Predicting Urban Land Use and Mitigating Land Surface Temperature: Exploring the Role of Urban Configuration with Convolutional Neural Networks
Publication: Journal of Urban Planning and Development
Volume 150, Issue 3
Abstract
The objective of this research was to examine the influence of urban configuration on the mitigation of land surface temperature (LST) and the prediction of land use and land cover change through the utilization of convolutional neural network modeling. The results indicate that the formation of different urban heat island patterns is significantly influenced by both urban geometry and land use land cover (LULC) types. However, there is no significant correlation between these factors and LST across all configuration metrics. The associations between landscape configuration and land cover types exhibit variability contingent upon the particular forest cover categories under examination. Furthermore, the application of predictive LULC mapping reveals a divergent pattern, characterized by a rise in the overall extent of vegetation but a decline in the inner context of the Shiraz metropolitan area. The projected trajectory of built-up areas indicates a continued trend of urban expansion. The unique landscape patterns are a result of the distinct characteristics of each LULC. According to recommendations, to address the issue of mean LST, it is advisable for urban landscape planning to give priority to cohesion, density, and continuity while simultaneously minimizing fragmentation, variability, and complexity.
Practical Applications
This research provides valuable insights into the following aspects for urban planners, policymakers, and practitioners to address the following. (1) Selecting appropriate landscape metrics: this study identifies suitable landscape metrics to represent and interpret different landscape structures and land use land cover changes (LULCCs) over time. (2) Understanding regional variations: this research highlights that different landscape metrics have distinct effectiveness in different regions. This knowledge helps urban planners and policymakers to tailor their strategies and interventions based on specific regional characteristics, ensuring more effective mitigation of the urban heat island effect. (3) Different correlations with configuration metrics: this study reveals that the correlations between landscape configuration and LST differ in land cover types. (4) Anticipating future changes: this research utilizes machine learning models to predict future LULCC and landscape metrics. This information is valuable for urban planners and policymakers in anticipating and preparing for future urban expansion and changes in vegetation areas. It enables them to proactively design and implement strategies to manage the surface urban heat island effect. Urban planners and policymakers can utilize these insights to develop comprehensive strategies that integrate land-use design, landscape configuration, and urban form to mitigate the negative impacts of urbanization.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to express their sincere gratitude to Dr. Betsabeh Tanoori and Dr. Ehsan Sharifi for their invaluable contributions and support throughout the completion of this research.
References
Abedini, A., A. Khalili, and N. Asadi. 2020. “Urban sprawl evaluation using landscape metrics and black-and-white hypothesis (case study: Urmia City).” J. Indian Soc. Remote Sens. 48: 1021–1034. https://doi.org/10.1007/s12524-020-01132-5.
Amindin, A., S. Pouyan, H. R. Pourghasemi, S. Yousefi, and J. P. Tiefenbacher. 2021. “Spatial and temporal analysis of urban heat island using Landsat satellite images.” Environ. Sci. Pollut. Res. 28: 41439–41450. https://doi.org/10.1007/s11356-021-13693-0.
Allan, A., A. Soltani, M. H. Abdi, and M. Zarei. 2022. “Driving forces behind land use and land cover change: A systematic and bibliometric review.” Land 11 (8): 1222. https://doi.org/10.3390/land11081222.
Alphan, H., E. Karamanli, M. A, Derse, and C. Uslu. 2022. “Analyzing pattern features of urban/rural residential land use change: The case of the southern coast of Turkey.” Land Use Policy 122: 106348. https://doi.org/10.1016/j.landusepol.2022.106348.
Asgarian, A., B. J. Amiri, and Y. Sakieh. 2015. “Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach.” Urban Ecosyst. 18 (1): 209–222. https://doi.org/10.1007/s11252-014-0387-7.
Athukorala, D., and Y. Murayama. 2020. “Spatial variation of land use/cover composition and impact on surface urban heat island in a tropical Sub-Saharan city of Accra, Ghana.” Sustainability 12 (19): 7953. https://doi.org/10.3390/su12197953.
