D, Tang R, Zhao W, Tang B, Wu H, Shao K, Li ZL.2014. Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature, Remote Sensing, 6(4), pp 3170-3187. https://doi.org/10.3390/rs6043170.