تجزیه‌ و تحلیل آماری و پیش‌بینی تغییرات زمانی- مکانی جزایر حرارتی شهری با استفاده از داده‌های سنجش‌ از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه سنجش از دور و سیستم اطلاعات جغرافیایی، مرکز سنجش از دور و GIS، دانشگاه شهید بهشتی، تهران، ایران

چکیده

سابقه و هدف: جزیره گرمایی شهری به‌عنوان یکی از اثرات توسعه شهرنشینی می‌تواند بر روی گیاهان و جانوران درگیر در اکوسیستم شهری و حومه‌ای، غلظت آلاینده‌ها، کیفیت هوا، مصرف انرژی و آب و همچنین سلامت و اقتصاد انسان تأثیر منفی بگذارد. بنابراین، تجزیه وتحلیل مکانی-زمانی تغییرات جزیره گرمایی شهری به­ عنوان رویکردی موثر برای درک تأثیر شهرنشینی بر اکوسیستم شهری و حومه‌ای در نظر گرفته شده است که می‌تواند از توسعه و برنامه‌ریزی شهری پایدار نیز حمایت کند. بر این اساس، این مطالعه یک رویکرد جدید برای شناسایی روند و پیش‌بینی الگوی تغییرات جزایرحرارتی شهری با استفاده از تجزیه و تحلیل آماری، آنتروپی شانون و آمار کای اسکور ارائه می‌کند.
مواد و روش‌ها: منطقه موردمطالعه در این تحقیق شامل شهر رشت و اطراف آن است که در شمال کشور ایران واقع است. این مطالعه با استفاده از تصاویر سنجش از دور از سال 1991 تا 2021 که توسط ماهواره لندست 5 و 8 با فاصله زمانی ثابت 10 سال جمع آوری شده است، اجرا شد. تمامی تصاویر مربوط به فصل تابستان است. برای انجام این مطالعه ابتدا پیش‌پردازش‌های موردنیاز همچون تصحیحات اتمسفری و رادیومتریکی بر روی تصاویر اعمال‌شده است سپس در گام دوم شاخص‌های بیوفیزیکی سطح منطقه از تصاویر ماهواره‌ای استخراج‌شده است. در گام سوم دمای سطح زمین نیز با استفاده از تصاویر ماهواره‌ای در سال 2021 محاسبه شد. در گام چهارم، رگرسیون خطی چند متغیره خصوصیات بیوفیزیکی سطح و دمای سطح زمین در سال 2021 اعمال شد و سپس از مدل سلول های خودکار - زنجیره مارکوف برای پیش‌بینی دمای سطح زمین برای سال 2031 استفاده شد. در نهایت الگوی تغییرات جزایر حرارتی شهر رشت با استفاده از تحلیل‌های آماری در جهات جغرافیایی مختلف و دوره‌های زمانی متفاوت مورد بررسی قرار گرفت.
نتایج و بحث: نتایج این مطالعه نشان داد که بیشترین همبستگی مثبت (R=0.89) بین شاخص NDBI و دمای سطح زمین بوده است. همچنین بیشترین همبستگی منفی (R = -0.81) بین شاخص سبزینگی و دمای سطح زمین و در نهایت کمترین همبستگی (R = 0.42) بین شاخص درخشندگی با دمای سطح زمین بود. پیش‌بینی دمای سطح زمین با استفاده از مدل رگرسیون چند متغیره و شاخص‌های بیوفیزیکی سطح حاکی از خطای پایین این مدل (RMSE=1.33K) برای پیش‌بینی دمای سطح زمین در سال 2021 است. این بدان معناست که مقادیر پیش بینی شده در سال 2021 به مقادیر واقعی نزدیک است و بنابراین می ­توان به این مدل برای پیش بینی دمای سطح زمین در سال 2031 اعتماد کرد. تجزیه و تحلیل آماری درباره الگوی تغییرات جزایر حرارتی مشاهده ‌شده و مورد انتظار نشان می‌دهد که میزان نرخ تغییرات برحسب زمان و مکان متفاوت بوده است و همچنین به صورت پیوسته از سال 1991 تا 2031 رو به افزایش است. علاوه بر این این تجزیه و تحلیل ­ها همچنین نشان داد که جزایر حرارتی شهر رشت از درجه آزادی بالا و درجه پراکندگی بالایی برخودار است. بنابراین درجه خوب بودن آن منفی است.
