A hybrid machine learning approach to investigate the changing urban thermal environment by dynamic land cover transformation: A case study of Suwon, republic of Korea

Siwoo Lee1, Cheolhee Yoo2, Jungho Im1, Dongjin Cho1, Yeonsu Lee1, Dukwon Bae1
1Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Tài liệu tham khảo

Alhichri, 2021, Classification of remote sensing images using EfficientNet-B3 CNN model with attention, IEEE Access, 9, 14078, 10.1109/ACCESS.2021.3051085

Alqurashi, 2016, Urban land cover change modelling using time-series satellite images: a case study of urban growth in five cities of Saudi Arabia, Remote Sens. (Basel), 8, 838, 10.3390/rs8100838

Anderson, 1976, Vol. 964

Bechtel, 2012, Downscaling land surface temperature in an urban area: a case study for Hamburg, Germany. Remote Sens., 4, 3184, 10.3390/rs4103184

Bechtel, 2015, Mapping local climate zones for a worldwide database of the form and function of cities, ISPRS Int. J. Geo Inf., 4, 199, 10.3390/ijgi4010199

Bhatta, 2009, Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. Int. J. Remote Sens., 30, 4733, 10.1080/01431160802651967

Bonafoni, 2016, Downscaling of Landsat and MODIS land surface temperature over the heterogeneous urban area of Milan, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 2019, 10.1109/JSTARS.2016.2514367

Bounoua, 2018, Mapping urbanization in the United States from 2001 to 2011, Appl. Geogr., 90, 123, 10.1016/j.apgeog.2017.12.002

Cao, 2022, Seasonal and diurnal surface urban heat islands in China: an investigation of driving factors with three-dimensional urban morphological parameters, GISci. Remote Sensing, 59, 1121, 10.1080/15481603.2022.2100100

Chen, 2020, Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea, Environ. Res. Lett., 15, 054016, 10.1088/1748-9326/ab7e51

Chen, 2017, March). Modeling the urban thermal environment distributions in Taipei Basin using Local Climate Zone (LCZ), 1

Cheval, 2020, Exploratory analysis of cooling effect of urban lakes on land surface temperature in Bucharest (Romania) using Landsat imagery, Urban Clim., 34, 100696, 10.1016/j.uclim.2020.100696

Cho, 2020, Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas., Earth and Space Sci., 7, 10.1029/2019EA000740

Demuzere, 2022, A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835, 10.5194/essd-14-3835-2022

Dong, 2020, Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data, ISPRS J. Photogramm. Remote Sens., 169, 44, 10.1016/j.isprsjprs.2020.08.018

Feyisa, 2016, Locally optimized separability enhancement indices for urban land cover mapping: exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia, Remote Sens. Environ., 175, 14, 10.1016/j.rse.2015.12.026

Fu, 2016, A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery, Remote Sens. Environ., 175, 205, 10.1016/j.rse.2015.12.040

Guidici, 2017, One-Dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area, California. Remote Sens., 9, 629, 10.3390/rs9060629

Guo, 2022, Strengthening of surface urban heat island effect driven primarily by urban size under rapid urbanization: national evidence from China, GISci. Remote Sens., 59, 2127, 10.1080/15481603.2022.2147301

Han, 2022, Using Local Climate Zones to investigate Spatio-temporal evolution of thermal environment at the urban regional level: a case study in Xi'an, China. Sustainable Cities and Soc., 76, 103495, 10.1016/j.scs.2021.103495

Ho, 2014, Mapping maximum urban air temperature on hot summer days, Remote Sens. Environ., 154, 38, 10.1016/j.rse.2014.08.012

Hutengs, 2016, Downscaling land surface temperatures at regional scales with random forest regression, Remote Sens. Environ., 178, 127, 10.1016/j.rse.2016.03.006

Irons, 2012, The next Landsat satellite: the Landsat data continuity mission, Remote Sens. Environ., 122, 11, 10.1016/j.rse.2011.08.026

Jimenez-Munoz, 2009, Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data, IEEE Trans. Geosci. Remote Sens., 47, 339, 10.1109/TGRS.2008.2007125

Jiménez-Muñoz, 2014, Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data, IEEE Geosci. Remote Sens. Lett., 11, 1840, 10.1109/LGRS.2014.2312032

Kattenborn, 2021, Review on Convolutional Neural Networks (CNN) in vegetation remote sensing, ISPRS J. Photogramm. Remote Sens., 173, 24, 10.1016/j.isprsjprs.2020.12.010

Kim, 2018, Convolutional neural network-based land cover classification using 2-D spectral reflectance curve graphs with multitemporal satellite imagery, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 4604, 10.1109/JSTARS.2018.2880783

Lee, Y., Lee, S., Im, J., & Yoo, C. (2021). Analysis of surface urban heat island and land surface temperature using deep learning based local climate zone classification: A case study of suwon and daegu, korea.

