Article information
2022 , Volume 27, ¹ 3, p.95-111
Mamash E.A., Pestunov I.A., Sinyavskiy Y.N.
Analysis of patterns in the distribution of the temperature fields for large industrial cities of Siberia according to Landsat-8 data
Currently, one of the most rapidly developing and popular directions in remote sensing data processing and application is the analysis of longwave infrared (815 𝜇m) data. These data are widely used to analyze the underlying surface temperature (LST) for both natural and urban areas. Analysis of temperature fields of large cities allows identifying the thermal anomalies, its sources, intensity and character of distribution, defining the boundaries of surface urban heat island, and revealing patterns in temperature distribution within the city territory. It is important for rational planning and development of urban infrastructure, prevention and resolving of environmental problems, and creating a comfortable area for living. This paper addresses the estimation and analysis of the temperature field of the territories of major industrial Siberian cities by satellite data. The temperature maps of urban areas of Barnaul, Kemerovo, Krasnoyarsk, Novosibirsk, and Omsk for the snow-free period of 20132021 were constructed from Landsat-8 multitemporal data with Google Earth Engine system. The resulting maps allow us to identify patterns in the distribution of temperature fields, which, in turn, could provide information for evaluating the industrial development of cities, the degree of urbanization and the ecological state of the territory. An approach to qualitative assessment of the spatial differentiation of urban green areas, characterizing the level of comfort of the living and recreation environment, based on the analysis of histograms constructed by multi-year Landsat-8 LST data, is proposed. The analysis of constructed histograms showed the fundamental opportunity of its use for the integral evaluation of the urban environment comfort indices. For Siberian cities, the strong correlation between the Landsat-8 LST and the NDBI building index is also confirmed, which agrees with the results of different authors for other cities. In addition, the value of Landsat-8 LST data can be used as an additional informative feature for the classification of urban areas.
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Keywords: underlying surface temperature, LST, Landsat-8, heat island, Google Earth Engine, major Siberian cities
doi: 10.25743/ICT.2022.27.3.008
Author(s): Mamash Elena Alexandrovna PhD. Position: Senior Research Scientist Office: Institute of Computational Technologies of SB RAS Address: 630090, Russia, Novosibirsk, prospect Akademika Lavrentjeva, 6
Phone Office: (383) 330 78 26 E-mail: elenamamash@gmail.com SPIN-code: 3961-1369Pestunov Igor Alekseevich PhD. , Associate Professor Position: Leading research officer Office: Federal Research Center for Information and Computational Technologies Address: 630090, Russia, Novosibirsk, Ac. Lavrentiev ave., 6
Phone Office: (383) 334-91-55 E-mail: pestunov@ict.nsc.ru SPIN-code: 9159-3765Sinyavskiy Yuriy Nikolaevich Position: Research Scientist Office: Institute of Computational Technologies SB RAS Address: 630090, Russia, Novosibirsk, Ac. Lavrentiev ave., 6
E-mail: yorikmail@gmail.com
References:
1. Sannigrahi S., Bhatt S., Rahmata S., Uniyal B., Banerjee S., Chakraborti S., Jha S., Lahiri S., Santra K., Bhatt A. Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Climate. 2018; (24):803819. DOI:10.1016/j.uclim.2017.10.002. Available at: https://www.researchgate.net/publication/320483430_Analyzing_the_role_of_biophysical_compositions_in_minimizing_urban_land_ surface_temperature_and_urban_heating.
2. Cook M., Schott J.R., Mandel J., Raqueno N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sensing. 2014; (6):1124411266. DOI:10.3390/rs61111244.
3. Li Z., Tang B., Wu H., Ren H., Yan G., Wan Z., Trigo I.F., Sobrino J.A. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment. 2013; (131):1437.
4. Sobrino J.A., Jim´enez-Mu˜noz J.C., Paolini L. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment. 2004; 90(4):434440. DOI:10.1016/J.RSE.2004.02.003. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0034425704000574?via%3Dihub.
5. Ermida S.L., Soares P., Mantas V., Ge¨ottsche F.M., Trigo I.F. Google Earth Engine opensource code for land surface temperature estimation from the Landsat series. Remote Sensing. 2020; (12):1471.
6. Nill L., Ullmann T., Kneisel C., Sobiech-Wolf J., Baumhauer R. Assessing spatiotemporal variations of Landsat land surface temperature and multispectral indices in the Arctic Mackenzie delta region between 1985 and 2018. Remote Sensing. 2019; 11(19):2329. DOI:10.3390/rs11192329. Available at: https://www.mdpi.com/2072-4292/11/19/2329.