نتیجه‌گیری: الگوی تغییرات جزایر حرارتی از گذشته تا به زمان حال و پیش بینی آن در آینده نشان می ­دهد که وابستگی بالایی با الگوی تغییرات اراضی ساخته‌ شده دارد. در نتیجه با نظارت و کنترل مستقیم الگوی اراضی ساخته شده (همچون توسعه عمودی از طریق بام و دیوارهای سبز و مصالح ساختمانی با توان بازتابی بالا) و جلوگیری از ساخت و سازها در زمین­ های کشاورزی حاشیه شهر الگوی تغییرات جزایر حرارتی را کنترل نمود.

کلیدواژه‌ها


عنوان مقاله [English]

Statistical analysis and predicting spatio-temporal variations of urban heat islands using remote sensing data

نویسندگان [English]

  • Keyvan Ezimand
  • Hossein Aghighi
  • Davod Ashourloo
  • Alireza SHakiba
Department of Remote Sensing and GIS, Remote Sensing and GIS Center, University of Shahid Beheshti, Tehran, Iran
چکیده [English]

Introduction: The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystems, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis of the urban heat island changes has been considered an effective approach to understanding the impact of urbanization on the urban and suburban ecosystem, which also can support sustainable urban development and planning. Accordingly, this study contributes a novel approach to identifying the trend and predicting the pattern of UHI changes using statistical analysis, Shannon's entropy and chi-score statistics.
Material and methods: The study area of this research is the city of Rasht and its surroundings, a region located in the north of Iran. This research was implemented using remote sensing images from 1991 to 2021 that were collected by LANDSAT 5 and 8 with a fixed time interval of 10 years. All images were captured in the summer. In order to conduct this research in the pre-foresight stage, first, the required preprocessing, including atmospheric and radiometric corrections applied to the satellite images. Then, the surface biophysical characteristics of the study area were extracted from the satellite images. In the third step, the land surface temperature was computed using satellite images in 2021. In the fourth step, Multivariate linear regression between surface biophysical characteristics and the land surface temperature in 2021 was applied and then the cellular automata-Markov chain model was utilized to predict the land surface temperature for 2031. Finally, the pattern of changes in the urban heat island of Rasht city was investigated using statistical analysis in different geographic directions and different time periods.
Results and discussion: The results of this study indicate that the highest positive correlation (R=0.89) was between NDBI and LST. Moreover, the highest negative correlation (R=-0.81) was between the greenness and LST. Our results also showed that the lowest correlation (R=0.42) was between the brightness and LST. The predicted LST corresponding to surface biophysical characteristics using a multivariate linear regression model illustrates the low error of this approach (RMSE=1.33K) in 2021. This means that the predicted values in 2021 are close to the real values, and therefore, this model can be trusted to predict LST in 2031. Statistical analysis of the patterns of observed and expected changes in UHI clearly illustrated that Rasht urban expansion and the UHI expansion will consistently continue to increase from 1991 to 2031. However, the expansion rate changes over time and space. Moreover, these analyses also showed that the UHI of Rasht city has a high degree of freedom and a high degree of sprawl. Thus, and as a result, its degree of goodness is negative.
Conclusion: The pattern of UHI changes is highly dependent on the pattern of built-up land changes: as a result, sustainable development, resilience and environmental protection of Rasht requires direct monitoring and control of the pattern of urban growth, such as preventing changes in built-up areas and agricultural lands in suburban areas by incorporating a vertical form of development as well as constructing green roofs and walls and using high-reflectance building materials.

کلیدواژه‌ها [English]

  • Remote sensing data
  • cellular automata-Markov chain modeling
  • Statistical analysis
  • urban heat island (UHI)
Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. and Choi, K., 2013. Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing. 5, 5969-5998.
Akbari, H. and Matthews, H.D, 2012. Global cooling updates: Reflective roofs and pavements. Energy and Buildings. 55, 2-6.
Akbari, H., Pomerantz, M. and Taha, H., 2001. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy. 70, 295-310.
