Evaluation of Earth Surface Temperature and its Relationship with Spectral Indices Case study: Khuzestan province

Document Type : Research Paper

Authors

1 Associate Professor of Remote Sensing and GIS, University of Isfahan, Isfahan, Iran

2 Ph. D Student of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

In order to achieve this goal, the OLI sensor images of Landsat-8 satellite were used in July 2022. After calculating the land surface temperature (LST) using the split window method, three indices of vegetation cover, the index of built-up areas or impervious surfaces and the index of barren lands were calculated and using Pearson's correlation at the confidence level of 0.01 were investigated with the temperature of the earth's surface, the results of the correlation of temperature with the mentioned indices showed that the barren areas index (NDBaI) has a positive correlation with the temperature of the earth's surface and its R2 value is equal to 0.488. The built-up area index (NDBI) has a negative correlation with the temperature of the earth's surface, and its R2 value is equal to -0.642, and the vegetation cover index has a non-linear positive correlation with the temperature of the earth's surface, the non-linear reason The existence of vegetation cover is scattered and limited in different parts of Khuzestan province. The significance and effectiveness of the three mentioned indicators were investigated using the least square regression method. The results of the investigation of heat islands in Khuzestan province using the local Moran index showed that the heat islands are concentrated outside the urban area and in barren areas, and the temperature of urban areas is lower than the areas outside the city. And cool islands are concentrated on water areas and vegetation and have a much smaller area than thermal islands in Khuzestan province. 

