Monitoring the spatial autocorrelation of land surface temperature with land use in different climatic regions(Case Study: The Metropolitans of Mashhad and Sari)

Document Type : Research Paper

Authors

1 Department of Remote Sensing and GIS, University of Tehran, Tehran, Iran.

2 Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran.

3 Department of Remote Sensing and GIS, University of Tehran, Tehran, Iran

Abstract

Knowing the temporal and spatial changes of land use and the formation of heat islands over time is one of the most important issues in metropolitan planning and policy making. Thus, in this study heat islands and temperature changes and its relationship with land use changes have been monitored over a period of 35 years in two study areas, i.e. the cities of Mashhad and Sari, using the Google Earth Engine platform. For this purpose, the LST was computed and the land use maps of the studied periods were extracted during 8 time steps of 5 years from 1985 to 2020. The aim of this study is to investigate the spatial autocorrelation of heat islands and its relationship with land use in two studied regions with different climatic conditions. The results of temperature monitoring showed an increase in temperature between 1 to 2 °C in all types of land uses during 35 years. This increasing trend of temperature is proportional to the type of land use changes, so that the temperature increase in built-up lands was estimated to be 2 and 1.75 degrees Celsius in the cities of Sari and Mashhad, respectively. The average temperature of the three months of summer in Mashhad city in built-up areas has increased from 34.5°C to 36.25°C and in Sari city from 29.51°C to 31.51°C. while the minimum increase in temperature has occurred in the lands with forest coverage, which is 1.02 °C and 1.19 °C, respectively in the cities of Sari and Mashhad. Conclusively, in both climatic regions, the areas where the changes are in the direction of reducing or removing vegetation and creating residential areas, the temperature increase is the maximum, and the areas where the changes are in the direction of increasing forests and agricultural lands, the temperature increase is the minimum.

Keywords

References

  1. References

    Abdi, Komeil Kamyabi, S. Zandmghaddam, M.Z. 2020. Investigating the role of urban green space coverage on the trend of surface temperature changes in urban environments (case study: Sari city). Environmental Science and Technology Quarterly. 23(2). 135-146. (In Persian)

    Abdul Athick, M.; Shankar, K. & Raja Naqvi, H. 2019. Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window. split window algorithm and spectral radiance model. Data in brief 27 (2019) 1047732.

    Agarwal, S., Vailshery, LS,, Jaganmohan, M., Nagendra, H., 2013. Mapping urban tree species using very high resolution satellite imagery: comparing pixel-based and object-based approaches. ISPRS International Journal of Geo-Information 2:220-236.

    Alavipanah, S. K. Hashemi Dare Badami, S. Kazemzadeh, A. 2014. Temporal-spatial analysis of the thermal island of Mashhad city according to the expansion of the city and land use-cover changes. Urban planning geography researches. 3(1). 1-17. (In Persian)

    Alavipanah, S. K., Mansourmoghaddam, M., Gomeh, Z., Galehban, E., & Hamzeh, S. (2022). The reciprocal effect of global warming and climatic change (new perspective): A review. Desert, 27(2), 291-305.

    Alavipanah, S.K. 2013. Principles of remote sensing and interpretation of aerial photos and satellite images. Second edition, Tehran, Tehran University (In Persian)

    Alavipanah, S.K. 2016. Thermal remote sensing and its application in earth sciences. Second edition, Tehran, Tehran University (In Persian)

    Amiri R., Q. Weng, A. Alimohammadi, and S. K. Alavipanah, "Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran," Remote sensing of environment, vol. 113, no. 12, pp. 2606-2617, 2009.

    Anselin L (1995) Local indicators of spatial association: LISA. Geogr Anal 27(2):93-115.

    Anselin L, Syabri I, Kho Y (2009) GeoDa: an introduction to spatial data analysis. In Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Springer, Berlin,Heidelberg and New York, pp.73-89.

    Asghari Saraskanroud, Syed and Emami, Hadi. 2018. Land surface temperature monitoring and land use relationship with surface temperature using OLI and ETM+ sensor images, case study: Ardabil city. Applied Research Journal of Geographical Sciences, 19 (53). (In Persian)

    Askari, A. 2011. Spatial statistics analysis using ArcGIS, publications of Tehran Municipality Information and Communication Technology Organization. 128 pages.

    Becker, F., & Li, Z. -L. (1990). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17– 33.

    Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62, 241– 252.

    Chen Q, Changjian N, Zhan L, Jingxuan R. 2009. Urban heat island effect research in Chengdu city based on MODIS data. In: 2009 3rd International Conference on Bioinformatics and Biomedical Engineering. IEEE, pp 1-5.

    Dadashi Rudbari, A. A., A. Falah Qalhari, 2014. Analyzing the autocorrelation of Iran's seasonal rainfall using APHRODITE rainfall database. Journals of Applied Meteorology, 2(1): 47-53. (In Persian)

    Estoque, R. C., Murayama, Y., & Myint, S. W. 2017. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577, 349-359.