Atri, M., S. Nedae-Tousi, S. Shahab, and E. Solgi. 2021. “The effects of thermal-spatial behaviours of land covers on urban heat islands in semi-arid climates.” Sustainability 13 (24): 13824. https://doi.org/10.3390/su132413824.
Azhdari, A., A. Soltani, and M. Alidadi. 2018. “Urban morphology and landscape structure effect on land surface temperature: Evidence from Shiraz, a semi-arid city.” Sustainable Cities Soc. 41: 853–864. https://doi.org/10.1016/j.scs.2018.06.034.
Azizi, P., A. Soltani, F. Bagheri, S. Sharifi, and M. Mikaeili. 2022. “An integrated modelling approach to urban growth and land use/cover change.” Land 11 (10): 1715. https://doi.org/10.3390/land11101715.
Bagheri, B., and A. Soltani. 2023. “The spatio-temporal dynamics of urban growth and population in metropolitan regions of Iran.” Habitat Int. 136: 102797. https://doi.org/10.1016/j.habitatint.2023.102797.
Bao, T., X. Li, J. Zhang, Y. Zhang, and S. Tian. 2016. “Assessing the distribution of urban green spaces and its anisotropic cooling distance on urban heat island pattern in Baotou, China.” ISPRS Int. J. Geo-Inf. 5 (2): 12. https://doi.org/10.3390/ijgi5020012.
Basu, T., and A. Das. 2023. “Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India.” Sustainable Cities Soc. 90: 104373. https://doi.org/10.1016/j.scs.2022.104373.
Bindajam, A. A., J. Mallick, S. Talukdar, A. R. M. T. Islam, and S. Alqadhi. 2021. “Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: Past to future perspective.” Arabian J. Geosci. 14: 1–23. https://doi.org/10.1007/s12517-021-08251-4.
Cai, X., J. Yang, Y. Zhang, X. Xiao, and J. C. Xia. 2023. “Cooling island effect in urban parks from the perspective of internal park landscape.” Humanit. Social Sci. Commun. 10 (1): 1–12. https://doi.org/10.1057/s41599-022-01483-z.
Cao, L., P. Li, L. Zhang, and T. Chen. 2008. “Remote sensing image-based analysis of the relationship between urban heat island and vegetation fraction.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 37: 1379–1384.
Cao, Q., D. Yu, M. Georgescu, Z. Han, and J. Wu. 2015. “Impacts of land use and land cover change on regional climate: A case study in the agro-pastoral transitional zone of China.” Environ. Res. Lett. 10 (12): 124025. https://doi.org/10.1088/1748-9326/10/12/124025.
Chen, A., L. Yao, R. Sun, and L. Chen. 2014. “How many metrics are required to identify the effects of the landscape pattern on land surface temperature?” Ecol. Indic. 45: 424–433. https://doi.org/10.1016/j.ecolind.2014.05.002.
Chen, J., W. Zhan, P. Du, L. Li, J. Li, Z. Liu, F. Huang, J. Lai, and J. Xia. 2022. “Seasonally disparate responses of surface thermal environment to 2D/3D urban morphology.” Build. Environ. 214: 108928. https://doi.org/10.1016/j.buildenv.2022.108928.
Chen, Y., J. Yang, W. Yu, J. Ren, X. Xiao, and J. C. Xia. 2023. “Relationship between urban spatial form and seasonal land surface temperature under different grid scales.” Sustainable Cities Soc. 89: 104374. https://doi.org/10.1016/j.scs.2022.104374.
Chenary, K., A. Soltani, and A. Sharifi. 2023. “Street network patterns for mitigating urban heat islands in arid climates.” Int. J. Digital Earth 16 (1): 3145–3161. https://doi.org/10.1080/17538947.2023.2243901.
Connors, J. P., C. S. Galletti, and W. T. Chow. 2013. “Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona.” Landscape Ecol. 28: 271–283. https://doi.org/10.1007/s10980-012-9833-1.