Keywords

References

Asghari Saraskanroud, Sayad, Hadi Emami (2018). Earth surface temperature monitoring and investigation of land use relationship with a case study: Ardabil city, ETM and OLI + surface temperature publication using sensor images, applied research of geographical sciences, 215-159. (in Persian)
https://sid.ir/paper/102206/fa
Azimand, K., H. agate d. Ashurlo and A. Shahi Aghbalaghi (2019). Analyzing the effects of the physical and morphological characteristics of the constructed lands on the changes in the surface temperature of the land in semi-arid regions. Environmental Science Quarterly. Volume 18, No. 2, 1-18. (in Persian)
https://envs.sbu.ac.ir/article_98133.html
Alexander C (2020). Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). International Journal of Applied Earth Observation and Geoinformation, 86, 102013.
https://doi.org/10.1016/j.jag.2019.102013
Aliabadi, K. and A. Dadashi-roudbari (2019). Investigation of changes in spatial autocorrelation patterns of Iran's maximum temperature. Geographical Studies of Arid Areas, 6 (21): 86-104. (In Persian).
http://journals.hsu.ac.ir jarhs article-1-718-en
Alibakhshi Z, Ahmadi M& Farajzadeh ASL M (2020). Modeling Biophysical Variables and Land Surface Temperature Using the GWR Model: Case Study-Tehran and Its Satellite Cities. Journal of Indian Society of Remote Sensing,48, 59-70.
Adegoke, C. and A. Sojobi (2015). "Climate change impact on infrastructure in Osogbo metropolis, south-west Nigeria." Journal of Emerging Trends in Engineering and AppliedSciences.167-156, (3) 6.
https://doi.org/10.22059/jne.2018.253756.1491
Akbari, H., Menon, S and Rosenfeld, A (2007). Global cooling: effect of urban albedo on global temperature algorithms for estimating sea and land surface temperature with ATSR data. Int J Remote Sens.17. 2114-2089, (11).
https://heatisland.lbl.gov/publications/global-cooling-effect-urban-albedo
Alhawiti، RH, Mitsova) 2016). using Landsat-8 data to investigate the correlation between urban heat island and urban land uses. International Journal of Research in Engineering and Technology, 5,457-466.
https://doi.org/10.15623/ijret.2016.050 .
Azhdari, A., Soltani, A. and Alidadi, M (2018). Urban morphology and landscape structure effect on land surface temperature: evidence from Shiraz, a semi-arid city. Sustainable Cities and Society. 41, 853-864.
https://doi.org/10.1016/j.scs.2018.06.034
Balew A, Korme T (2020). Monitoring land surfacetemperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Science.
https://doi.org/10.1016/j.ejrs.2020.02.001.
Bari, E, Nipa. N.J & Roy B (2021). Association of vegetation indices with atmospheric & biological factors using MODIS time series products, Environ. Challeng., 5 (2021), 100376.
https://doi.org/10.1016/j.envc.2021.100376
Chen, X.L. Zhao, H.M. Li, P.X. & Yin, Z.Y (2006) .Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote Sensing of Environment.146-133,104.
http://dx.doi.org/10.13005/bbra/3111
Darvishi, Shadman; Karim Soleimani, Mustafa Rashidpour (2018). The effect of vegetation indices and urban characteristics on changes in ground surface temperature, a case study (Sanandaj city). Remote Sensing and Geographical Information System in Natural Resources (10th year, first issue). (in Persian)
https://www.sid.ir/paper/189453/fa
Deilami K. Kamruzzaman M (2017). Modelling the urban heat island effect of smart growth policy scenarios in Brisbane, Land Use Pol., 64 (2017),  38-55.
Deng, C. & Wu, C (2013). Examining the Impacts of Urban Biophysical Compositions on Surface Urban Heat Island: A Spectral Unmixing and Thermal Mixing Approach. Remote Sensing of Environment, 131, 262-274.
https://doi.org/10.1016/j.rse.2012.12.020.
Emami& emami (2018). Investigation of Urban Biophysical Compounds in the Formation of Thermal Islands Using RS and GIS (Case Study: Yazd (2018). Journal of Radar and Optic Remote Sensing” (JRORS), 2018.04.13. Environment, Vol.127, 210-222.
https://jrors.yazd.iau.ir/article_542418
Enteziri, Alireza, Zandi, Rahman, Khosrovian, Maryam (2018). Evaluation of spatial changes of vegetation and surface temperature using Landsat and MADIS images, case study: Fars province 1986-2017. Watershed engineering and management. Volume 11, Number 4. 940-929. (in Persian)
https://jwem.areeo.ac.ir/article_120254.html
Ezimand, K., Kakroodi, A. A and Kiavarz, M (2019). The development of spectral indices for detecting builtup from LANDSAT TM:5.Remote Sensing of Environment.90.440-434,(4).
