The relationship between the land surface temperature, changes in the vegetation cover and air pollutants using the Google Earth Engine
Subject Areas : Environment
Razieh
Mirfazlolah
1
(Master's student in Remote Sensing, Yazd branch, Islamic Azad University, Yazd, Iran)
Keywords: air pollutants, NDVI, LST, Goggle Earth Engine,
Abstract :
Background and objective:The research was conducted to the relationship review of LST and NDVI of the Mashhad and Gorgan cities of Iran using the GEE system based on the Landsat 8 OLI and Sentinel 5P images from the period between 1/1/2021 and 1/1/2022. The purpose of this research was to compare the relationship between the temperature of the earth's surface and changes in the vegetation index and its possible relationship with air pollution.Materials and methods:For this purpose, first, the date of certain images and then the desired bands for calculating three variables, earth surface temperature, vegetation index, and air pollution were introduced to it. In the end, a normalized vegetation difference index or NDVI was obtained to calculate surface emissivity and LST land surface temperature map, and an air pollutants map (SO2, NO2, HCHO, CO, Aerosol) was prepared and produced.Results and conclusion:The results showed that the highest average temperature for the cities of Mashhad and Gorgan is 42 and 35 degrees Celsius, and the lowest average temperature is 27 and 17 degrees Celsius, respectively. It can also be seen that the relationship between the temperature of the earth's surface and the amount of vegetation has an inverse relationship. Thus, the lowest temperature is related to the areas with the most vegetation and the highest temperature is related to the barren lands and built areas. By superimposing the surface temperature map and the air pollution map, it was found that high temperature brings more pollution.
Anjomshoa, F., Morovati, M., Tazeh, M., & Bahadori Amjaz, F. (2021). Investigating the Relationship between Thermal Islands and Green Space Areas and Detecting its Changes (Case Study: Kerman City). Geography and Environmental Sustainability, 11(4), 83-106. https://doi.org/10.22126/ges.2022.6836.2439
Arianpour, M., & Jamali, A. A. (2014). Locating flood spreading suitable sites for groundwater recharging through multi criteria modeling in GIS (case study: Omidieh-Khuzestan). Journal of Biodiversity and Environmental Sciences, 5, 119-127.
Areffian, A., Kiani Sadr, M., Eslamian, S. Khoshfetrat, A., 2021, Monitoring the effects of drought on vegetation in mountainous areas using MODIS satellite images (Case study: Lorestan province), Journal of Environmental Sciences Studies, 5(4): 3183-3189. https://doi.org/10.1007/s12205-021-2062-x
Arvin. (2017). Investigating thermal island in connection with air pollution in Isfahan city. Geography and Environmental Hazards, 7(1), 115-129. https://doi.org/10.22067/geo.v7i1.64590
Dang, T., Yue, P., Bachofer, F., Wang, M., & Zhang, M. (2020). Monitoring land surface temperature change with landsat images during dry seasons in Bac Binh, Vietnam. Remote Sensing, 12(24), 4067. https://doi.org/10.3390/rs12244067
Ebrahimi, A., Motamedvaziri, B., Nazemosadat, S. M. J., & Ahmadi, H. (2020). Assessing the relationship between land surface temperature with vegetation and water area change in Arsanjan county, Iran. Journal of RS and GIS for Natural Resources, 11(4), 65-86. dorl.net/dor/20.1001.1.26767082.1399.11.4.4.4
Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17-26. https://dx.doi.org/10.30495/jonass.2021.680327
Hadipour, Darabi, & Davodi Rod. (2020). Investigating urban thermal islands and its relationship with air pollution conditions and NDVI and NDBI indices in Arak city. Scientific-Research Quarterly of Geographical Information "Sephehr", 28(112), 249-264. https://doi.org/10.22131/sepehr.2020.38619
Jamali, A. A., Zarekia, S., & Randhir, T. O. (2018). Risk assessment of sand dune disaster in relation to geomorphic properties and vulnerability in the Saduq-Yazd Erg. Applied Ecology and Environmental Research, 16(1), 579-590. https://doi.org/10.15666/aeer/1601_579590
Jamali, A. A., & Raeesi, N. (2015). Socio-Economic Aspects of Some Watershed Management Projects in Mateh-Sang Watershed, Iran. Journal of Agriculture and Biological Science, 10(7), 280-287.
Khoshnam, A. M., Jamali, A. A., & Zare, A. (2015). The Effect of Individual, Social and Economic Factors on Villagers Participation in Watershed Projects in MianKouh Watershed, Yazd. European Online Journal of Natural and Social Sciences: Proceedings, 4(1 (s)), pp-446.
Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., ... & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 15(131), 14-37. https://doi.org/10.1016/j.rse.2012.12.008
Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., & Xu, B. (2021). Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0. Remote Sensing of Environment, 258, 112364. https://doi.org/10.1016/j.rse.2021.112364
Lu, D., & Weng, Q. (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 70(9), 1053-1062. https://doi.org/10.14358/PERS.70.9.1053
Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131-140. https://doi.org/10.4236/ahs.2014.31006
Masoumi, H., Jamali, A. A., & Khabazi, M. (2014). Investigation of role of slope, aspect and geological formations of landslide occurrence using statistical methods and GIS in some watersheds in Chahar Mahal and Bakhtiari Province. J. Appl. Environ. Biol. Sci, 4(9), 121-129.
Niliyeh Brojeni, M., & Ahmadi Nadoushan, M. (2019). The relationship between urban vegetation and land surface temperature in Isfahan city using Landsat TM and OLI satellite images and LST index. Environmental Sciences, 17(4), 163-178.https://doi.org/10.29252/envs.17.4.163
Panda, S., & JAIN, M. K. (2017). Effects of Green Space Spatial Distribution on Land Surface Temperature: Implications for Land Cover Change as Environmental Indices. International Journal of Earth Sciences and Engineering, 10(2), 180-184.https://doi.org/10.21276/ijee.2017.10.0207
Soltani, N., & Mohammadnejad, V. (2021). Efficiency of Google Earth Engine (GEE) system in land use change assessment and predicting it using CA-Markov model (Case study of Urmia plain). Journal of RS and GIS for Natural Resources, 12(3), 101-114. http://dorl.net/dor/20.1001.1.26767082.1400.12.3.6.1
Srivastava, P. K., Majumdar, T. J., & Bhattacharya, A. K. (2009). Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research, 43(10), 1563-1574. https://doi.org/10.1016/j.asr.2009.01.023
Sun, D., & Kafatos, M. (2007). Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34(24),1. https://doi.org/10.1029/2007GL031485
USGS, 2013, http://landsat.usgs.gov/Landsat8 Using Product. PHP.
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467-483. https://doi.org/10.1016/j.rse.2003.11.005
Yaghobi, S., Heidarizadi, Z., & Mirzapour, H. (2019). Comparing NDVI and RVI for forest density estimation and their relationships with rainfall (Case study: Malekshahi, Ilam Province). Environmental Resources Research, 7(2), 117-128. https://doi.org/10.22069/ijerr.2019.4819
Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), 375-386.
https://doi.org/10.1016/j.rse.2006.09.003
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. https://doi.org/10.1080/01431160304987
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. Procedia Environmental Sciences, 13, 574-581. https://doi.org/10.1016/j.proenv.2012.01.048