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Impacts of urbanization on heat in Ho Chi Minh, southern Vietnam using U-Net model and remote sensing

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Abstract

Green space in cities has been reducing rapidly due to the intensive urban expansion, which contributes to surface temperature growth, leading to numerous challenges in management and planning. This work applied U-Net and cellular automaton-artificial neural network (CA-ANN) models to classify and predict the land use and land cover (LULC) change in Ho Chi Minh, the largest city in Vietnam. The present study indicates that the LULC in this city has changed remarkedly for 27 years when the urban green space (UGS) performed a gradual decline. The urban expansion is mainly in the north and northeast direction. The UGS and temperature are negatively correlated since the UGS decline contributes to a temperature increase from 1995 to 2022 in the study area. The temperature is high in all urban areas, being highest in industrial zones or areas with manufacturing activities. There is a different picture of temperature between the inner-city area and the other areas according to the density of green spaces. Based on the CA-ANN model, this work can predict the LULC change in 2035 as the urban land will increase, but the UGS will reduce and the expansion direction being to the east, northeast and northwest. Our findings suggest that remote sensing and U-Net models may be used to investigate urban heat islands and urbanization, as well as to analyze geographical and temporal changes. These results would be helpful for planners and managers to pay more attention to long-term plans for sustainable urban development and management in this city.

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References

  • Abbas AW, Minallh N, Ahmad N et al (2016) K-Means and ISODATA clustering algorithms for landcover classification using remote sensing. Sindh Univ Res J-SURJ Sci Ser 48(2):315–318

    Google Scholar 

  • AlDousari AE, Kafy A-A, Saha M et al (2022) Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustain Cities Soc 86:104107

    Article  Google Scholar 

  • Ali S, Patnaik S, Madguni O (2017) Microclimate land surface temperatures across urban land use/land cover forms. Global J Environ Sci Manag 3(3):231–242

    Google Scholar 

  • Altarez RDD, Apan A, Maraseni T (2022) Deep learning U-Net classification of Sentinel-1 and 2 fusions effectively demarcates tropical montane forest’s deforestation. Remote Sens Appl Soc Environ 29:100887

    Google Scholar 

  • Bhosle K, Musande V (2019) Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. J Indian Soc Remote Sens 47:1949–1958

    Article  Google Scholar 

  • Cao H, Liu J, Chen J et al (2019) Spatiotemporal patterns of urban land use change in typical cities in the greater mekong subregion (GMS). Remote Sens 11:801

    Article  Google Scholar 

  • Casper JK (2010) Greenhouse gases: worldwide impacts. Infobase Publishing, Newyork

    Google Scholar 

  • Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903

    Article  Google Scholar 

  • Chen Z, Wang C, Li J, Xie N, Han Y, Du J (2021) Reconstruction bias U-Net for road extraction from optical remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2284–2294

    Article  Google Scholar 

  • de Faria PL, de Lucena AJ, Rotunno Filho OC, de Almeida França JR (2018) The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data. Int J Appl Earth Obs Geoinformation 64:104–116

    Article  Google Scholar 

  • Deng JS, Wang K, Deng YH, Qi GJ (2008) PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int J Remote Sens 29:4823–4838

    Article  Google Scholar 

  • Do ANT, Tran HD (2022) Potential application of artificial neural networks for analyzing the occurrences of fish larvae and juveniles in an estuary in northern Vietnam. Aquat Ecol. https://doi.org/10.1007/s10452-022-09959-5

    Article  Google Scholar 

  • Do ANT, Tran HD, Ashley M (2022) Employing a novel hybrid of GA-ANFIS model to predict distribution of whiting fish larvae and juveniles from tropical estuaries in the context of climate change. Ecol Inform 71:101780. https://doi.org/10.1016/j.ecoinf.2022.101780

    Article  Google Scholar 

  • Do ANT, Tran HD, Ashley M, Nguyen AT (2022) Monitoring landscape fragmentation and aboveground biomass estimation in Can Gio Mangrove biosphere reserve over the past 20 years. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2022.101743

    Article  Google Scholar 

  • Do TAT, Do ANT, Tran HD (2022) Quantifying the spatial pattern of urban expansion trends in the period 1987–2022 and identifying areas at risk of flooding due to the impact of urbanization in Lao Cai city. Ecol Inform. https://doi.org/10.1016/j.ecoinf.2022.101912

    Article  Google Scholar 

  • Dos Santos S, Adams EA, Neville G et al (2017) Urban growth and water access in sub-Saharan Africa: Progress, challenges, and emerging research directions. Sci Total Environ 607:497–508

    Article  Google Scholar 

  • Duda T, Canty M (2002) Unsupervised classification of satellite imagery: choosing a good algorithm. Int J Remote Sens 23:2193–2212

    Article  Google Scholar 

  • Ejaro SP, Abubakar A (2013) Impact of rapid urbanization on sustainable development of Nyanya, Federal Capital Territory, Abuja, Nigeria. Res J Soc Sci Manag 3:31–44