https://doi.org/10.1080/01431161.2018.1488282
Feyisa G.L. Meilby H, Darrel G. Jenerette, S (2016). Pauliet, locally optimized separability enhancement indices for urban land cover mapping: exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia, Remote Sens. Environ, 175, 14-31.
Gohain, K.J. P. Mohammad & Goswami A (2021). Assessing the impact of land use land cover changes on land surface temperature over Pune city, India, Quat. Int., 575-576 (2021), 259-269.
https://doi.org/10.1016/j.quaint.2020.04.052.
Guanglei H. Hongyan Z, Yeqiao W, Zhihe Q, Zhengxiang Z (2010). Retrieval and spatial distribution of land surface temperature in the middle part of jilin province based on MODIS data, Sci. Geogr. Sin., 30 (3) (2010), 421-427. Accessed: Aug. 16, 2021.
https://doi.org/10.1016/j.heliyon.2022.e10668
Guha, S. Govil. H (2021). An assessment on the relationship between land surface temperature and normalized difference vegetation index Environ. Dev. Sustain., 23 (2) (2021), 1944-1963.
https://doi.org/10.1007/s10668-020-00657-6
Guha S. Govil H, Dey A Gill N (2017). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy, Eur. J. Remote Sens., 51 (1) (2018),  667-678.
https://doi.org/10.1080/22797254.2018.1474494
Guha S. Govil H, Mukherjee S (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India, J. Appl. Remote Sens., 11 (3) (2017), 1. 
https://doi.org/10.1117/1.JRS.11.036020
Guo G, Wu Z, Xiao R, Chen Y, Liu X& Zhang X (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1-10.
https://doi.org/10.1016/j.landurbplan.2014.11.007
Hashemi Dere Badami, Siros; Ali Darvishi Balorani; Kazem Alavipanah; Mohammad Maleki; Reza Bayat (2018). Analysis of heat island changes of urban surfaces during the day and night using Madis multi-time sensor products. (in Persian)
https://www.sid.ir/paper/102377/fa
Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote sensing of environment,114(3): 504-513.
https://doi.org/10.1016/j.rse.2009.10.008.
Imran, ESE (2012). Detection of land use and surface temperature change in different resolutions. Geographic Information System Journal, 4,189-203.
https://doi.10.4236/jgis.2012.43024.
Jain S, Sannigrahi S, Sen S, Bhatt S, Chakraborti S& Rahmat S (2020). Urban heat island intensity and its mitigation strategies in the fast-growing urban area. Journal of Urban Management, 9(1), 54-66.
https://doi.org/10.1016/j.jum.2019.09.004.
Jiménez-Muñoz JC, Sobrino JA (2008). Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors. IEEE Geosci Remote Sens Lett 5: 806-809.
https://doi.org/10.1109/LGRS.2008.2001636.
Lazzarini, M., Marpu, P.R. and Ghedira, H (2013). Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment. 130, 136-152.
https://doi.org/10.1016/j.rse.2012.11.007
Lee PS-H, Park J (2020). An Effect of Urban Forest on Urban Thermal Environment in Seoul, South Korea, Based on Landsat Imagery Analysis.Forests, 11(6): 630.
https://doi.org/10.3390/f11060630.
Lei Wang, Yao Lu and Yunlong Yao (2019). Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images, 19 November 2019.
https://doi.org/10.3390/s19225049.
Lillesand, T., Kiefer, R. W., & Chipman, J (2015). Remote sensing and image interpretation. John Wiley & Sons.
ISBN: 978-1-118-34328-9.
Nie Q. Man W, Li Z, Y (2016). Huang, Spatiotemporal impact of urban impervious surface on land surface temperature in Shanghai, China,Can. J. Rem. Sens., 42 (6) (2016),  680-689.
https://doi.org/10.1080/07038992.2016.1217484
Pearsall H (2017). Staying cool in the compact city: vacant land and urban heating in Philadelphia, Pennsylvania,Appl. Geogr., 79 (2017), 84-92.
https://doi.org/10.1016/j.apgeog.2016.12.010
Police station, Khalil (2012). Data processing and analysis in socio-economic research, Tehran, Farhang Saba Publications, fifth edition. (in Persian)
https://www.gisoom.com/book/11157179
Rasul, A., Balzter, H., Smith, C., Remedios, J., Adamu, B., Sobrino, J., Srivanit, M. and Weng, Q (2017). A reviewon remote sensing of urban heat and cool islands. Land. 6, 1-10.
https://doi.org/10.3390/land6020038
Rodriguez Lopez J.M, Heider K, Scheffran J (2017). Frontiers of urbanization: identifying and explaining urbanization hot spots in the south of Mexico City using human and remote sensing,Appl. Geogr., 79 (2017), 1-10.
https://doi.org/10.1016/j.apgeog.2016.12.001