    Feng Y., C. Gao, X. Tong, S. Chen, Z. Lei, and J. Wang, "Spatial patterns of land surface temperature and their influencing factors: a case study in Suzhou, China," Remote Sensing, vol. 11, no. 2, p. 182, 2019.

    Gao, Y., Mas J.F., and Navarrete A., 2009, The Improvement of an Object-oriented Classification Using Multi-Temporal MODIS EVI Satellite Data, International Journal of Digital Earth, Volume 2, Issue 3 September 2009 , PP. 219 - 236.

    Getis A, Ord JK (1992), the analysis of spatial association by use of distance statistics.Geogr Anal 24(3):189-206.

    Gorelick N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, vol. 202, pp. 18-27, 2017.

    1. Yang and Y. Liu, "A satellite remote sensing based assessment of urban heat island in Lanzhou city, northwest China," International Archives of Photogrammetry. Netherlands: Remote Sensing and Spatial Information Sciences, 2005.
    2. Yang and Y. Liu, "A satellite remote sensing based assessment of urban heat island in Lanzhou city, northwest China," International Archives of Photogrammetry. Netherlands: Remote Sensing and Spatial Information Sciences, 2005.

    Hamzeh, S, Mijani, Naeem, and K. Firoozjaei, "Modeling the Relationship between Surface Temperature, Topographic Conditions, and Vegetation Using Landsat 8 Satellite Images," Natural Geography Research, vol. 50, no. 1, pp. 35-55, 2018. (In Persian)

    Hashemidarebadami, Darvishi-Bulrani. A, Alavipanah, S.K, Maleki, Bayat. 2013. Analysis of heat island changes of urban surfaces during the day and night using MODIS multi-temporal products (case study: Tehran metropolis). Applied Research of Geographical Sciences. 52(19), 113-128. (In Persian)

    He J., W. Zhao, A. Li, F. Wen, and D. Yu, "The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas," International Journal of Remote Sensing, vol. 40, no. 5-6, pp. 1808-1827, 2019.

    Kazemi, M. Nafarzadegan, A. Mohammadi, F. 2018. Investigating the effect of land use changes on the thermal islands of Minab sity using random forest classification and spatial autocorrelation analysis. Journal of remote sensing and geographic information system in natural resources. 10(4) (In Persian)

    Khosravi, Y., M. A. Heydari, A. Tawakli and A. A. a Zamani. 2016. Analysis of the temporal relationship between the surface temperature and the spatial pattern of land use changes (case study of Zanjan city). Jornal of Space planning and design. 21(3), 119-144. (In Persian)

    Kotchi S. O., N. Barrette, A. A. Viau, J.-D. Jang, V. Gond, and M. A. Mostafavi, "Estimation and uncertainty assessment of surface microclimate indicators at local scale using airborne infrared thermography and multispectral imagery," Geospatial Technology–Environmental and Social Applications. Intech Publishers, Rijeka, Croatia, pp. 99-141, 2016.

    Kumar L. and O. Mutanga, "Google Earth Engine applications since inception: Usage, trends, and potential," Remote Sensing, vol. 10, no. 10, p. 1509, 2018.

    Lazzarini, M., Marpu, P. R., & 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.

    Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D., 2016. A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98.

    Mackie, R.I., 2013, Dynamic Analysis of Structures on Multicore Computers – Achieving Efficiency Through Object Oriented Design, Advances in Engineering Software 66, PP. 3–9.

    Memarian, H., Balasundram, S.K. and Khosla, R., 2013. Comparison between pixel-and object-based image classification of a tropical landscape using Système Pour l’Observation de la Terre-5 imagery. Journal of Applied Remote Sensing, 7(1), pp.073512-073512.

    Mutanga O. and L. Kumar, "Google Earth Engine Applications," ed: Multidisciplinary Digital Publishing Institute, 2019.

    Nascetti, M. Di Rita, R. Ravanelli, M. Amicuzi, S. Esposito, and M. Crespi, "free global dsm assessment on large scale areas exploiting the potentialities of the innovative google earth engine platform,"International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, 2017.

    Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and anapplication. Geogr Anal 27(4):287-306.

    Owen T., T. Carlson, and R. Gillies, "An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization," International journal of remote sensing, vol. 19, no. 9, pp. 1663-1681, 1998.

    Petropoulos, G.P., Kalaitzidis, C. and Vadrevu, K.P., 2012, Suppsrt Vector Machines and Object-based Classification for Obtaining Land-use/cover Cartography from Hyperion Hyperspectral Imagery, Computers & Geosciences, 41, PP. 99–107.

    Puissant, A., Rougier, S., Stumpf, A., 2014, Object-oriented Mapping of Urban Trees Using Random Forest Classifiers, International Journal of Applied Earth Observation and Geoinformation, 26, PP. 235–245.

    Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746.

    Rasul, A., Balzter, H., & Smith, C. (2015). Spatial variation of the daytime surface urban cool island during the dry season in Erbil, Iraqi Kurdistan, from Landsat 8. Urban Climate, 14, 176-186.