Dadashpoor, H., P. Azizi, and M. Moghadasi. 2019. “Land use change, urbanization, and change in landscape pattern in a metropolitan area.” Sci. Total Environ. 655: 707–719. https://doi.org/10.1016/j.scitotenv.2018.11.267.
Dehghani, A., M. Alidadi, and A. Soltani. 2023. “Density and urban resilience, cross-section analysis in an Iranian metropolis context.” Urban Sci. 7 (1): 23. https://doi.org/10.3390/urbansci7010023.
Dehghani, A., and A. Soltani. 2024. “Site selection of car parking with the GIS-based fuzzy multi-criteria decision making.” Int. J. Inf. Technol. Decis. Making 23: 715–740. https://doi.org/10.1142/S0219622023500293.
Deilami, K., M. Kamruzzaman, and Y. Liu. 2018. “Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures.” Int. J. Appl. Earth Obs. Geoinf. 67: 30–42. https://doi.org/10.1016/j.jag.2017.12.009.
Ding, Q., T. Pan, T. Lin, and C. Zhang. 2022. “Urban land-cover changes in major cities in China from 1990 to 2015.” Int. J. Environ. Res. Public Health 19 (23): 16079. https://doi.org/10.3390/ijerph192316079.
Du, H., J. Ai, Y. Cai, H. Jiang, and P. Liu. 2019. “Combined effects of the surface urban heat island with landscape composition and configuration based on remote sensing: A case study of Shanghai, China.” Sustainability 11 (10): 2890. https://doi.org/10.3390/su11102890.
Essien, E., and S. Cyrus. 2019. “Detection of urban development in Uyo (Nigeria) using remote sensing.” Land 8 (6): 102. https://doi.org/10.3390/land8060102.
Estoque, R. C., Y. Murayama, and S. W. Myint. 2017. “Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia.” Sci. Total Environ. 577: 349–359. https://doi.org/10.1016/j.scitotenv.2016.10.195.
Fatemi, M., and M. Narangifard. 2019. “Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City.” Arabian J. Geosci. 12: 127. https://doi.org/10.1007/s12517-019-4259-6.
Floreano, I. X., and L. A. F. de Moraes. 2021. “Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil.” Environ. Monit. Assess. 193 (4): 239. https://doi.org/10.1007/s10661-021-09016-y.
Fu, J., K. Dupre, S. Tavares, D. King, and Z. Banhalmi-Zakar. 2022. “Optimized greenery configuration to mitigate urban heat: A decade systematic review.” Front. Archit. Res. 11 (3): 466–491. https://doi.org/10.1016/j.foar.2021.12.005.
Ghanbari, R., M. Heidarimozaffar, A. Soltani, and H. Arefi. 2023. “Land surface temperature analysis in densely populated zones from the perspective of spectral indices and urban morphology.” Int. J. Environ. Sci. Technol. 20: 2883–2902. https://doi.org/10.1007/s13762-022-04725-4.
Goldblatt, R., A. Addas, D. Crull, A. Maghrabi, G. G. Levin, and S. Rubinyi. 2021. “Remotely sensed derived land surface temperature (LST) as a proxy for air temperature and thermal comfort at a small geographical scale.” Land 10 (4): 410. https://doi.org/10.3390/land10040410.
Gómez, J. A., J. E. Patiño, J. C. Duque, and S. Passos. 2020. “Spatiotemporal modeling of urban growth using machine learning.” Remote Sens. 12 (1): 109. https://doi.org/10.3390/rs12010109.
Haashemi, S., Q. Weng, A. Darvishi, and S. Alavipanah. 2016. “Seasonal variations of the surface urban heat island in a semi-arid city.” Remote Sens. 8 (4): 352. https://doi.org/10.3390/rs8040352.
Hamad, R. 2020. “A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan.” SN Appl. Sci. 2 (1): 1–9. https://doi.org/10.1007/s42452-019-1806-4.
