Volume 10, Issue 2 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(2): 131-148 |
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feyzolahpour M. Estimating land surface temperature (LST) and comparing it with NDMI, NDWI and NDVI indices in order to investigate water stress with an emphasis on land use changes (LULC) in the support vector machine (SVM) system (study area: Anzali wetland). Journal of Spatial Analysis Environmental Hazards 2023; 10 (2) :131-148
URL: http://jsaeh.khu.ac.ir/article-1-3405-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3405-en.html
Assistant Professor of Geomorphology, Zanjan University, Zanjan, Iran , feyzolahpour@znu.ac.ir
Abstract: (1879 Views)
Earth's surface temperature is considered an important parameter in biosphere, ice globe and climate change studies. In this research, LST, NDVI, NDMI and NDWI values were calculated for the Anzali wetland area using the OLI and TIRS measurements of the Landsat 8 satellite. Investigations showed that the minimum LST temperature for the years 2013, 2018 and 2023 was equal to 13.94, 22.36 and 14.6, respectively, and its maximum values for these years were equal to 35.7, 40.58 and 31.6. 31.6 degrees Celsius is estimated respectively. Vegetation status, access to water resources and water stress for the study area were estimated with NDVI, NDWI and NDMI indices. Bands 3, 4, 5, 6 and 10 of Landsat 8 satellite were used to estimate these indicators. The obtained values were compared with LST values. The distribution charts show that the highest negative correlation between LST and NDMI is established at the rate of -0.65 and the highest positive correlation between the NDWI and LST indices is established at the rate of 0.23. In general, the investigations have shown that there is a negative correlation between the NDMI and NDVI indices with the LST index. The Support Vector Machine (SVM) method was also used to investigate land use changes (LULC). The results showed that in the studied area, which has an area of 686.81 square kilometers, agricultural lands have faced significant expansion and reached 487.7 square kilometers from 329 square kilometers in 2013. In the meantime, forest areas have faced a sharp decrease and have decreased from 34.8 square kilometers to 1.73 square kilometers.
Type of Study: Research |
Subject:
Special
Received: 2023/12/2 | Accepted: 2023/09/1 | Published: 2023/09/1
Received: 2023/12/2 | Accepted: 2023/09/1 | Published: 2023/09/1
References
1. جمالی، زهرا؛ مجید اونق و عبدالرسول سلمان ماهینی. 1398. تحلیل ارتباط دمای سطح زمین با کاربری اراضی و شاخص اختلاف گیاهی نرمال شده در دشت گرگان. برنامه ریزی و آمایش فضا، 3: 194- 175.
2. حسینی چمنی، فرشید؛ احمد فرخیان و هادی عامری خواه. 1398. تابع انتقالی به منظور برآورد رطوبت خاک به کمک شاخص های پوشش گیاهی، دمای سطح خاک و شاخص نرمال شده رطوبت. نشریه پژوهش های حفاظت آب و خاک، 4:254- 239.
3. عابدینی، موسی؛ احسان قلعه و نازفر آقازاده. 1401. پایش دمای سطح زمین و بررسی رابطه کاربری اراضی با دمای سطح با استفاده از تصاویر سنجنده OLI و TM مطالعه موردی شهرستان مشگین شهر. نشریه تحقیقات کاربردی علوم جغرافیایی، 67: 393- 375.
4. علیمرادی، سامان؛ اسداله خورانی و یحیی اسمعیل پور. 1396. پویایی پوشش گیاهی در رابطه با دما و بارش در مراتع حوضه کارون محدوده استان خوزستان. نشریه تحقیقات کاربردی علوم جغرافیایی، 44: 177- 155.
5. فیضی زاده، بختیار؛ خلیل دیده بان و خلیل غلام نیا. 1395. برآورد دمای سطح زمین با استفاده از تصاویر ماهواره لندست 8 و الگوریتم پنجره مجزا مطالعه موردی حوضه آبریز مهاباد. فصلنامه اصلاعات جغرافیایی، 98: 181- 171.
6. نیلیه بروجنی، مرضیه و مژگان احمدی. 1398. بررسی رابطه پوشش گیاهی شهری و درجه حرارت سطح زمین با استفاده از تصویرهای ماهواره لندست TM و OLI و سنجه LST در شهر اصفهان. فصلنامه علوم محیطی، 4: 178- 163.
