Analyzing land use types’ effects on LST using the GWR model and case studies in Beijing
Abstract
The development of urbanization and the transformation of green lands into impermeable land increase temperature and create urban heat islands (UHIs). Our observations with remote sensing instruments of Landsat platforms show considerable changes in land use types in Beijing city with the shrinking of green lands, expansion of built environments, and a slight increase in the temperature during the recent four decades. Using remote sensing instruments of Landsat platforms and registered data from two meteorological stations in Beijing, this study finds the relationship between land surface temperature (LST) and the increasing conversion of cultivated lands into built-up areas. This article presents innovative research that shows the mutual correlation well and recommends revisions in the land use policies for better weather. The geographically weighted regression model (GWR) with a Gaussian weighting kernel function analyzes the impact of various urban land use types on the LST and the increase UHIs. In Beijing city, green lands show fewer standard deviations (SD) in the average temperatures equal to 0.109, while the industrial spaces exhibit a high SD equal to 0.212. The outcomes of this paper contribute to finding optimal land use policies everywhere in the world with the increasing urbanization through simulating its model for a more comfortable life.
Keyword : landscape management, land use type, urban heat island, land surface temperature, remote sensing, captured images, geographically weighted regression
How to Cite
Yao, Z., Liu, L., Li, W., Shahraki, A. A., & Pang, Y. (2023). Analyzing land use types’ effects on LST using the GWR model and case studies in Beijing. Journal of Environmental Engineering and Landscape Management, 31(3), 196–205. https://doi.org/10.3846/jeelm.2023.19469
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zhi, Y., Shan, L., Ke, L., & Yang, R. (2020). Analysis of land surface temperature driving factors and spatial heterogeneity research based on geographically weighted regression model. Complexity, 2020, 1–9. https://doi.org/10.1155/2020/2862917
Bilang, R. G. J. P., Blanco, A. C., Santos, J. A. S., & Olaguera, L. M. P. (2022). Simulation of urban heat island during a high-heat event using WRF urban canopy models: A case study for Metro Manila. Atmosphere, 13(10), 1658. https://doi.org/10.3390/atmos13101658
Bouton, S., Newsome, D., & Woetzel, J. (2015). Building the cities of the future with green districts. McKinsey & Company.
Cai, G., Zhang, J., Du, M., Li, C., & Peng, S. (2020). Identification of urban land use efficiency by indicator-SDG 11.3. 1. PloS One, 15(12), e0244318. https://doi.org/10.1371/journal.pone.0244318
Chen, M., Zhou, Y., Hu, M., & Zhou, Y. (2020). Influence of urban scale and urban expansion on the urban heat island effect in metropolitan areas: A case study of Beijing–Tianjin–Hebei urban agglomeration. Remote Sensing, 12(21), 3491. https://doi.org/10.3390/rs12213491
De Miguel González, R., & Vallvé, M. L. T. (2023). Sustainable cities, urban indicators, and planning for the new urban agenda. Sustainable development goals and the rights to the city. In Sustainable development goals in Europe: A geographical approach (pp. 217–241). Springer International Publishing. https://doi.org/10.1007/978-3-031-21614-5_11
Dobruskin, V. K. (2022). The impact of energy produced by civilization on global warming. Open Journal of Ecology, 12(6), 325–332. https://doi.org/10.4236/oje.2022.126019
Feng, S., & Fan, F. (2022). Developing an Enhanced Ecological Evaluation Index (EEEI) based on remotely sensed data and assessing spatiotemporal ecological quality in Guangdong–Hong Kong–Macau Greater Bay Area, China. Remote Sensing, 14(12), 2852.
Feng, Y., Du, S., Myint, S. W., & Shu, M. (2019). Do urban functional zones affect land surface temperature differently? A case study of Beijing, China. Remote Sensing, 11(15), 1802. https://doi.org/10.3390/rs11151802
Fonseka, H. P. U., Zhang, H., Sun, Y., Su, H., Lin, H., & Lin, Y. (2019). Urbanization and its impacts on land surface temperature in Colombo metropolitan area, Sri Lanka, from 1988 to 2016. Remote Sensing, 11(8), 957. https://doi.org/10.3390/rs11080957
Galland, D., & Stead, D. (2022). Periodizing planning history in metropolitan regions: An historical-discursive institutionalist approach. In Association of Collegiate Schools of Planning (ACSP) Annual Conference 2022 (pp. 614–615), Toronto, Canada.