Hamad, R., H. Balzter, and K. Kolo. 2018. “Predicting land use/land cover changes using a CA-Markov model under two different scenarios.” Sustainability 10 (10): 3421. https://doi.org/10.3390/su10103421.
Han, D., H. An, H. Cai, F. Wang, X. Xu, Z. Qiao, K. Jia, Z. Sun, and Y. An. 2023a. “How do 2D/3D urban landscapes impact diurnal land surface temperature: Insights from block scale and machine learning algorithms.” Sustainable Cities Soc. 99: 104933. https://doi.org/10.1016/j.scs.2023.104933.
Han, D., et al. 2022. “Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach.” Build. Environ. 226: 109770. https://doi.org/10.1016/j.buildenv.2022.109770.
Han, D., et al. 2023b. “The roles of surrounding 2D/3D landscapes in park cooling effect: Analysis from extreme hot and normal weather perspectives.” Build. Environ. 231: 110053. https://doi.org/10.1016/j.buildenv.2023.110053.
He, B.-J., L. Ding, and D. Prasad. 2019. “Enhancing urban ventilation performance through the development of precinct ventilation zones: A case study based on the Greater Sydney, Australia.” Sustainable Cities Soc. 47: 101472. https://doi.org/10.1016/j.scs.2019.101472.
Hou, H., and R. C. Estoque. 2020. “Detecting cooling effect of landscape from composition and configuration: An urban heat island study on Hangzhou.” Urban For. Urban Greening 53: 126719. https://doi.org/10.1016/j.ufug.2020.126719.
Huang, C., E. L. Geiger, and J. A. Kupfer. 2006. “Sensitivity of landscape metrics to classification scheme.” Int. J. Remote Sens. 27 (14): 2927–2948. https://doi.org/10.1080/01431160600554330.
Kamali Maskooni, E., H. Hashemi, R. Berndtsson, P. Daneshkar Arasteh, and M. Kazemi. 2021. “Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data.” Int. J. Digital Earth 14 (2): 250–270. https://doi.org/10.1080/17538947.2020.1813210.
Kamarianakis, Y., X. Li, B. L. Turner, and A. J. Brazel. 2019. “On the effects of landscape configuration on summer diurnal temperatures in urban residential areas: Application in Phoenix, AZ.” Front. Earth Sci. 13: 445–463. https://doi.org/10.1007/s11707-017-0678-4.
Kantakumar, L. N., S. Kumar, and K. Schneider. 2019. “SUSM: A scenario-based urban growth simulation model using remote sensing data.” Eur. J. Remote Sens. 52 (Suppl. 2): 26–41. https://doi.org/10.1080/22797254.2019.1585209.
Karimi, H., J. Jafarnezhad, J. Khaledi, and P. Ahmadi. 2018. “Monitoring and prediction of land use/land cover changes using CA-Markov model: A case study of Ravansar County in Iran.” Arabian J. Geosci. 11: 1–9. https://doi.org/10.1007/s12517-018-3940-5.
Khan, A., A. Sohail, U. Zahoora, and A. S. Qureshi. 2020. “A survey of the recent architectures of deep convolutional neural networks.” Artif. Intell. Rev. 53: 5455–5516. https://doi.org/10.1007/s10462-020-09825-6.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. http://arxiv.org/abs/1412.6980.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “ImageNet classification with deep convolutional neural networks.” Commun. ACM 60 (6): 84–90. https://doi.org/10.1145/3065386.
Křováková, K., S. Semerádová, M. Mudrochová, and J. Skaloš. 2015. “Landscape functions and their change—A review on methodological approaches.” Ecol. Eng. 75: 378–383. https://doi.org/10.1016/j.ecoleng.2014.12.011.
Kumar, M., D. M. Denis, S. K. Singh, S. Szabó, and S. Suryavanshi. 2018. “Landscape metrics for assessment of land cover change and fragmentation of a heterogeneous watershed.” Remote Sens. Appl.: Soc. Environ. 10: 224–233. https://doi.org/10.1016/j.rsase.2018.04.002.