Alavi Moghadam, M.R., Mokhtarani  N. and Mokhtarani, B., 2009. Municipal solid waste management in Rasht City, Iran. Waste Management. 29, 485-489.
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.
Almeida, C.M.D., Monteiro, A.M.V., Câmara, G., Soares‐Filho, B.S., Cerqueira, G.C., Pennachin, C.L. and Batty, M., 2005. GIS and remote sensing as tools for the simulation of urban land‐use change. International Journal of Remote Sensing. 26, 759-774.
Almusaed., A., 2011. The urban heat island phenomenon upon urban components. Biophilic and Bioclimatic Architecture. Springer.
Amiri, R., Weng, Q., Alimohammadi, A. and Alavipanah, S.K., 2009. 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. 113, 2606-2617.
Artis, D.A. and Carnahan, W. H., 1982. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment. 12, 313-329.
Avdan, U. and Jovanovska, G., 2016. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors. 2016, 1480307.
Azimi,  N., 2005. Restructuring Urban Morphology: ACase study of Rasht, Iran.
Baig, M.H.A., Zhang, L., Shuai, T. and Tong, Q., 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters. 5, 423-431.
Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A. and Wood, E.F., 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific data. 5, 180214.
Bek, M.A., Azmy, N. and Elkafrawy, S., 2018. The effect of unplanned growth of urban areas on heat island phenomena. Ain Shams Engineering Journal.
Bhatta, B., Saraswati, S. and Bandyopadhyay, D., 2010. Quantifying the degree-of-freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data. Applied Geography. 30, 96-111.
Chakraborty, S.D., Kant, Y. and Mitra, D., 2015. Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. Journal of environmental management. 148, 143-152.
Chander, G., Markham, B.L. and Helder, D. L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment. 113, 893-903.
CI, 2016. Census information, rasht: the statistical centre of iran. Https://www.amar.org.ir/english.
Clinton, N. and Gong, P., 2013. MODIS detected surface urban heat islands and sinks: Global locations and controls. Remote Sensing of Environment. 134, 294-304.
Dadras, M., Shafri, H.Z.M., Ahmad, N., Pradhan, B. and Safarpour, S., 2015. Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. The Egyptian Journal of Remote Sensing and Space Science. 18, 35-52.
Duan, S., Luo, Z., Yang, X. and Li, Y., 2019. The impact of building operations on urban heat/cool islands under urban densification: A comparison between naturally-ventilated and air-conditioned buildings. Applied Energy. 235, 129-138.
Echendu, A.J. and Okafor, P.C.C., 2021. Smart city technology: a potential solution to Africa's growing population and rapid urbanization?. Development Studies Research. 8, 82-93.
El-Hattab, M., Amany, S. and Lamia, G., 2018. Monitoring and assessment of urban heat islands over the Southern region of Cairo Governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Science. 21, 311-323.
ENVI, 2009. Atmospheric correction module: quac and flaash user’s guide. Accessed 19 december 2014. Www.exelisvis.com/portals/0/pdfs/envi/flaash.
Ezimand, K., Azadbakht, M. and Aghighi, H., 2021a. Analyzing the effects of 2D and 3D urban structures on LST changes using remotely sensed data. Sustainable Cities and Society. 103216.
Ezimand, K., Chahardoli, M., Azadbakht, M. and Matkan, A.A., 2021b. Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques. Sustainable Cities and Society. 64, 102508.
Ezimand, K., Kakroodi, A.A. and Kiavarz, M., 2018. The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. International Journal of Remote Sensing. 39, 8428-8449.
Fan, F., Wang, Y. and Wang, Z., 2008. Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental Monitoring and Assessment. 137, 127-147.
Firozjaei, M.K., Fathololoumi, S., Kiavarz, M., Arsanjani, J.J. and Alavipanah, S.K., 2020. Modelling surface heat island intensity according to differences of biophysical characteristics: A case study of Amol city, Iran. Ecological Indicators. 109, 105816.
Firozjaei, M.K., Kiavarz, M. and Alavipanah, S.K., 2022. Impact of surface characteristics and their adjacency effects on urban land surface temperature in different seasonal conditions and latitudes. Building and Environment. 219, 109145.