Li, 2022, Cost-effective land cover classification for remote sensing images, J. Cloud Comput., 11, 1, 10.1186/s13677-022-00335-0

Liu, 2011, Urban heat island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong, Remote Sens. (Basel), 3, 1535, 10.3390/rs3071535

Ma, 2021, Advances of local climate zone mapping and its practice using object-based image analysis, Atmos., 12, 1146, 10.3390/atmos12091146

Masolele, 2021, Spatial and temporal deep learning methods for deriving land-use following deforestation: a pan-tropical case study using Landsat time series, Remote Sens. Environ., 264, 112600, 10.1016/j.rse.2021.112600

McNairn, 2009, Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories, ISPRS J. Photogramm. Remote Sens., 64, 434, 10.1016/j.isprsjprs.2008.07.006

Mishra, 2016, Continuous calibration improvement in solar reflective bands: landsat 5 through Landsat 8, Remote Sens. Environ., 185, 7, 10.1016/j.rse.2016.07.032

Mo, 2021, A review of reconstructing remotely sensed land surface temperature under cloudy conditions, Remote Sens. (Basel), 13, 2838, 10.3390/rs13142838

Mohammad, 2021, Quantifying diurnal and seasonal variation of surface urban heat island intensity and its associated determinants across different climatic zones over Indian cities, GIScience & Remote Sensing, 58, 955, 10.1080/15481603.2021.1940739

Oke, T. R. 1995. The heat island of the urban boundary layer: characteristics, causes and effects. Wind climate in cities, 81–107.

Qiu, 2020, Multilevel feature fusion-based CNN for local climate zone classification from sentinel-2 images: benchmark results on the So2Sat LCZ42 dataset, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 13, 2793, 10.1109/JSTARS.2020.2995711

Reddy, K. N. 1996. Urban redevelopment: a study of high-rise buildings. Concept publishing company.

Rwanga, 2017, Accuracy assessment of land use/land cover classification using remote sensing and GIS, Int. J. Geosci., 08, 611, 10.4236/ijg.2017.84033

Sera, 2019, How urban characteristics affect vulnerability to heat and cold: a multi-country analysis, Int. J. Epidemiol., 48, 1101, 10.1093/ije/dyz008

Stathopoulou, 2009, Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation, Remote Sens. Environ., 113, 2592, 10.1016/j.rse.2009.07.017

Stewart, 2012, Local climate zones for urban temperature studies, Bull. Am. Meteorol. Soc., 93, 1879, 10.1175/BAMS-D-11-00019.1

Tomlinson, 2011, Remote sensing land surface temperature for meteorology and climatology: a review, Meteorol. Appl., 18, 296, 10.1002/met.287

Trigo, 2008, An assessment of remotely sensed land surface temperature, J. Geophys. Res. Atmos., 113, 10.1029/2008JD010035

Unger, 2014, Local Climate Zone mapping using GIS methods in Szeged, Hungarian Geographical Bulletin, 63, 29, 10.15201/hungeobull.63.1.3

Verburg, 2011, Challenges in using land use and land cover data for global change studies, Glob. Chang. Biol., 17, 974, 10.1111/j.1365-2486.2010.02307.x

Wang, 2018, Assessing local climate zones in arid cities: the case of Phoenix, Arizona and Las Vegas, Nevada, ISPRS J. Photogramm. Remote Sens., 141, 59, 10.1016/j.isprsjprs.2018.04.009

Xian, 2021, The effects of urban land cover dynamics on urban heat Island intensity and temporal trends, GIScience & Remote Sensing, 58, 501, 10.1080/15481603.2021.1903282

Xu, 2020, Downscaling ASTER land surface temperature over urban areas with machine learning-based area-to-point regression Kriging, Remote Sens. (Basel), 12, 1082, 10.3390/rs12071082

Yoo, 2019, Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS J. Photogramm. Remote Sens., 157, 155, 10.1016/j.isprsjprs.2019.09.009

Zawadzka, 2020, Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data, Int. J. Digital Earth, 13, 899, 10.1080/17538947.2019.1593527

Zhang, 2013, Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China, Appl. Geogr., 44, 121, 10.1016/j.apgeog.2013.07.021

Zhang, 2014, The cooling effect of urban green spaces as a contribution to energy-saving and emission-reduction: a case study in Beijing, China, Build. Environ., 76, 37, 10.1016/j.buildenv.2014.03.003

Zhao, 2020, Use of local climate zones to investigate surface urban heat islands in texas, GIScience & Remote Sensing, 57, 1083, 10.1080/15481603.2020.1843869

Zhao, 2022, Use of local climate zones to assess the spatiotemporal variations of urban vegetation phenology in Austin, Texas, USA, GIScience & Remote Sensing, 59, 393, 10.1080/15481603.2022.2033485

Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., ... & Wang, Y. 2019. So2Sat LCZ42: A benchmark dataset for global local climate zones classification. arXiv preprint arXiv:1912.12171.