7. Wang M., Zhang Z., Hu T., Wang G., He G., Zhang Z., Li H., Wu Z., Liu X. An efficient framework for producing Landsat-based land surface temperature data using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; (13):4689.
8. Chakraborty T.C., Lee X., Ermida S., Zhan W. On the land emissivity assumption and Landsat-derived surface urban heat islands: a global analysis. Remote Sensing of Environment. 2021; (265):112682.
9. Gosteva A.A., Matuzko A.K., Yakubailik O.E. Search of changes in the temperature of urban environment with use of satellite data on the example of the Krasnoyarsk. CEUR Workshop Proceedings. 2020; (2534):401405. Available at: http://ceur-ws.org/Vol-2534/68_short_paper. pdf.
10. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment. 1997; (62):241252.
11. Valor E., Caselles V. Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sensing of Environment. 1996; (57):167184.
12. Van de Griend A.A., Owe M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing. 1993; 14(6):11191131.
13. Sekertekin A., Bonafoni S. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing. 2020; 12(2):294.
14. Walawender J.P., Szymanowski M., Hajto M.J., Bokwa A. Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ data. Pure and Applied Geophysics. 2014; (171):913940.
15. Mustafa E.K., Co Y., Liu G., Kaloop M.R., Beshr A.A., Zarzoura F., Sadek M. Study for predicting land surface temperature (LST) using Landsat data: a comparison of four algorithms. Advances in Civil Engineering. 2020; (3):7363546. DOI:10.1155/2020/7363546 Available at: https:// www.researchgate.net/publication/339149597_Study_for_Predicting_Land_Surface_Temperature_LST_Using_Landsat_Data_A_Comparison_of_Four_Algorithms.
16. Sobrino J.A., Oltra-Carri´o R., S`oria G., Jim´enez-Mu˜noz J.C., Julien Y., Cuenca J., Romaguera M. Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. International Journal of Remote Sensing. 2013; (34):31773192.
17. Fudala J., N`adudvari A., Bronder J., Fudala M. ` Application of satellite images analysis to assess the variability of the surface thermal heat island distribution in urban areas. E3S Web of Conferences. 2018; (28):01011. Available at: https://www.e3s-conferences.org/articles/e3sconf/pdf/2018/ 03/e3sconf_aptp2018_01011.pdf.
18. Twumasi Y.A., Merem E.C., Namwamba J.B., Mwakimi O.S., Ayala-Silva T., Frimpong D.B., Ning Z.H., Asare-Ansah A.B., Annan J.B., Oppong J., Loh P.M., Owusu F., Jeruto V., Petja B.M., Okwemba R., McClendon-Peralta J., Akinrinwoye C.O., Mosby H.J. Estimation of land surface temperature from Landsat-8 OLI thermal infrared satellite data. A comparative analysis of two cities in Ghana. Advances in Remote Sensing. 2021; (10):131149.
19. Khallef B., Biskri Y., Mouchara N., Brahamia K. Analysis of urban heat islands using Landsat 8 OLI/TIR data: case of the city of Guelma (Algeria). Asian Journal of Environment & Ecology. 2020; 12(4):4251.
20. Soydan O. Effects of landscape composition and patterns on land surface temperature: urban heat island case study for Nigde, Turkey. Urban Climate. 2020; (34):100688.
21. Denga C., Zhu Z. Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sensing of Environment. 2020; (238):110929.
22. Balew A., Korme T. Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Sciences. 2020; (23):371386.
23. Matuzko Ą.Ź., Yakubailik Ī.Å. Monitoring of land surface temperature in Krasnoyarsk and its suburban area based on Landsat 8 satellite data. Journal of Siberian Federal University. Engineering & Technologies. 2018; 11(8):934945.
24. Chena X., Zhanga Y. Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society. 2017; (32):8799.
25. Zhang Y., Odeh I.O.A., Han C. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a subpixel image analysis. International Journal of Applied Earth Observation and Geoinformation. 2009; 11(4):256264. DOI:10.1016/j.jag.2009.03.001.
26. Guha S., Govil H., Gill N., Dey A. A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quaternary International. 2021; (575576):249258. DOI:10.1016/j.quaint.2020.06.041. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1040618220303530?via%3Dihub.
27. Jamei Y., Rajagopalan P., Sun Q.C. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of the Total Environment. 2019; (659):13351351.