Kumar, V., and S. Agrawal. 2023. “A multi-layer perceptron—Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India.” Environ. Monit. Assess. 195 (5): 619. https://doi.org/10.1007/s10661-023-11205-w.
Li, H., Y. Li, T. Wang, Z. Wang, M. Gao, and H. Shen. 2021. “Quantifying 3D building form effects on urban land surface temperature and modeling seasonal correlation patterns.” Build. Environ. 204: 108132. https://doi.org/10.1016/j.buildenv.2021.108132.
Li, X., Y. Zhou, G. R. Asrar, M. Imhoff, and X. Li. 2017. “The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States.” Sci. Total Environ. 605–606: 426–435. https://doi.org/10.1016/j.scitotenv.2017.06.229.
Li, X., W. Zhou, Z. Ouyang, W. Xu, and H. Zheng. 2012. “Spatial pattern of greenspace affects land surface temperature: Evidence from the heavily urbanized Beijing metropolitan area, China.” Landscape Ecol. 27: 887–898. https://doi.org/10.1007/s10980-012-9731-6.
Li, Z.-T., M. Li, and B.-C. Xia. 2020. “Spatio-temporal dynamics of ecological security pattern of the Pearl River Delta urban agglomeration based on LUCC simulation.” Ecol. Indic. 114: 106319. https://doi.org/10.1016/j.ecolind.2020.106319.
Lin, J., S. Qiu, X. Tan, and Y. Zhuang. 2023. “Measuring the relationship between morphological spatial pattern of green space and urban heat island using machine learning methods.” Build. Environ. 228: 109910. https://doi.org/10.1016/j.buildenv.2022.109910.
Liu, X., Z. Deng, and Y. Yang. 2019. “Recent progress in semantic image segmentation.” Artif. Intell. Rev. 52: 1089–1106. https://doi.org/10.1007/s10462-018-9641-3.
Liu, Y., J. Peng, and Y. Wang. 2018. “Efficiency of landscape metrics characterizing urban land surface temperature.” Landscape Urban Plann. 180: 36–53. https://doi.org/10.1016/j.landurbplan.2018.08.006.
Magidi, J., and F. Ahmed. 2019. “Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015).” Egypt. J. Remote Sens. Space. Sci. 22 (3): 335–346. https://doi.org/10.1016/j.ejrs.2018.07.003.
Mahtta, R., A. Mahendra, and K. C. Seto. 2019. “Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+.” Environ. Res. Lett. 14 (12): 124077. https://doi.org/10.1088/1748-9326/ab59bf.
Masoudi, M., and P. Y. Tan. 2019. “Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature.” Landscape Urban Plann. 184: 44–58. https://doi.org/10.1016/j.landurbplan.2018.10.023.
Mayfield, H., C. Smith, M. Gallagher, and M. Hockings. 2017. “Use of freely available datasets and machine learning methods in predicting deforestation.” Environ. Modell. Software 87: 17–28. https://doi.org/10.1016/j.envsoft.2016.10.006.
Nagendra, H., D. K. Munroe, and J. Southworth. 2004. “From pattern to process: Landscape fragmentation and the analysis of land use/land cover change.” Agric. Ecosyst. Environ. 101: 111–115. https://doi.org/10.1016/j.agee.2003.09.003.
Oh, M., and E. J. Lee. 2021. “Cushion plant Silene acaulis is a pioneer species at abandoned coal piles in the High Arctic, Svalbard.” J. Ecol. Environ. 45 (1): 1–13. https://doi.org/10.1186/s41610-020-00177-4.
Oke, T. R. 1988. “The urban energy balance.” Prog. Phys. Geogr.: Earth Environ. 12 (4): 471–508. https://doi.org/10.1177/030913338801200401.
