Abstract
The urban forest plays an important role in mitigating the urban heat island. However, the cooling effects of different types of urban forest are unclear. In addition, the fairness of the cooling effect of the urban forest has not been discussed. In this study, the land surface temperature (LST) of Changchun City, China was obtained from Landsat ETM+ data and then correlated with detailed urban forest information derived from the high-spatial-resolution Google Maps in order to determine the cooling intensity and cooling distance of different types of urban forest. In addition, the Gini coefficient was used to evaluate the equity of cooling services provided by the urban forest. The results indicated that (1) the total area of urban forest in Changchun City is 106.69 km2 and is composed of attached forest (AF, 45.83 km2), road forest (RF, 23.87 km2), ecological public welfare forest (EF, 23.24 km2) and landscape forest (LF, 13.75 km2); (2) the cooling effect of different types of urban forest varies. The cooling intensity and cooling distance are 3.2 °C and 125 m (LF), 0.2 °C and 150 m (EF) and 0.6 °C and 5 m (AF), and RF had no cooling effect; and (3) the cooling effect of urban forest benefits approximately 760,000 people in Changchun City, and the Gini coefficient of the cooling services of urban forest was 0.29, indicating that the cooling service was reasonable. Therefore, we demonstrated that ETM+ and Google data are a convenient and affordable approach to study the LST on an urban scale, and the Gini coefficient could be a meaningful indicator to evaluate urban ecological services.
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Acknowledgements
This research was supported by the Key projects of Chinese Academy of Science (KFZD-SW-302-03), Foundation for Excellent Young Scholars of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (DLSYQ13004) and Jilin Province Science and Technology Development Plan (20140520146JH).
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Tang, Z., Zheng, H., Ren, Z. et al. Evaluating environmental equities of urban forest in terms of cooling services using ETM+ and Google data. J Indian Soc Remote Sens 46, 287–296 (2018). https://doi.org/10.1007/s12524-017-0689-3
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DOI: https://doi.org/10.1007/s12524-017-0689-3