    Google Scholar 

  • Harmay NSM, Kim D, Choi M (2021) Urban heat island associated with land use/land cover and climate variations in Melbourne. Australia Sustain Cities Soc 69:102861

    Article  Google Scholar 

  • Huang Z, Du X (2018) Urban land expansion and air pollution: evidence from China. J Urban Plan Dev 144:05018017

    Article  Google Scholar 

  • Kafy A-A, Al Rakib A, Fattah MA et al (2021) Impact of vegetation cover loss on surface temperature and carbon emission in a fastest-growing city, Cumilla. Build Environ, Bangladesh, p 108573

    Google Scholar 

  • Kantakumar LN, Neelamsetti P (2015) Multi-temporal land use classification using hybrid approach. Egypt J Remote Sens Space Sci 18:289–295

    Google Scholar 

  • Kazak JK (2018) The use of a decision support system for sustainable urbanization and thermal comfort in adaptation to climate change actions—the case of the Wroc\law larger urban zone (Poland). Sustainability 10:1083

    Article  Google Scholar 

  • Khan I, Javed T, Khan A et al (2019) Impact assessment of land use change on surface temperature and agricultural productivity in Peshawar-Pakistan. Environ Sci Pollut Res 26:33076–33085

    Article  Google Scholar 

  • Li X, Yeh AG-O (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16:323–343

    Article  Google Scholar 

  • Liou Y-A, Nguyen AK, Li M-H (2017) Assessing spatiotemporal eco-environmental vulnerability by Landsat data. Ecol Indic 80:52–65

    Article  Google Scholar 

  • Liu Y, Hiyama T, Yamaguchi Y (2006) Scaling of land surface temperature using satellite data: a case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sens Environ 105:115–128

    Article  Google Scholar 

  • Mahanta NR, Samuel AK (2020) Study of Land Surface Temperature (LST) and Land Cover for Urban Heat Island (UHI) Analysis in Dubai. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). IEEE, pp 1285–1288

  • Maimaitiyiming M, Ghulam A, Tiyip T et al (2014) Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS J Photogramm Remote Sens 89:59–66

    Article  Google Scholar 

  • Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29:617–663

    Article  Google Scholar 

  • Morelli VG, Rontos K, Salvati L (2014) Between suburbanisation and re-urbanisation: revisiting the urban life cycle in a mediterranean compact city. Urban Res Pract 7:74–88

    Article  Google Scholar 

  • Naghibi F, Delavar MR, Pijanowski B (2016) Urban growth modeling using cellular automata with multi-temporal remote sensing images calibrated by the artificial bee colony optimization algorithm. Sensors 16:2122. https://doi.org/10.3390/s16122122

    Article  Google Scholar 

  • Neinavaz E, Skidmore AK, Darvishzadeh R (2020) Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. Int J Appl Earth Obs Geoinformation 85:101984. https://doi.org/10.1016/j.jag.2019.101984

    Article  Google Scholar 

  • Payan V, Royer A (2004) Analysis of temperature emissivity separation (TES) algorithm applicability and sensitivity. Int J Remote Sens 25:15–37

    Article  Google Scholar 

  • Perumal K, Bhaskaran R (2010) Supervised classification performance of multispectral images. ArXiv Prepr ArXiv10024046

  • Pramanik S, Punia M (2020) Land use/land cover change and surface urban heat island intensity: source–sink landscape-based study in Delhi, India. Environ Dev Sustain 22:7331–7356

    Article  Google Scholar 

  • Rangarajan S (2022) Predicting the future land use and land cover changes for Bhavani basin Tamil Nadu India using QGIS MOLUSCE plugin. Environ Sci Poll Res 29(57):86337–86348

    Article  Google Scholar 

  • Reis S (2008) Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors 8:6188–6202

    Article  Google Scholar 

  • Ren G, Zhou Y, Chu Z et al (2008) Urbanization effects on observed surface air temperature trends in North China. J Clim 21:1333–1348

    Article  Google Scholar 

  • Schneider A, Logan KE, Kucharik CJ (2012) Impacts of urbanization on ecosystem goods and services in the US Corn Belt. Ecosystems 15:519–541

    Article  CAS  Google Scholar 

  • Simperler L, Ertl T, Matzinger A (2020) Spatial compatibility of implementing nature-based solutions for reducing urban heat islands and stormwater pollution. Sustainability 12:5967

    Article  CAS  Google Scholar 

  • Solórzano JV, Mas JF, Gao Y, Gallardo-Cruz JA (2021) Land use land cover classification with U-net: advantages of combining sentinel-1 and sentinel-2 imagery. Remote Sens 13:3600

    Article  Google Scholar 

  • Son N-T, Chen C-F, Chen C-R et al (2017) Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data. Sustain Cities Soc 30:150–161

    Article  Google Scholar 

  • Su Z, Li W, Ma Z, Gao R (2022) An improved U-Net method for the semantic segmentation of remote sensing images. Appl Intell 52:3276–3288

    Article  Google Scholar 

  • Ullah S, Ahmad K, Sajjad RU et al (2019a) Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region. J Environ Manage 245:348–357