Ghosh, M.S., Kumar, D. and Kumari, R., 2022. Assessing spatiotemporal variations in land surface temperature and SUHI intensity with a cloud based computational system over five major cities of India. Sustainable Cities and Society. 104060.
Gober, P., 2007. The Impact of the Phoenix Urban Heat Island on Residential Water Use AU - Guhathakurta, Subhrajit. Journal of the American Planning Association. 73, 317-329.
Goldblatt, R., Deininger, K. and Hanson, G., 2018. Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam. Development Engineering. 3, 83-99.
Grigoraș, G. and Urițescu, B., 2019. Land Use/Land Cover changes dynamics and their effects on Surface Urban Heat Island in Bucharest, Romania. International Journal of Applied Earth Observation and Geoinformation. 80, 115-126.
Haashemi, S., Weng, Q., Darvishi, A. and Alavipanah, S.K., 2016. Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sensing. 8, 352.
Habitat, U., 2020. WORLD CITIES REPORT 2020: The value of sustainable urbanization. United Nations.
Healey, S.P., Cohen, W.B., Zhiqiang, Y. and Krankina, O.N., 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment. 97, 301-310.
Howard, L., 1833. The climate of London: deduced from meteorological observations made in the metropolis and at various places around it, Harvey and Darton, J. and A. Arch, Longman, Hatchard, S. Highley and R. Hunter.
Huang, C., Wylie, B., Yang, L., Homer, C. and Zylstra, G., 2002. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing. 23, 1741-1748.
Jaeger, J.A. and Schwick, C., 2014. Improving the measurement of urban sprawl: Weighted Urban Proliferation (WUP) and its application to Switzerland. Ecological Indicators. 38, 294-308.
Jamei, Y., Rajagopalan, P. and Sun, Q., 2019. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of The Total Environment. 659, 1335-1351.
Jiménez‐Muñoz, J.C. and Sobrino, J.A., 2003. A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres. 108.
Kleerekoper, L., Van Esch, M. and Salcedo, T.B., 2012. How to make a city climate-proof, addressing the urban heat island effect. Resources, Conservation and Recycling. 64, 30-38.
Kloog, I., Chudnovsky, A., Koutrakis, P. and Schwartz, J., 2012. Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA. Science of the total environment. 432, 85-92.
Kohler, M., Tannier, C., Blond, N., Aguejdad, R. and Clappier, A., 2017. Impacts of several urban-sprawl countermeasures on building (space heating) energy demands and urban heat island intensities. A case study. Urban Climate. 19, 92-121.
Kuang, W., Du, G., Lu, D., Dou, Y., Li, X., Zhang, S., Chi, W., Dong, J., Chen, G. and Yin, Z., 2021. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 66, 297-300.
Kumar, J.A.V., Pathan, S. and Bhanderi, R., 2007. Spatio-temporal analysis for monitoring urban growth–a case study of Indore city. Journal of the Indian Society of Remote Sensing. 35, 11-20.
Li, H., Meier, F., Lee, X., Chakraborty, T., Liu, J., Schaap, M. and Sodoudi, S., 2018. Interaction between urban heat island and urban pollution island during summer in Berlin. Science of The Total Environment. 636, 818-828.
Li, J., Song, C., Cao, L., Zhu, F., Meng, X. and Wu, J., 2011. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment. 115, 3249-3263.
Li, T. and Meng, Q., 2018. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data. Landscape and Urban Planning. 179, 63-71.
Li, Z., Goldstein, R. H. and Franseen, E. K., 2017.Meteoric calcite cementation: diagenetic response to relative fall in sea-level and effect on porosity and permeability, Las Negras area, southeastern Spain. Sedimentary Geology. 348, 1-18.
Lilly Rose, A. and Devadas, M.D., 2009. analysis of land surface temperature and land use/land cover types using remote sensing imagery-a case in chennai city, india.  The seventh International Conference on Urban Clim held on.
Liu, Q., Liu, G., Huang, C., Liu, S. and Zhao, J., 2014. A tasseled cap transformation for Landsat 8 OLI TOA reflectance images.  Geoscience and Remote Sensing Symposium (IGARSS), IEEE International. 541-544.