Rangzan, Kazem; Saeed Maleki, Ayub Taghizadeh; Peyman Heydarian (2012). Urban spatial development modeling using geographic information systems technology and spatially weighted regression (GWR): a case study of Tehran metropolis, GIS remote sensing master's thesis, Faculty of Earth Sciences. (in Persian)
https://elmnet.ir/doc/10651141-42011
Rongali G, Keshari AK, Gosain AK, Khosa R (2018). A mono-window algorithm for land surface temperature estimation from Landsat 8, thermal infrared sensor data: a case study of the Beas River Basin, India. Pertanika J Sci Technol 26: 829-840.
https://www.researchgate.net/publication/324982810.
Rongali, Gopinadh & Keshari, Ashok K. & Gosain, Ashwani K. & Rakesh Khosa (2018). Split-Window Algorithm for Retrieval of Land SurfaceTemperature Using Landsat 8 Thermal Infrared Data, Journal of Geovisualization and Spatial Analysis (2018). 2: 14.
https://doi.org/10.1007/s41651-018-0021.
Roy B. Bari E., N.J. Nipa, S.A (2021).Ani, Comparison of temporal changes in urban settlements and land surface temperature in Rangpur and Gazipur Sadar, Bangladesh after the establishment of city corporation, Remote Sens. Appl. Soc. Environ., 23 (2021),  100587. 
https://doi.org/10.1016/j.rsase.2021.100587
Sharma, A. Conry, P. Fernando, H. Hamlet, A.F. Hellmann, J. & Chen, F (2016). Green and cool roofs to mitigate urban heat island effects in the Chicago metropolitan area: Evaluation with a regional climate model. Environmental Research Letters, (6)11064004(6).
https://doi 10.1088/1748-9326/11/6/064004
Sobrino JA, El Kharraz J, Li ZL (2003). Surface temperature and water vapor retrieval from MOODIS data. Int J Remote Sens 24:5161–5182.
 https://doi.org/10.1080/0143116031000102502
Sobrino JA, Li Z, Stoll MP, Becker F (1996). Multi-channel and multiangle algorithms for estimating sea and land surface temperature with ATSR data. Int J Remote Sens 17:2089–2114.
https://doi.org/10.1080/01431169608948760
Sobrino JA, Romaguera M (2008). Land surface temperature retrieval from MSG1-SEVIRI data. Remote Sens Environ 92:247–254.
https://doi.org/10.1016/j.rse.2004.06.009
Kashki, A., Karami, M., Zandi, R., & Roki, Z (2021). Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran. Urban Climate, 37, 100832.‏
Wan Mohd Jaafar WS, Abdul Maulud KN, Muhmad Kamarulzaman AM, Raihan A, Md Sah S, Ahmad A, Saad SNM, Mohd Azmi AT, Jusoh Syukri NKA, Razzaq Khan W (2020). The Influence of Deforestation on Land Surface Temperature-A Case Study of Perak and Kedah, Malaysia. Forests, 11(6): 670.
https://doi.org/10.3390/f11060670.
Weng, Q., Liu, H. and Lu, D (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban Ecosystems. 10, 203-219.
https://doi.org 10.1007/s11252-007-0020-0.
Weng, Q., Lu, D.& Schubring, J (2006). Estimation of Land Surface Temperature Vegetation Abundance Relationship for Urban Heat Island Studies, Remote Sensing of Environment, 89(4), 467-483.
https://doi.org10.1016/j.rse.2003.11.005.
Xu, S., Ehlers, M (2017). Hyperspectral image sharpening based on Ehlers fusion.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7: 941-947.
https://doi.org/10.5194/isprs-archives-XLII-2-W7-941-2017.
Xu, H., Yunhao, C., Shangming, D., & Wenxia, Q (2011). Dynamical Monitoring and Evaluation Methods to Urban Heat Island Effects Based on RS & GIS. Procedia Environmental Sciences.Volume 10, 1228-1237.
https://doi.org/10.1016/j.proenv.2011.09.197.
Zareie S, Khosravi H, Nasiri A, Dastorani M (2016). Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran. Solid Earth, 7(6): 1551.:
https://doi.org/10.5194/se-7-1551-2016.
Zhang, C., Luo, L., Xu, W., & Ledwith, V (2008). Use of local Moran's I and GIS toidentify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of theTotal Environment, 398(1), 212-221
https://doi.org/10.1016/j.scitotenv.2008.03.011.
Zhang, Y. Yiyun, C. Qing, D. & Jiang, P (2012). Study on Urban Heat Island Effect Based on Normalized Difference Vegetated Index: A Case Study of Wuhan City, the 18th Biennial Conference of International Society for Ecological Modeling, Procedia Environmental Sciences 13.
https://doi.org/10.1016/j.proenv.2012.01.048
Zandi, Rahman, Salmani Moghadam, Mohammad, & Roki, Zohra (2023). Measuring the degree of spatial autocorrelation of land surface temperature with land use, study sample: Isfahan city. Geography and Environmental Planning, 34(1), 61-76. (in Persian)
https://gep.ui.ac.ir/article_26770.html
 
 
 
 

Files

History

  • Receive Date: 17 July 2023
  • Revise Date: 09 January 2024
  • Accept Date: 27 February 2024

Share

How to cite

Statistics