Othman, H. A. S., and A. A. Alshboul. 2020. “The role of urban morphology on outdoor thermal comfort: The case of Al-sharq city—Az zarqa.” Urban Clim. 34: 100706. https://doi.org/10.1016/j.uclim.2020.100706.
Peng, W., R. Wang, J. Duan, W. Gao, and Z. Fan. 2022. “Surface and canopy urban heat islands: Does urban morphology result in the spatiotemporal differences?” Urban Clim. 42: 101136. https://doi.org/10.1016/j.uclim.2022.101136.
Peng, Y., Q. Wang, and L. Bai. 2020. “Identification of the key landscape metrics indicating regional temperature at different spatial scales and vegetation transpiration.” Ecol. Indic. 111: 106066. https://doi.org/10.1016/j.ecolind.2020.106066.
Rajasekhar, M., G. S. Raju, R. S. Raju, M. Ramachandra, and B. P. Kumar. 2020. “Accuracy assessment of land use/land cover classification in parts of Kadapa district (Andhra Pradesh, India), using remote sensing and GIS.” J. Indian Geophys. Union 23 (4): 356–366.
Rakoto, P. Y., K. Deilami, J. Hurley, M. Amati, and Q. C. Sun. 2021. “Revisiting the cooling effects of urban greening: Planning implications of vegetation types and spatial configuration.” Urban For. Urban Greening 64: 127266. https://doi.org/10.1016/j.ufug.2021.127266.
Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional networks for biomedical image segmentation.” In Proc., 18th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, edited by N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi. Cham, Switzerland: Springer.
Sejati, A. W., I. Buchori, and I. Rudiarto. 2019. “The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region.” Sustainable Cities Soc. 46: 101432. https://doi.org/10.1016/j.scs.2019.101432.
Sertel, E., R. Topaloğlu, B. Şallı, I. Yay Algan, and G. Aksu. 2018. “Comparison of landscape metrics for three different level land cover/land use maps.” ISPRS Int. J. Geo-Inf. 7 (10): 408. https://doi.org/10.3390/ijgi7100408.
Shafizadeh-Moghadam, H., Q. Weng, H. Liu, and R. Valavi. 2020. “Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: A case study of Tehran, Iran.” GISci. Remote Sens. 57 (4): 483–496. https://doi.org/10.1080/15481603.2020.1736857.
Sharifi, E., and A. Soltani. 2017. “Patterns of urban heat island effect in Adelaide: A mobile traverse experiment.” Mod. Appl. Sci. 11 (4): 80–90. https://doi.org/10.5539/mas.v11n4p80.
Shih, W. 2017. “Greenspace patterns and the mitigation of land surface temperature in Taipei metropolis.” Habitat Int. 60: 69–80. https://doi.org/10.1016/j.habitatint.2016.12.006.
Shirani-bidabadi, N., T. Nasrabadi, S. Faryadi, A. Larijani, and M. Shadman Roodposhti. 2019. “Evaluating the spatial distribution and the intensity of urban heat island using remote sensing, case study of Isfahan city in Iran.” Sustainable Cities Soc. 45: 686–692. https://doi.org/10.1016/j.scs.2018.12.005.
Shivakumar, B., and S. V. Rajashekararadhya. 2018. “Investigation on land cover mapping capability of maximum likelihood classifier: A case study on North Canara, India.” Procedia Comput. Sci. 143: 579–586. https://doi.org/10.1016/j.procs.2018.10.434.
Soltani, A., and D. Karimzadeh. 2013. “The spatio-temporal modeling of urban growth using remote sensing and intelligent algorithms, case of Mahabad, Iran.” J. Land Use Mobility Environ. 6 (2): 189–200.
Soltani, A., and E. Sharifi. 2017. “Daily variation of urban heat island effect and its correlations to urban greenery: A case study of Adelaide.” Front. Archit. Res. 6 (4): 529–538. https://doi.org/10.1016/j.foar.2017.08.001.
Soltani, A., and E. Sharifi. 2019. “Understanding and analysing the urban heat island (UHI) effect in micro-scale.” Int. J. Social Ecol. Sustainable Dev. 10 (2): 14–28. https://doi.org/10.4018/IJSESD.2019040102.
