Estimating local-scale urban heat island intensity using nighttime light satellite imageries

Sun, Y., Wang, S. and Wang, Y. (2020) Estimating local-scale urban heat island intensity using nighttime light satellite imageries. Sustainable Cities and Society, 57 . p. 102125. ISSN 2210-6707

Full content URL: https://doi.org/10.1016/j.scs.2020.102125

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Item Type:Article
Item Status:Live Archive

Abstract

Urban heat island (UHI) effect tends to harm health, increase anthropogenic energy consumption, and water consumption. Some policies targeting UHI mitigation have been implemented for a few years and thus needs to be evaluated for changes or modifications in the future. A low-cost approach to rapidly monitoring UHI intensity variations can assist in evaluating policy implementations. In this study, we proposed a new approach to local-scale UHI intensity estimates by using nighttime light satellite imageries. We explored to what extent UHI intensity could be estimated according to nighttime light intensity at two local scales. We attempted to estimate district-level and neighbourhood-level UHI intensity across London and Paris. As the geography level rises from district to neighbourhood, the capacity of the models explaining the variations of the UHI intensity decreases. Although the possible presence of residual spatial autocorrelation in the conventional regression models applied to geospatial data, most of the studies are likely to neglect this issue when fitting data to models. To remove negative effects of the residual spatial autocorrelation, this study used spatial regression models instead of non-spatial regression models (e.g., OLS models) to estimate UHI intensity. As a result, district-level UHI intensity was successfully estimated according to nighttime light intensity (approximately R2 = 0.7, MAE =1.16 °C, and RMSE =1.74 °C).

Additional Information:cited By 13
Keywords:Urban heat island effect, Nighttime light intensity, Nighttime light imagery, SNPP-VIIRS, Spatial regression model
Subjects:F Physical Sciences > F891 Geographical Information Systems
Divisions:College of Science > School of Geography
ID Code:49371
Deposited On:19 May 2022 10:30

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