    Article  Google Scholar 

  • Ullah S, Tahir AA, Akbar TA et al (2019b) Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability 11:5492. https://doi.org/10.3390/su11195492

    Article  Google Scholar 

  • Van Pham T, Do TAT, Tran HD, Do ANT (2023) Assessing the impact of ecological security and forest fire susceptibility on carbon stocks in Bo Trach, district Quang Binh province, Vietnam. Ecol Inform 74:101962. https://doi.org/10.1016/j.ecoinf.2022.101962

    Article  Google Scholar 

  • Van TT (2008) Research on the effect of urban expansion on agricultural land in Ho Chi Minh City by using remote sensing method. VNU J Sci Earth Environ Sci 24:104–111

    Google Scholar 

  • Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37:360–363

    Google Scholar 

  • Wang J (2022) Landscape classification method using improved U-net model in remote sensing image ecological environment monitoring system. J Environ Public Health 2022:1–12

    Google Scholar 

  • Weng Q (2001) A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int J Remote Sens 22:1999–2014

    Google Scholar 

  • Wenger R, Puissant A, Weber J et al (2022) U-Net feature fusion for multi-class semantic segmentation of urban fabrics from Sentinel-2 imagery: an application on Grand Est Region, France. Int J Remote Sens 43:1983–2011

    Article  Google Scholar 

  • White R, Engelen G (1993) Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environ Plan A 25:1175–1199

    Article  Google Scholar 

  • Xiao J, Shen Y, Ge J et al (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75:69–80

    Article  Google Scholar 

  • Xiao Y, Su X, Yuan Q, Liu D, Shen H, Zhang L (2022a) Satellite video super-resolution via multiscale deformable convolution alignment and temporal grouping projection. IEEE Trans Geosci Remote Sens 60:1–19. https://doi.org/10.1109/TGRS.2021.3107352

    Article  Google Scholar 

  • Xiao Y, Wang Y, Yuan Q, He J, Zhang L (2022) Generating a long-term (2003–2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS). Sci Total Environ 848:157747. https://doi.org/10.1016/j.scitotenv.2022.157747

    Article  CAS  Google Scholar 

  • Xiao Y, Yuan Q, Jiang K, He J, Wang Y, Zhang L (2023) From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution. Inf Fusion 96:297–311. https://doi.org/10.1016/j.inffus.2023.03.021

    Article  Google Scholar 

  • Yan C, Fan X, Fan J, Wang N (2022) Improved U-Net remote sensing classification algorithm based on multi-feature fusion perception. Remote Sens 14:1118. https://doi.org/10.3390/rs14051118

    Article  Google Scholar 

  • Youme O, Bayet T, Dembele JM, Cambier C (2021) Deep learning and remote sensing: detection of dumping waste using UAV. Procedia Comput Sci 185:361–369

    Article  Google Scholar 

  • Yun-hao C, Jie W, Xiao-bing LI (2002) A study on urban thermal field in summer based on satellite remote sensing. Remote Sens Land Resour 14:55–59

    Google Scholar 

  • Zhang J, Foody GM (1998) A fuzzy classification of sub-urban land cover from remotely sensed imagery. Int J Remote Sens 19:2721–2738

    Article  Google Scholar 

  • Zhang H, Qi Z, Ye X et al (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 44:121–133

    Article  CAS  Google Scholar 

  • Zhang P, Bounoua L, Imhoff ML et al (2014) Comparison of MODIS land surface temperature and air temperature over the continental USA meteorological stations. Can J Remote Sens 40:110–122

    Google Scholar 

  • Zhao Z-Q, He B-J, Li L-G et al (2017) Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: case study of Shenyang, China. Energy Build 155:282–295

    Article  Google Scholar 

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Acknowledgements

The authors thank anonymous reviewers for their helpful comments and suggestions, which improved this manuscript. We would like to thank Dr. Ngoc Minh Nguyen for his thoughtful comments on the revised manuscript.

Funding

This research did not receive any specific grants from funding agencies in public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Vietnam National University, Ha Noi University of Science, Thanh Xuan, Hanoi, Vietnam

    A. N. T. Do

  2. Hanoi National University of Education, Cau Giay, Hanoi, Vietnam

    H. D. Tran

  3. Hanoi University of Natural Resources and Environment, Bac Tu Liem, Hanoi, Vietnam

    T. A. T. Do

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  1. A. N. T. Do
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  2. H. D. Tran
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Contributions

ANTD was contributed to methodology, formal analysis, writing—review and editing, validation. HDT was contributed to data curation, formal analysis, writing—review and editing. TATD was contributed to methodology, investigation, writing—original draft.

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Correspondence to H. D. Tran.

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Editorial responsibility: U.W. Tang.

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Do, A.N.T., Tran, H.D. & Do, T.A.T. Impacts of urbanization on heat in Ho Chi Minh, southern Vietnam using U-Net model and remote sensing. Int. J. Environ. Sci. Technol. 21, 3005–3020 (2024). https://doi.org/10.1007/s13762-023-05118-x

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