Liu, W., Zhan, J., Zhao, F., Yan, H., Zhang, F. and Wei, X., 2019. Impacts of urbanization-induced land-use changes on ecosystem services: A case study of the Pearl River Delta Metropolitan Region, China. Ecological Indicators. 98, 228-238.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A.D. and Chen, A., 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability. 3, 564-570.
Markham, B.L. and Helder, D.L., 2012. Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Remote Sensing of Environment. 122, 30-40.
Meng, Q., Zhang, L., Sun, Z., Meng, F., Wang, L. and Sun, Y., 2018. Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: A 12-year case study in Beijing, China. Remote Sensing of Environment. 204, 826-837.
Mitsova, D., Shuster, W. and Wang, X., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning. 99, 141-153.
Moazzam, M.F.U., Doh, Y.H. and Lee, B.G., 2022. Impact of urbanization on land surface temperature and surface urban heat Island using optical remote sensing data: A case study of Jeju Island, Republic of Korea. Building and Environment. 109368.
Moghaddam, Y.J., Akhoondzadeh, M. and Saradjian, M., 2015. A split-window algorithm for estimating lst from landsat-8 satellite images. Journal of Geomatics Science and Technology. 5, 215-226.
Mohamed, A. and Worku, H., 2018. Quantification of the land use/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone. Journal of Urban Management.
Mushore, T.D., Odindi, J., Dube, T., Matongera, T.N. and Mutanga, O., 2017a. Remote sensing applications in monitoring urban growth impacts on in-and-out door thermal conditions: A review. Remote Sensing Applications: Society and Environment. 8, 83-93.
Mushore, T.D., Odindi, J., Dube, T. and Mutanga, O., 2017b. Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city, Zimbabwe. Building and Environment. 122, 397-410.
Nakata-Osaki, C.M., Souza, L.C.L. and Rodrigues, D.S., 2018. THIS–Tool for Heat Island Simulation: A GIS extension model to calculate urban heat island intensity based on urban geometry. Computers, Environment and Urban Systems. 67, 157-168.
Negah, S., 2016. Investigation of the PM10 and PM2. 5 concentrations and meteorological parameters in dust emission hazard to the south west region of the Caspian Sea (Rasht city). Caspian Journal of Applied Sciences Research. 5.
O'Lenick, C.R., Wilhelmi, O.V., Michael, R., Hayden, M.H., Baniassadi, A., Wiedinmyer, C., Monaghan, A. J., Crank, P.J. and Sailor, D.J., 2019. Urban heat and air pollution: A framework for integrating population vulnerability and indoor exposure in health risk analyses. Science of The Total Environment. 660, 715-723.
Oke, T.R., 1973. City size and the urban heat island. Atmospheric Environment. 7, 769-779.
Oukawa, G.Y., Krecl, P. and Targino, A.C., 2022. Fine-scale modeling of the urban heat island: a comparison of multiple linear regression and random forest approaches. Science of the total environment. 815, 152836.
Patra, S., Sahoo, S., Mishra, P. and Mahapatra, S.C., 2018. Impacts of urbanization on land use /cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management. 7, 70-84.
Pontius, R.G., 2000. Quantification error versus location error in comparison of categorical maps. Photogrammetric engineering and remote sensing. 66, 1011-1016.
Qin, Z., Karnieli, A. and 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, 3719-3746.
Rahman, M.T., Aldosary, A.S. and Mortoja, M., 2017. Modeling future land cover changes and their effects on the land surface temperatures in the Saudi Arabian eastern coastal city of Dammam. Land. 6, 36.
Rajitha, K., Mukherjee, C., Vinu Chandran, R. and Prakash Mohan, M., 2010. Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta, Andhra Pradesh, India using multi-temporal satellite data. International Journal of Remote Sensing. 31, 4423-4442.
Ramachandra, T., Bharath, H. and Sowmyashree, M., 2014. Urban Footprint of Mumbai-The Commercial Capital of India. Journal of Urban and Regional Analysis. 6, 71.
Ramakreshnan, L., Aghamohammadi, N., Fong, C.S., Ghaffarianhoseini, A., Ghaffarianhoseini, A., Wong, L P., Hassan, N. and Sulaiman, N.M., 2018. A critical review of Urban Heat Island phenomenon in the context of Greater Kuala Lumpur, Malaysia. Sustainable Cities and Society. 39, 99-113.
Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H. and Zhu, Z., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment. 145, 154-172.
Santamouris, M., 2020. Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy and Buildings. 207, 109482.
Santamouris, M. and Kolokotsa, D., 2015. On the impact of urban overheating and extreme climatic conditions on housing, energy, comfort and environmental quality of vulnerable population in Europe. Energy and Buildings. 98, 125-133.
Sejati, A.W., Buchori, I. and Rudiarto, I., 2019. The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region. Sustainable Cities and Society. 46, 101432.
Seto, K.C., Güneralp, B. and Hutyra, L.R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences. 109, 16083-16088.
Shafizadeh-Moghadam, H., Tayyebi, A., Ahmadlou, M., Delavar, M.R. and Hasanlou, M., 2017. Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. Computers, Environment and Urban Systems. 65, 28-40.
Shen, H., Huang, L., Zhang, L., Wu, P. and Zeng, C., 2016. Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China. Remote Sensing of Environment. 172, 109-125.
Sobrino, J.A., Jimenez-Munoz, J.C. and Paolini, L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment. 90, 434-440.
Sultana, S. and Satyanarayana, A.N.V., 2020. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000 – 2018 over a sub-tropical Indian City. Sustainable Cities and Society. 52, 101846.
Sun, Y., Gao, C., Li, J., Li, W. and Ma, R., 2018. Examining urban thermal environment dynamics and relations to biophysical composition and configuration and socio-economic factors: A case study of the Shanghai metropolitan region. Sustainable Cities and Society. 40, 284-295.
UN, 2018. World Urbanization Prospects: The 2018 Revision. United Nations New York.
Wang, Z.-H. and Upreti, R., 2019. A scenario analysis of thermal environmental changes induced by urban growth in Colorado River Basin, USA. Landscape and Urban Planning. 181, 125-138.
Weng, Q., Firozjaei, M.K., Sedighi, A., Kiavarz, M. and Alavipanah, S.K., 2019. Statistical analysis of surface urban heat island intensity variations: A case study of babol city, iran. GIScience and remote sensing. 56, 576-604.
Weng, Q. and Yang, S, 2004. Managing the adverse thermal effects of urban development in a densely populated Chinese city. Journal of Environmental Management. 70, 145-156.
Willmott, C.J., Robeson, S. M. and Matsuura, K., 2012. A refined index of model performance. International Journal of climatology. 32, 2088-2094.
Wonorahardjo, S., Sutjahja, I.M., Mardiyati, Y., Andoni, H., Thomas, D., Achsani, R.A. and Steven, S., 2020. Characterising thermal behaviour of buildings and its effect on urban heat island in tropical areas. International Journal of Energy and Environmental Engineering. 11, 129-142.
Xu, H., 2010. Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogrammetric Engineering and Remote Sensing. 76, 557-565.
Yang, X., Zheng, X.Q. and Lv, L.N., 2012. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling. 233, 11-19.
Yao, R., Wang, L., Huang, X., Niu, Z., Liu, F. and Wang, Q., 2017. Temporal trends of surface urban heat islands and associated determinants in major Chinese cities. Science of The Total Environment. 609, 742-754.
Yuan, J., 2020. Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory. Geosciences. 10, 362.
Zha, Y., Gao, J. and Ni, S., 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International journal of remote sensing. 24, 583-594.
Zhou, W., Jiao, M., Yu, W. and Wang, J., 2017. Urban sprawl in a megaregion: a multiple spatial and temporal perspective. Ecological Indicators.
Zhou, X. and Chen, H., 2018. Impact of urbanization-related land use land cover changes and urban morphology changes on the urban heat island phenomenon. Science of The Total Environment. 635, 1467-1476.
Ziter, C.D., Pedersen, E.J., Kucharik, C.J. and Turner, M.G., 2019. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proceedings of the National Academy of Sciences. 116, 7575.
Zullo, F., Fazio, G., Romano, B., Marucci, A. and Fiorini, L., 2019. Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Science of The Total Environment. 650, 1740-1751.