    Ravanelli R. et al., "Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems," Remote Sensing, vol. 10, no. 9, p. 1488, 2018.

    Reynolds J, Wesson K, Desbiez A, Ochoa- Quintero J, Leimgruber P. 2016. Using remote sensing and Random Forest to assess the conservation status of critical Cerrado Habitats in Mato Grosso do Sul, Brazil. Land, 5(2): 12-24.

    Rongali, G.; Ashok Kumar, K.; Ashvani Kumar, G. & Rakesh, K. 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 (2): 829 – 840.

    Rott H., "Physical principles and technical aspects of remote sensing," in Remote sensing in hydrology and water management: Springer, 2000, pp. 15-39.

    Sadeghi Nia, A., Alijani, B. & Ziyaian Firouzabadi, P. 2012. Spatial-temporal analysis of the thermal island of Tehran metropolis using remote sensing and geographic information system. Geography and environmental hazards. 1(4), 1-17. (In Persian)

    Shi Y., L. Katzschner, and E. Ng, "Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity," Science of the Total Environment, vol. 618, pp. 891-904, 2018.

    Sidhu N., E. Pebesma, and G. Câmara, "Using Google Earth Engine to detect land cover change: Singapore as a use case," European Journal of Remote Sensing, vol. 51, no. 1, pp. 486-500, 2018.

    Singh P, Kikon N, Verma P. 2017. Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 32: 100-114.

    Sobrino J. A., F. Del Frate, M. Drusch, J. C. Jiménez-Muñoz, P. Manunta, and A. Regan, "Review of thermal infrared applications and requirements for future high-resolution sensors," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2963-2972, 2016.

    Sobrino J., C. Coll, and V. Caselles, "Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5," Remote sensing of environment, vol. 38, no. 1, pp. 19-34, 1991.

    Sobrino, J.A., Jiménez- Munoz, J.C., Paolinib, L., (2004). Land surface temperature retrieval from Landsat TM, Remote Sensing of Environment, Vol 90 (4), Pp 434-440.

    Tajbakhsh, M., Memarian, H., & Shahrokhi, Y. (2016). Analyzing and modeling urban sprawl and land use changes in a developing city using a CA-Markovian approach. Global Journal of Environmental Science and Management, 2(4), 397-410.

    Tan J., D. Yu, Q. Li, X. Tan, and W. Zhou, "Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China," Scientific Reports, vol. 10, no. 1, pp. 1-9, 2020.

    Tran, D. X. Pla, F.Latorre-Carmona, PedroMyint, S, M .Kieu, H.(2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132.

    Waagepetersen R, Schweder T. 2006. Likelihoodbased inference for clustered line transect data. Journal of Agricultural, Biological, and Environmental Statistics, 11(3): 264.

    Wang R., M. Cai, C. Ren, B. Bechtel, Y. Xu, and E. Ng, "Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone," Urban Climate, vol. 28, p. 100455, 2019.(a)

    Wang Y.C., B. K. Hu, S. W. Myint, C.C. Feng, W. T. Chow, and P. F. Passy, "Patterns of land change and their potential impacts on land surface temperature change in Yangon, Myanmar," Science of the Total Environment, vol. 643, pp. 738-750, 2018.

    Wang, M.; Zhang, Z.; Hu, T. & Liu, X. 2019. A practical single-channel algorithm for land surface temperature retrieval: Application to Landsat series data. Journal of Geophysical Research: Atmospheres. 124. 299–316. (b)

    Xiao, R., Cao, W., Liu, Y. and Lu, B., 2022. The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta. Science of The Total Environment, 821, p.153381.

    Xunqiang M, Chen C, Fuqun Z, Hongyuan L.  2011. Study on temporal and spatial variation of the urban heat island based on Landsat TM/ETM+ in central city and Binhai New Area of Tianjin. In: 2011 International Conference on Multimedia Technology. IEEE, pp 4616-4622.

    Yan, G., 2003. Pixel Based and Object Oriented Image for Coal Fire Research, http://www.ITC.com (accessed in July 2008). pp. 3-99.

    Zandi, R., Salmani Moghadam, M., & Roki, Z. (2023). Measuring the Degree of Spatial Autocorrelation of Land Surface Temperature with Land Use (Isfahan City). Geography and Environmental Planning, 34(1), 61-76.

    Zehrakar, Z., Shahidi, A., Memarian Khalilabad, H. 2019. Comparing the accuracy of pixel-based and object-oriented methods in land use classification (case study: Samalqhan Basin). Natural Environment, Natural Resources of Iran, 73(4), 687-700. (In Persian)

    Zhang C, Luo L, Xu W, Ledwith V. 2008. Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ 398 (1-3):212-221.

    Zhang J., Y. Li, and Y. Wang, "Monitoring the urban heat island and the spatial expansion: using thermal remote sensing image of ETM+ band6," in Geoinformatics 2007: Remotely Sensed Data and Information, 2007, vol. 6752, p. 67522F: International Society for Optics and Photonics.

Desert
Volume 28, Issue 2
December 2023
Pages 329-351

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