Szabó, S., T. Novák, and Z. Elek. 2012. “Distance models in ecological network management: A case study of patch connectivity in a grassland network.” J. Nat. Conserv. 20 (5): 293–300. https://doi.org/10.1016/j.jnc.2012.06.002.
Taloor, A. K., V. Kumar, V. K. Singh, A. K. Singh, R. V. Kale, R. Sharma, V. Khajuria, G. Raina, B. Kouser, and N. H. Chowdhary. 2020. “Land use land cover dynamics using remote sensing and GIS Techniques in Western Doon Valley, Uttarakhand, India.” In Geoecology of landscape dynamics, edited by S. Sahdev, R. B. Singh, and M. Kumar, 37–51. Singapore: Springer.
Tanoori, G., A. Soltani, and A. Modiri. 2024. “Machine learning for urban heat island (UHI) analysis: Predicting land surface temperature (LST) in urban environments.” Urban Climate 55: 101962. https://doi.org/10.1016/j.uclim.2024.101962.
Topaloğlu, R. H., G. A. Aksu, Y. A. G. Ghale, and E. Sertel. 2022. “High-resolution land use and land cover change analysis using GEOBIA and landscape metrics: A case of Istanbul, Turkey.” Geocarto Int. 37 (25): 9071–9097. https://doi.org/10.1080/10106049.2021.2012273.
Valueva, M. V., N. Nagornov, P. A. Lyakhov, G. V. Valuev, and N. I. Chervyakov. 2020. “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation.” Math. Comput. Simul 177: 232–243. https://doi.org/10.1016/j.matcom.2020.04.031.
Verma, D., and A. Jana. 2019. “LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: Evidence from Mumbai.” Preprint, submitted September 21, 2019. http://arxiv.org/abs/1909.09774.
Vivekananda, G. N., R. Swathi, and A. Sujith. 2021. “Retracted article: Multi-temporal image analysis for LULC classification and change detection.” Eur. J. Remote Sens. 54 (Suppl. 2): 189–199. https://doi.org/10.1080/22797254.2020.1771215.
Wang, J., M. Bretz, M. A. A. Dewan, and M. A. Delavar. 2022. “Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.” Sci. Total Environ. 822: 153559. https://doi.org/10.1016/j.scitotenv.2022.153559.
Wang, L., H. Hou, and J. Weng. 2020a. “Ordinary least squares modelling of urban heat island intensity based on landscape composition and configuration: A comparative study among three megacities along the Yangtze River.” Sustainable Cities Soc. 62: 102381. https://doi.org/10.1016/j.scs.2020.102381.
Wang, L., S. Wang, Y. Zhou, J. Zhu, J. Zhang, Y. Hou, and W. Liu. 2020b. “Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China.” Global Ecol. Conserv. 21: e00827.
Wang, Y., and H. Akbari. 2016. “Analysis of urban heat island phenomenon and mitigation solutions evaluation for Montreal.” Sustainable Cities Soc. 26: 438–446. https://doi.org/10.1016/j.scs.2016.04.015.
Wei, X., Z. Xiao, Q. Li, P. Li, and C. Xiang. 2017. “Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics.” Landscape Ecol. Eng. 13: 169–181. https://doi.org/10.1007/s11355-016-0314-6.
Willie, Y. A., R. Pillay, L. Zhou, and I. R. Orimoloye. 2019. “Monitoring spatial pattern of land surface thermal characteristics and urban growth: A case study of King Williams using remote sensing and GIS.” Earth Sci. Inf. 12: 447–464. https://doi.org/10.1007/s12145-019-00391-2.
Xiang, Y., Y. Ye, C. Peng, M. Teng, and Z. Zhou. 2022. “Seasonal variations for combined effects of landscape metrics on land surface temperature (LST) and aerosol optical depth (AOD).” Ecol. Indic. 138: 108810. https://doi.org/10.1016/j.ecolind.2022.108810.