7. Ahmed, B.; M. Kamruzzaman, X. Zhu, M. Rahman, and K. Choi. 2013. Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Rem Sens, 5: 5969–5998.
8. Alam, A.; M. Bhat, B. Kotlia, B. Ahmad, S. Ahmad, A. Taloor, and H. Ahmad. 2018. Hybrid tectonic character of the Kashmir basin: response to comment on “Coexistent pre-existing extensional and subsequent compressional tectonic deformation in the Kashmir basin, NW Himalaya (Alam et al., 2017)” by Shah (2017). Quat. Int, 468: 284–289.
9. Alexander, C. 2020. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Obs. Geoinf, 86: 102013.
10. Arnfield, A.J. 2003. Two decades of urban climate research: a review of turbulence, exchanges f energy and water, and the urban heat island. International Journal of Climate, 23: 1–26.
11. Barsi, J.A.; J. Schott, S. Hook, N. Raqueno, B. Markham, and R. Radocinski. 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Rem. Sens, 6: 11607–11626.
12. Bastiaanssen, W.G.M.; M. Menenti, R. Feddes, and A. Holtslag. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol, 212: 198–212.
13. Becker, F. 1987. The impact of spectral emissivity on the measurement of land surface temperature from a satellite. Int. J. Rem. Sens, 8: 1509–1522.
14. Chen, X.L.; H. Zhao, P. Li, and Z. Yi. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ, 2: 133–146.
15. Drury, S.A. 1987. Image Interpretation in Geology. Allen and Unwin Publisher Ltd U.K.
16. Gao, B.C. 1996. Ndwi - a normalized difference water index for remote sensing of vegetation liquid water from space. Rem. Sens. Environ, 58: 257–266.
17. Guha, S.; H. Govil, and M. Besoya. 2020. An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data. Geomatics, Nat. Hazards Risk ,11: 1319–1345.
18. Guo, G.; Z. Wu, R. Xiao, Y. Chen, X. Liu, and X. Zhang. 2015. Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc. Urban Plann. 135: 1–10.
19. Haque, S.; S. Kannaujiya, A. Taloor, D. Keshri, R. Bhunia, P. Ray, and P. Chauhan. 2020. Identification of Groundwater Resource Zone in the Active Tectonic Region of Himalaya through Earth Observatory Techniques. Groundwater for Sustainable Development, p. 108.
20. Joshi, J.P. and B. Bhatt. 2012. Estimating temporal land surface temperature using remote sensing: a study of Vadodara urban area, Gujarat. International Journal of Geology, Earth and Environmental Sciences, 2: 123–130.
21. Kalma, J.D.; T. McVicar, and M. McCabe. 2008. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv. Geophys, 29: 421–469.
22. Khan, A.; H. Govil, A. Taloor, and G. Kumar. 2020. Identification of Artificial Groundwater Recharge Sites in Parts of Yamuna River Basin India Based on Remote Sensing and Geographical Information System. Groundwater for Sustainable Development, p. 100415.
23. Kogan, F.N. 2001. Operational space technology for global vegetation assessment. Bull. Am. Meteorol. Soc, 9: 1949–1964.
24. Kothyari, G.C.; N. Joshi, A. Taloor, R. Kandregula, B. Kotlia, C. Pant, and R. Singh. 2019. Landscape evolution and deduction of surface deformation in the Soan Dun, NW Himalaya, India. Quat. Int, 507: 302–323.
25. Kour, R.; N. Pate, and A. Krishna. 2016. Influence of shadow on the thermal and optical snow indices and their interrelationship. Rem. Sens. Environ, 187: 119–129.
26. Kriegler, F. 1969. Preprocessing transformations and their effects on multispectral recognition. In: Proceedings of the Sixth International Symposium on Remote Sensing of Environment, 16: 97–131.
27. Lejeune, Q.; E. Davin, B. Guillod, and S. Seneviratne. 2015. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dynam, 44: 2769–2786.
28. Liang, X.Z.; M. Xu, X. Yuan, T. Ling, H. Choi, F. Zhang, L. Chen, S. Liu, S. Su, F. Qiao, and Y. He. 2012. Regional climate–weather research and forecasting model. Bull. Am. Meteorol. Soc, 93:1363–1387.
