Abstract
The feasibility of integrating urban biophysical descriptors into land surface temperature (LST) prediction in a rapidly urbanizing region, Jinan, China, was explored. We combined several models, including LST and an urban biophysical descriptor generation algorithm, through regression analysis and cellular automata-Markov modeling. First, the relationship between the indices, including the soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), index-based built-up index (IBI), and the LST was established; then, the future LST was simulated and predicted. The results showed that LST has a negative correlation with SAVI (R2 = 0.905, P < 0.01) and MNDWI (R2 = 0.911, P < 0.01) while having a high positive correlation with IBI (R2 = 0.801, P < 0.01). Additionally, correlations between the simulated values and retrieval of different land use/cover types show good agreement, with a mean R from 0.6 to 0.9, such that this method can be used to simulate future urban heat island effects. If the development trend remains unchanged, the urban heat island effect of Jinan will continue to strengthen by 2030 because of the increase in impervious ground and reduction in green vegetation. These results can provide guidance for Jinan’s urban construction and planning, which promote sustainable urbanization.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Afrakhteh, R., Aagarian, A., Sakieh, Y., & Soffianian, A. (2016). Evaluating the strategy of integrated urban–rural planning system and analyzing its effects on land surface temperature in a rapidly developing region. Habitat International, 56, 147–156. https://doi.org/10.1016/j.rse.2009.07.021.
Alhowaish, A. K. (2015). Eighty years of urban growth and socioeconomic trends in Dammam Metropolitan Area, Saudi Arabia. Habitat International, 50, 90–98. https://doi.org/10.1016/j.habitatint.2015.08.019.
Alsharif, A., et al. (2014). Urban sprawl analysis of tripoli Metropolitan City (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42, 149–163. https://doi.org/10.1007/s12524-013-0299-7.
Amiri, R., et al. (2009). Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113, 2606–2617. https://doi.org/10.1016/j.rse.2009.07.021.
Arnfield, A. J. (2003). Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23, 1–26. https://doi.org/10.1002/joc.859.
Bhatti, S. S., et al. (2015). A multi-scale modeling approach for simulating urbanization in a metropolitan region. Habitat International, 50, 354–365. https://doi.org/10.1016/j.habitatint.2015.09.005.
Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24, 459–479. https://doi.org/10.1016/0034-4257(88)900193.
Chen, X., et al. (2005). A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98, 63–79. https://doi.org/10.1016/j.rse.2005.05.021.
CSY. (2017). China Statistical Yearbook. Beijing: China Statistical Publishing House
Du, Y., et al. (2002). Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection. Remote Sensing of Environment, 82, 123–134. https://doi.org/10.1016/s0034-4257(02)00029-9.
Gillespie, A. (1998). A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1113–1126.
Grimm, N. B., et al. (2008). Global change and the ecology of cities. Science, 319, 756–760. https://doi.org/10.1126/science.1150195.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. https://doi.org/10.1016/0034-4257(88)90106-X.
Jimenes-Munoz, J. C. (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108(D22), 4688.
Jimenez-Munoz, J. C., Cristobal, J., Sobrino, J. A., Soria, G., Ninyerola, M., & Pons, X. (2009). Revision of the single-channel algorithm for land surface temperature retrieval from landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing, 47, 339–349. https://doi.org/10.1109/TGRS.2008.2007125.
Losiri, C., et al. (2016). Modeling urban expansion in Bangkok metropolitan region using demographic-economic data through cellular automata-Markov chain and multi-layer perceptron-markov chain models. Sustainability, 8, 686. https://doi.org/10.3390/su8070686.
Mathew, A., Sreekumar, S., Khandelwal, S., Kaul, N., & Kumar, R. (2016). Prediction of surface temperatures for the assessment of urban heat island effect over Ahmedabad City using linear time series model. Energy and Buildings, 128, 605–616. https://doi.org/10.1016/j.enbuild.2016.07.004.
Meng, F., et al. (2014). Remote-sensing evaluation of the relationship between urban heat islands and urban biophysical descriptors in Jinan, China. Journal of Applied Remote Sensing, 8, 083693. https://doi.org/10.1117/1.JRS.8.083693.
Michishita, R., Jiang, Z., & Xu, B. (2012). Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing. Remote Sensing of Environment, 117, 3–18. https://doi.org/10.1016/j.rse.2011.06.021.
Nor, A. N. M., et al. (2017). Impact of rapid urban expansion on green space structure. Ecological Indicators, 81, 274–284. https://doi.org/10.1016/j.ecolind.2017.05.031.
Peng, J. (2016). Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173, 145–155. https://doi.org/10.1016/j.rse.2015.11.027.
Qin, Z., Dall’Olmo, G., Karnieli, A., et al. (2001). Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high-resolution radiometer data. Journal of Geophysical Research Atmospheres, 106(D19), 22655–22670.
Roy, S., et al. (2015). Monitoring and prediction of land use/land cover change using the integration of markov chain model and cellular automation in the Southeastern Tertiary Hilly Area of Bangladesh. International Journal of Sciences: Basic and Applied Research, 24, 125–148.
Sekertekin, A., et al. (2018). The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: A comparative analysis. Environmental Monitoring and Assessment, 190, 381. https://doi.org/10.1007/s10661-018-6767-3.
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106, 7183–7192.
United Nations, Department of Economic and Social Affairs, Population Division. (2018). World Urbanization Prospects: The 2018 Revision, Online Edition. https://esa.un.org/unpd/wup/Publications.
Voogt, J. A. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8.
Wu, J. (2014). Urban ecology and sustainability: The state-of-the-science and future directions. Landscape and Urban Planning, 125, 209–221. https://doi.org/10.1016/j.landurbplan.2014.01.018.
Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033. https://doi.org/10.1080/01431160600589179.
Xu, H. (2008a). Comment on the enhanced water index (EWI): A discussion on the creation of a water index. Geo-Information Science, 10, 776–780. https://doi.org/10.3724/SP.J.1047.2008.00026. (in Chinese).
Xu, H. (2008b). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276.
Xu, H., et al. (2009). Urban expansion and heat island dynamics in the Quanzhou region, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2, 74–79. https://doi.org/10.1109/JSTARS.2009.2023088.
Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106, 375–386. https://doi.org/10.1016/j.rse.2006.09.003.
Zha, Y., et al. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583–594. https://doi.org/10.1080/01431160304987.
Zhang, L. (2011). Is industrialization still a viable development strategy for developing countries under climate change? Climate Policy, 11, 1159–1176. https://doi.org/10.1080/14693062.2011.579263.
Zhang, X. Y., et al. (2009). Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. International Journal of Remote Sensing, 30, 2105–2118. https://doi.org/10.1080/01431160802549252.
Zhang, L., et al. (2014). Automatic radiometric normalization for multitemporal remote sensing imagery with iterative slow feature analysis. IEEE Transactions on Geoscience and Remote Sensing, 52, 6141–6155. https://doi.org/10.1109/TGRS.2013.2295263.
Zhou, W., et al. (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102, 54–63. https://doi.org/10.1016/j.landurbplan.2011.03.009.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Meng, X., Meng, F., Zhao, Z. et al. Prediction of Urban Heat Island Effect over Jinan City Using the Markov-Cellular Automata Model Combined with Urban Biophysical Descriptors. J Indian Soc Remote Sens 49, 997–1009 (2021). https://doi.org/10.1007/s12524-020-01274-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-020-01274-6