Gyimah, R. R. (2023). The hot zones are cities: Methodological outcomes and synthesis of surface urban heat island effect in Africa. Cogent Social Sciences, 9(1), 2165651. https://doi.org/10.1080/23311886.2023.2165651
Henry, J. A., & Dicks, S. E. (1987). Association of urban temperatures with land use and surface materials. Landscape and Urban Planning, 14, 21–29. https://doi.org/10.1016/0169-2046(87)90003-X
Islam, M. K., Hassan, N. M. S., Rasul, M. G., Emami, K., & Chowdhury, A. A. (2022). Green and renewable resources: An assessment of sustainable energy solution for Far North Queensland, Australia. International Journal of Energy and Environmental Engineering, 1–29. https://doi.org/10.1007/s40095-022-00552-y
Kafy, A. A., Al Rakib, A., Fattah, M. A., Rahaman, Z. A., & Sattar, G. S. (2022). Impact of vegetation cover loss on surface temperature and carbon emission in a fastest-growing city, Cumilla, Bangladesh. Building and Environment, 208, 108573. https://doi.org/10.1016/j.buildenv.2021.108573
Kelly-Fair, M., Gopal, S., Koch, M., Pancasakti Kusumaningrum, H., Helmi, M., Khairunnisa, D., & Kaufman, L. (2022). Analysis of land use and land cover changes through the lens of SDGs in Semarang, Indonesia. Sustainability, 14(13), 7592. https://doi.org/10.3390/su14137592
Liu, W., Jia, B., Li, T., Zhang, Q., & Ma, J. (2022). Correlation analysis between urban green space and land surface temperature from the perspective of spatial heterogeneity: A case study within the sixth ring road of Beijing. Sustainability, 14(20), 13492. https://doi.org/10.3390/su142013492
Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J., Storey, J., & Jenstrom, D. T. (2020). Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, 248, 111968. https://doi.org/10.1016/j.rse.2020.111968
Meng, Q., Liu, W., Zhang, L., Allam, M., Bi, Y., Hu, X., Gao, J., Hu, D., & Jancsó, T. (2022). Relationships between land surface temperatures and neighboring environment in highly urbanized areas: Seasonal and scale effects analyses of Beijing, China. Remote Sensing, 14(17), 4340. https://doi.org/10.3390/rs14174340
Mostafa, E., Li, X., & Sadek, M. (2023). Urbanization trends analysis using hybrid modeling of fuzzy analytical hierarchical Process-Cellular Automata-Markov chain and investigating its impact on land surface temperature over Gharbia City, Egypt. Remote Sensing, 15(3), 843. https://doi.org/10.3390/rs15030843
National Center for Environmental Information. (2022). www.cma.gov.cn
Olorunfemi, I. E., Fasinmirin, J. T., Olufayo, A. A., & Komolafe, A. A. (2020). GIS and remote sensing-based analysis of the impacts of land use/land cover change (LULCC) on the environmental sustainability of Ekiti State, southwestern Nigeria. Environment, Development and Sustainability, 22, 661–692. https://doi.org/10.1007/s10668-018-0214-z
Schott, J. R., & Schimminger, E. W. (1981). Data use investigations for applications Explorer Mission A (Heat Capacity Mapping Mission): HCMM’s role in studies of the urban heat island, Great Lakes thermal phenomena, and radiometric calibration of satellite data (No. CALSPAN-6175-M-1). https://ntrs.nasa.gov/citations/19810015961
Sayler, K. (2022). Landsat 9 data users handbook (Version 1.0). USGS.