28. Sekertekin A., Zadbagher E. Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators. 2021; (122):107230.
29. Son N.T., Chen C.F., Chen C.R. Urban expansion and its impacts on local temperature in San Salvador, El Salvador. Urban Climate. 2020; (32):100617. DOI:10.1016/j.uclim.2020.100617.
30. Kaplan G., Avdan U., Avdan Z.Y. Urban heat island analysis using the Landsat 8 satellite data: a case study in Skopje, Macedonia. MDPI Proceedings. 2018; 2(7):358. DOI:10.3390/ecrs2-05171.
31. Taoufik M., Laghlimi M., Fekri A. Comparison of land surface temperature before, during and after the Covid 19 lockdown using Landsat imagery: a case study of Casablanca city, Morocco. Geomatics and Environmental Engineering. 2021; 15(2):105120.
32. Baldina E.A., Grishchenko M.Y. Object oriented analysis of multi-temporal thermal infrared images. South-Eastern European Journal of Earth Observation and Geomatics. 2014; 3(2S):415418.
33. Varentsov M.I., Konstantinov P.I., Samsonov T.E., Repina I.A. Investigation of the urban heat island phenomenon during polar night based on experimental measurements and remote sensing of Norilsk city. Current Problems in Remote Sensing of the Earth from Space. 2015; 11(4):329337.
34. Grishchenko M.Yu., Varentsov M.I., Mikhailyukova P.G. Analysis of the heat island of Moscow using space images of different spatial resolution and climate modeling results. Regional Problems of Remote Sensing of the Earth: Materials of the VI International Scientific Conference. Krasnoyarsk, September 1013, 2019. Krasnoyarsk: SibFU; 2019: 217219. (In Russ.)
35. Varentsov M.I., Grishchenko M.Yu., Wouters H. Simultaneous assessment of the summer urban heat island in moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling. Geography, Environment, Sustainability. 2019; 12(4):7495. DOI:10.24057/2071-9388-2019-10.
36. Grishchenko M.Yu., Ermilova Yu.V. Mapping of the built-up areas of Russian Arctic biggest cities using satellite imagery of various spatial resolution. Geodesy and Cartography. 2018; 79(3):2334. (In Russ.)
37. Proshin A.A., Loupian E.A., Balashov I.V., Kashnitskiy A.V., Bourtsev M.A. Unified satellite data archive management platform for remote monitoring systems development. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa. 2016; 13(3):927. (In Russ.)
38. Klimanova O.A., Illarionova O.I. Green infrastructure indicators for urban planning: applying the integrated approach for Russian largest cities. Geography, Environment, Sustainability. 2020; 13(1):251259. DOI:10.24057/2071-9388-2019-123.
39. Klimanova O.A., Kolbowskiy E.Yu., Illarionova O.A. The ecological framework of Russian major cities: spatial structure, territorial planning and main problems of development. Vestnik of Saint-Petersburg University. Earth Sciences. 2018; 63(2):127146. DOI:10.21638/11701/spbu07.2018.201. Available at: https://escjournal.spbu.ru/article/view/1123. (In Russ.)
40. Parastatidis D., Mitraka Z., Chrysoulakis N., Abrams M. Online global land surface temperature estimation from Landsat. Remote Sensing. 2017; (9):1208.
41. Jim´enez-Mu˜noz J.C., Sobrino J.A., Skokovic D., Mattar C., Cristobal J. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters. 2014; (11):18401843.
42. Sobrino J.A., Jim´enez-Mu˜noz J.C., S`oria G., Romaguera M., Guanter L., Moreno J., Member A. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing. 2008; 46(2):316327. DOI:10.1109/TGRS.2007.904834. Available at: https://www.scirp.org/reference/referencespapers.aspx?referenceid=2219077.
43. Al-Hattab M., Amany S.M., Lamyaa Gamal El-deen Taha. Monitoring and assessment of urban heat islands over the Southern region of Cairo Governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences. 2018; 21(3):311323. Available at: https://www.researchgate. net/publication/320211208_Monitoring_and_Assessment_of_Urban_Heat_Islands_over_the_Southern_Region_of_Cairo_Governorate_Egypt.
44. Mamash E.A., Pestunov I.A., Chubarov D.L. Spatiotemporal analysis of the land surface temperature distribution over the territory of Novosibirsk city based on Landsat data. E3S Web of Conferences. 2020; (223):03011. Bibliography link: Mamash E.A., Pestunov I.A., Sinyavskiy Y.N. Analysis of patterns in the distribution of the temperature fields for large industrial cities of Siberia according to Landsat-8 data // Computational technologies. 2022. V. 27. ¹ 3. P. 95-111
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