Yan, J., W. Zhou, and G. D. Jenerette. 2019. “Testing an energy exchange and microclimate cooling hypothesis for the effect of vegetation configuration on urban heat.” Agric. For. Meteorol. 279: 107666. https://doi.org/10.1016/j.agrformet.2019.107666.
Yang, G., Z. Yu, G. Jørgensen, and H. Vejre. 2020. “How can urban blue-green space be planned for climate adaption in high-latitude cities? A seasonal perspective.” Sustainable Cities Soc. 53: 101932. https://doi.org/10.1016/j.scs.2019.101932.
Yang, X., Y. Li, Z. Luo, and P. W. Chan. 2017. “The urban cool island phenomenon in a high-rise high-density city and its mechanisms.” Int. J. Climatol. 37 (2): 890–904. https://doi.org/10.1002/joc.4747.
Yue, W., X. Liu, Y. Zhou, and Y. Liu. 2019. “Impacts of urban configuration on urban heat island: An empirical study in China mega-cities.” Sci. Total Environ. 671: 1036–1046. https://doi.org/10.1016/j.scitotenv.2019.03.421.
Zaharchuk, G. 2019. “Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.” Eur. J. Nucl. Med. Mol. Imaging 46: 2700–2707. https://doi.org/10.1007/s00259-019-04374-9.
Zhang, Q., C. Chen, J. Wang, D. Yang, Y. Zhang, Z. Wang, and M. Gao. 2020. “The spatial granularity effect, changing landscape patterns, and suitable landscape metrics in the Three Gorges Reservoir Area, 1995–2015.” Ecol. Indic. 114: 106259. https://doi.org/10.1016/j.ecolind.2020.106259.
Zhang, R., J. Yang, X. Ma, X. Xiao, and J. C. Xia. 2023. “Optimal allocation of local climate zones based on heat vulnerability perspective.” Sustainable Cities Soc. 99: 104981. https://doi.org/10.1016/j.scs.2023.104981.
Zhang, X., D. Wang, H. Hao, F. Zhang, and Y. Hu. 2017. “Effects of land use/cover changes and urban forest configuration on urban heat islands in a loess hilly region: Case study based on Yan’an City, China.” Int. J. Environ. Res. Public Health 14 (8): 840. https://doi.org/10.3390/ijerph14080840.
Zhang, X., T. Zhong, X. Feng, and K. Wang. 2009. “Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing.” Int. J. Remote Sens. 30 (8): 2105–2118. https://doi.org/10.1080/01431160802549252.
Zhao, X., C. Cao, X. Ni, and W. Chen. 2017. “Retrieval and application of leaf area index over China using HJ-1 data.” Geomatics Nat. Hazards Risk 8 (2): 478–495. https://doi.org/10.1080/19475705.2016.1238854.
Zhou, D., J. Xiao, S. Bonafoni, C. Berger, K. Deilami, Y. Zhou, S. Frolking, R. Yao, Z. Qiao, and J. Sobrino. 2019. “Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives.” Remote Sens. 11 (1): 48. https://doi.org/10.3390/rs11010048.
Zhou, L., F. Hu, B. Wang, C. Wei, D. Sun, and S. Wang. 2022. “Relationship between urban landscape structure and land surface temperature: Spatial hierarchy and interaction effects.” Sustainable Cities Soc. 80: 103795. https://doi.org/10.1016/j.scs.2022.103795.
Zhou, X., and Y.-C. Wang. 2011. “Dynamics of land surface temperature in response to land-use/cover change.” Geogr. Res. 49 (1): 23–36. https://doi.org/10.1111/j.1745-5871.2010.00686.x.
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Journal of Urban Planning and Development
Volume 150 • Issue 3 • September 2024
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© 2024 American Society of Civil Engineers.
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Received: Oct 20, 2023
Accepted: Apr 10, 2024
Published online: Jun 24, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 24, 2024
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