Sfîcă, L., Ichim, P., Apostol, L., & Ursu, A. (2018). The extent and intensity of the urban heat island in Iași city, Romania. Theoretical and Applied Climatology, 134(3), 777–791. https://doi.org/10.1007/s00704-017-2305-4
Shahjahan, A. T. M. (2018). Urban adaptation measures for climate change: Study of urban wetlands in view of potential urban cooling [Dissertation]. Bangladesh University of Engineering and Technology (BUET), Department of Architecture.
Shahraki, A. A. (2020). Ending the water migrations thru the sustainable improvement of origins’ water resources, a reflection of case studies. Sustainable Water Resources Management, 6(6), 1–14. https://doi.org/10.1007/s40899-020-00468-7
Shen, P., Zhao, S., Ma, Y., & Liu, S. (2023). Urbanization-induced Earth’s surface energy alteration and warming: A global spatiotemporal analysis. Remote Sensing of Environment, 284, 113361. https://doi.org/10.1016/j.rse.2022.113361
Shi, Q., & Cao, G. (2020). Urban spillover or rural industrialization: Which drives the growth of the Beijing Metropolitan Area. Cities, 105, 102354. https://doi.org/10.1016/j.cities.2019.05.023
Siqi, J., Wang, Y., Chen, L., & Bi, X. (2022). A novel approach to predicting land surface temperature by the combination of geographically weighted regression and deep neural network models. https://doi.org/10.2139/ssrn.4203692
Sun, Y., Gao, C., Li, J., Wang, R., & Liu, J. (2019). Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning. Remote Sensing, 11(8), 959. https://doi.org/10.3390/rs11080959
Sykes, O., Shaw, D., & Webb, B. (2023). A global agenda for planning. In International planning studies: An introduction (pp. 157–195). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-5407-8_6
Time and Date. (n.d.). Weather in Beijing, Beijing municipality, China. https://www.timeanddate.com/weather/china/beijing
Ullah, S., Ullah, R., Javed, M. F., Sajjad, R. U., Ullah, I., Mohamed, A., & Ullah, W. (2023). Land Use Land Cover (LULC) and Land Surface Temperature (LST) Changes and its Relationship with human modification in Islamabad capital territory, Pakistan. https://doi.org/10.21203/rs.3.rs-2487695/v1
UN-Habitat. (2020). A new strategy of sustainable neighborhood planning: Five principles. https://unhabitat.org/sites/default/files/2019/10/64._5_principles_of_neighbourhood_design.pdf
United Nations Department of Economic and Social Affairs, Population Division. (2022). World population prospects 2022: Summary of results. https://www.un.org/development/desa/pd/content/World-Population-Prospects-2022
United Nations, & Regional Information Centre System for Western Europe. (n.d.). https://unric.org/en/
United States Geological Survey. (2023). https://earthexplorer.usgs.gov/
Wang, X., Hou, X., Piao, Y., Feng, A., & Li, Y. (2021). Climate change projections of temperature over the coastal area of China using SimCLIM. Frontiers in Environmental Science, 9, 548. https://doi.org/10.3389/fenvs.2021.782259
Wang, Q., Wang, X., Meng, Y., Zhou, Y., & Wang, H. (2023). Exploring the impact of urban features on the spatial variation of land surface temperature within the diurnal Cycle. Sustainable Cities and Society, 91, 104432. https://doi.org/10.1016/j.scs.2023.104432
Wasif Ali, N. U. A. B., Amir, S., Iqbal, K. M. J., Shah, A. A., Saqib, Z., Akhtar, N., Ullah, W., & Tariq, M. A. U. R. (2022). Analysis of land surface temperature dynamics in Islamabad by using MODIS Remote Sensing Data. Sustainability, 14(16), 9894. https://doi.org/10.3390/su14169894
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., … Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195
Xu, H., Li, C., Hu, Y., Li, S., Kong, R., & Zhang, Z. (2023). Quantifying the effects of 2D/ 3D urban landscape patterns on land surface temperature: A perspective from cities of different sizes. Building and Environment, 233, 110085.
Zhi, Y., Shan, L., Ke, L., & Yang, R. (2020). Analysis of land surface temperature driving factors and spatial heterogeneity research based on geographically weighted regression model. Complexity, 2020, 1–9. https://doi.org/10.1155/2020/2862917