29. Malik, M.S, and J. Shukla. 2018. Retrieving of land surface temperature using thermal remote sensing and GIS techniques in Kandaihimmat watershed, Hoshangabad, Madhya Pradesh. J. Geol. Soc. India, 92: 298–304.
30. Mannstein, H. 1987. Surface energy budget, surface temperature and thermal inertia. In: Remote Sensing Applications in Meteorology and Climatology, 24: 391–410.
31. McFeetemrs, S.K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Rem. Sens, 17: 1425–1432.
32. Owen, T.W.; T. Carlson, and R. Gillies. 1998. Remotely sensed surface parameters governing urban climate change. Int. J. Rem. Sens, 19: 1663–1681.
33. Prata, A.J.; V. Caselles, C. Coll, J. Sobrino, and C. Ottle. 1995. Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Rem. Sens. Rev, 12: 175–224.
34. Reddy, S.N.; B. Manikiam. 2017. Land surface temperature retrieval from LANDSAT data using emissivity estimation. Int. J. Appl. Eng. Res, 12: 9679–9687.
35. Sarkar, A.; V. Kumar, A. Jasrotia, A. Taloor, R. Kumar, R. Sharma, V. Khajuria, G. Raina, B. Kouser, and S. Roy. 2020. Spatial analysis and mapping of malaria risk in dehradun city India: a geospatial technology-based decision-making tool for planning and management. In: Geoecology of Landscape Dynamics, 12: 207–221.
36. Sahana, M.; S. Dutta, and H. Sajjad. 2019. Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. Int. J. Unity Sci, 23: 205–225.
37. Sellers, P.J.; F. Hall, G. Asrar, D. Strebel, and R. Murphy. 1988. The first ISLSCP field experiment (FIFE). Bull. Am. Meteorol. Soc, 69: 22–27.
38. Sekertekin, A, and S. Bonafoni. 2020. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Rem. Sens, 12: 294-312.
39. Singh, A.K.; A. Jasrotia, A. Taloor, B. Kotlia, V. Kumar, S. Roy, P. Ray, K. Singh, A. Singh, and A. Sharma. 2017. Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology. Quat. Int, 444: 191–200.
40. Sobrino, J.A, and N. Raissouni. 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. Int. J. Rem. Sens, 21: 353–366.
41. Taloor, A.K.; B. Kotlia, A. Jasrotia, A. Kumar, A. Alam, S. Ali, B. Kouser, P. Garg, R. Kumar, A. Singh, and B. Singh. 2019. Tectono-climatic influence on landscape changes in the glaciated Durung Drung basin, Zanskar Himalaya, India: a geospatial approach. Quat. Int, 507: 262–273.
42. Tomlinson, C.J.; L. Chapman, J. Thornes, and C. Baker. 2011. Remote sensing land surface temperature for meteorology and climatology: a review. Meteorol. Appl, 18: 296–306.
43. Voogt, J.A, and T. Oke. 2003. Thermal remote sensing of urban climates. Rem. Sens. Environ, 86: 370–384.
44. Wen, L.J. 2017. An analysis of land surface temperature (LST) and its influencing factors in summer in western Sichuan Plateau: a case study of Xichang City. Remote Sensing for Land and Resources 29: 207–214.
45. Weng, Q.; D. Lu, and J. Schubring. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Rem. Sens. Environ. 89:467–483.
46. Weng, Q. 2009. Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS J. Photogrammetry Remote Sens, 64: 335–344.
47. Yan, Y.; K. Mao, J. Shi, S. Piao, X. Shen, J. Dozier, Y. Liu, H. Ren and Q. Bao. 2020. Driving forces of land surface temperature anomalous changes in North America in 2002– 2018. Sci. Rep, 10: 1–13.
48. Yao, R.; L. Wang, X. Huang, W. Zhang, J. Li and Z. Niu. 2018. Interannual variations in surface urban heat island intensity and associated drivers in China. J. Environ. Manag, 222: 86–94.
49. Yuan, X.; W. Wang, J. Cui, F. Meng, A. Kurban and P. De Maeyer. 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Sci. Rep. 7: 32-87.
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