{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:37:47Z","timestamp":1775803067545,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Moscow State University of Civil Engineering and the Ministry of Education of Vietnam","award":["B2022-MDA-12"],"award-info":[{"award-number":["B2022-MDA-12"]}]},{"name":"Moscow State University of Civil Engineering and the Ministry of Education of Vietnam","award":["2190\/QD-BG\u0110T"],"award-info":[{"award-number":["2190\/QD-BG\u0110T"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The expansion of construction zones, transportation, and utilities for industry and high-tech areas due to human activities has caused the deterioration of the natural ecological environment. As cities face problems related to the surface urban heat island (SUHI) effect and environmental pollution, there is an urgent need to develop new methods for the ecological\u2013microclimatic assessment and structural\u2013functional planning of urban areas. The main goal of this study was to demonstrate the evolution of the surface urban heat island (SUHI) effect in Moscow over a long period and to determine the interaction between SUHIs and urban pollution islands (UPIs) using a geospatial analysis platform while optimizing vegetation classification with machine learning. Additionally, we are creating a digital database for modeling the sustainability of cities on the GEE platform using cloud computing. This study used cloud computing and remote sensing image analysis platforms for a 17-year temporal-series ecological\u2013microclimatic assessment, which provided a sequence of values describing the ongoing process of changes in the ecological conditions of Moscow over time. Combining machine learning with the random forest algorithm (RF) improved vegetation classification accuracy while reducing computation time. The study findings demonstrated how the SUHI affected Moscow\u2019s territory and showed the urban areas significantly impacted by this phenomenon. The locations of surface urban heat islands in Moscow and areas affected by SUHI and UPI were identified using numerical modeling of the urban thermal field variance index (UTFVI). From the findings, we identified the need to develop a new method for obtaining geospatial data for assessing the interaction between UPIs and SUHIs using cloud computing and mathematical data models.<\/jats:p>","DOI":"10.3390\/rs15133294","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:45:11Z","timestamp":1687913111000},"page":"3294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Technique for Generating Preliminary Satellite Data to Evaluate SUHI Using Cloud Computing: A Case Study in Moscow, Russia"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4164-7452","authenticated-orcid":false,"given":"Minh Tuan","family":"Le","sequence":"first","affiliation":[{"name":"Department of Urban Planning, Moscow State University of Civil Engineering, 26, Yaroslavskoye Shosse, Moscow 129337, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0518-6521","authenticated-orcid":false,"given":"Natalia","family":"Bakaeva","sequence":"additional","affiliation":[{"name":"Department of Urban Planning, Moscow State University of Civil Engineering, 26, Yaroslavskoye Shosse, Moscow 129337, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0168-1273(87)10004-9","article-title":"Chapter 62 High-energy spectorscopy of lanthanide materials\u2014An overview","volume":"Volume 10","author":"Baer","year":"1987","journal-title":"Handbook on the Physics and Chemistry of Rare Earths"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Whitman, R.T., Park, M.B., Ambrose, S.M., and Hoel, E.G. (2014, January 4). Spatial indexing and analytics on Hadoop. Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Dallas, TX, USA.","DOI":"10.1145\/2666310.2666387"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, J., Wu, J., and Sarwat, M. (2015, January 3\u20136). GeoSpark: A cluster computing framework for processing large-scale spatial data. Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Washington, DC, USA.","DOI":"10.1145\/2820783.2820860"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hughes, J.N., Annex, A., Eichelberger, C.N., Fox, A., Hulbert, A., and Ronquest, M. (2015, January 20\u201321). GeoMesa: A distributed architecture for spatio-temporal fusion. Proceedings of the Geospatial Informatics, Fusion, and Motion Video Analytics V, Baltimore, MD, USA.","DOI":"10.1117\/12.2177233"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.3390\/rs4092595","article-title":"Overcoming limitations with landsat imagery for mapping of peat swamp forests in sundaland","volume":"4","author":"Wijedasa","year":"2012","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13485","DOI":"10.3390\/rs71013485","article-title":"Comparison of the continuity of vegetation indices derived from Landsat 8 OLI and Landsat 7 ETM+ data among different vegetation types","volume":"7","author":"She","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/2248487.2150982","article-title":"Clearing the clouds","volume":"47","author":"Ferdman","year":"2012","journal-title":"ACM SIGPLAN Not."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1080\/01431161.2015.1035410","article-title":"A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada\u2019s Arctic","volume":"36","author":"He","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Faridatul, M.I., and Wu, B. (2018). Automatic classification of major urban land covers based on novel spectral indices. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7120453"},{"key":"ref_16","unstructured":"Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R.E. (2006, January 6\u20138). BigTable: A distributed storage system for structured data. Proceedings of the OSDI 2006-7th USENIX Symposium on Operating Systems Design and Implementation, Seattle WA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2491245","article-title":"Spanner: Google\u2019s Globally Distributed Database","volume":"31","author":"Corbett","year":"2013","journal-title":"ACM Trans. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., and Wilkes, J. (2015, January 21\u201324). Large-scale cluster management at Google with Borg. Proceedings of the 10th European Conference on Computer Systems, EuroSys, Bordeaux France.","DOI":"10.1145\/2741948.2741964"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1145\/1809028.1806638","article-title":"FlumeJava","volume":"45","author":"Chambers","year":"2010","journal-title":"ACM SIGPLAN Not."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., and Goldberg-Kidon, J. (2010, January 6\u201310). Google fusion tables: Web-centered data management and collaboration. Proceedings of the ACM SIGMOD International Conference on Management of Data, Indianapolis, IN, USA.","DOI":"10.1145\/1807167.1807286"},{"key":"ref_21","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Deng, W., Huang, Z., Zhang, J., and Xu, J. (2021, January 15\u201317). A Data Mining Based System for Transaction Fraud Detection. Proceedings of the 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, ICCECE, Guangzhou, China.","DOI":"10.1109\/ICCECE51280.2021.9342376"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.catena.2017.11.022","article-title":"Prediction of the landslide susceptibility: Which algorithm, which precision?","volume":"162","author":"Pourghasemi","year":"2018","journal-title":"Catena"},{"key":"ref_24","first-page":"62","article-title":"Influence of the Effect of the Urban Heat Island on the Cities Sustainable Development","volume":"10","author":"Shukurov","year":"2020","journal-title":"Urban Constr. Arch."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.3390\/rs3071535","article-title":"Urban heat island analysis using the landsat TM data and ASTER Data: A case study in Hong Kong","volume":"3","author":"Liu","year":"2011","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1016\/j.atmosenv.2005.12.051","article-title":"The ion chemistry, seasonal cycle, and sources of PM2.5 and TSP aerosol in Shanghai","volume":"40","author":"Wang","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, Y., Zou, J., Yang, W., and Li, C.Q. (2018). A review of recent advances in research on PM2.5 in China. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15030438"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"D05109","DOI":"10.1029\/2010JD014581","article-title":"A simple relationship between volcanic sulfate aerosol optical depth and surface temperature change simulated in an atmosphere-ocean general circulation model","volume":"116","author":"Harris","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00703-008-0302-y","article-title":"Simulation of direct effects of black carbon aerosol on temperature and hydrological cycle in Asia by a Regional Climate Model","volume":"100","author":"Wu","year":"2008","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.atmosenv.2013.10.052","article-title":"Optical properties and radiative forcing of urban aerosols in Nanjing, China","volume":"83","author":"Zhuang","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18998","DOI":"10.1038\/srep18998","article-title":"Enhanced air pollution via aerosol-boundary layer feedback in China","volume":"6","author":"Kerminen","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15760","DOI":"10.1038\/s41598-017-15909-1","article-title":"Severe Pollution in China Amplified by Atmospheric Moisture","volume":"7","author":"Tie","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6095","DOI":"10.1038\/s41598-018-24366-3","article-title":"New positive feedback mechanism between boundary layer meteorology and secondary aerosol formation during severe haze events","volume":"8","author":"Liu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.scitotenv.2018.04.254","article-title":"Interaction between urban heat island and urban pollution island during summer in Berlin","volume":"636","author":"Li","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"D03302","DOI":"10.1029\/2006JD007850","article-title":"Observed relationship between surface specific humidity, integrated water vapor, and longwave downward radiation at different altitudes","volume":"112","author":"Ruckstuhl","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_36","first-page":"94","article-title":"Contributing factors to downward longwave radiation at the Earth\u2019s surface","volume":"8","author":"Yamada","year":"2012","journal-title":"Sci. Online Lett. Atmos."},{"key":"ref_37","first-page":"3987","article-title":"Correlation analysis of the urban heat island effect and its impact factors in China","volume":"38","author":"Cao","year":"2017","journal-title":"Huan Jing Ke Xue"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1016\/j.atmosenv.2004.03.032","article-title":"New Directions: The growing urban heat and pollution \u201cisland\u201d effect-Impact on chemistry and climate","volume":"38","author":"Crutzen","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3390\/rs3010083","article-title":"Satellite-observed urbanization characters in Shanghai, China: Aerosols, urban heat Island effect, and land-atmosphere interactions","volume":"3","author":"Jin","year":"2011","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12509","DOI":"10.1038\/ncomms12509","article-title":"Urban heat islands in China enhanced by haze pollution","volume":"7","author":"Cao","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1016\/j.atmosenv.2004.04.021","article-title":"Suspended particulate matter and its relations to the urban climate in Dar es Salaam, Tanzania","volume":"38","author":"Jonsson","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2014.05.017","article-title":"Surface urban heat island in China\u2019s 32 major cities: Spatial patterns and drivers","volume":"152","author":"Zhou","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101493","DOI":"10.1016\/j.ecoinf.2021.101493","article-title":"Determination of urban pollution islands by using remote sensing technology in Moscow, Russia","volume":"67","author":"Bakaeva","year":"2022","journal-title":"Ecol. Inform."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:01:54Z","timestamp":1760126514000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133294"],"URL":"https:\/\/doi.org\/10.3390\/rs15133294","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]}}}