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RSS FeedsRemote Sensing, Vol. 14, Pages 6166: Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data (Remote Sensing)

 
 

5 december 2022 14:45:37

 
Remote Sensing, Vol. 14, Pages 6166: Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data (Remote Sensing)
 


Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are strongly related to the city’s heterogeneity and have significant spatial-temporal characteristics but fail to address the albedo of the urban surface as a unique variable in urban thermal environment research. This study selects Beijing as the experimental area for exploring the spatial-temporal characteristics of the urban surface albedo and the albedo’s uniqueness in environmental research on urban spaces. Our results show that the urban surface albedo at high spatial resolution can better represent the urban spatial heterogeneity, seasonal variation, building canyon, and pixel adjacency effects. Urban surface albedo is associated with building density and height, land surface temperature (LST), and fractional vegetation cover (FVC). Furthermore, albedo can reflect livability and environmental rating due to the variances of building materials and architectural formats in the urban development. Hence, we argue that the albedo of the urban surface can be considered as a unique variable for improving the acknowledgment of the urban environment and human livability with wider application in urban environmental research.


 
88 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 6164: Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring (Remote Sensing)
Remote Sensing, Vol. 14, Pages 6167: Optimization Method of Airborne LiDAR Individual Tree Segmentation Based on Gaussian Mixture Model (Remote Sensing)
 
 
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