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RSS FeedsRemote Sensing, Vol. 11, Pages 2140: Night on South Korea: Unraveling the Relationship between Urban Development Patterns and DMSP-OLS Night-Time Lights (Remote Sensing)

 
 

14 september 2019 09:00:29

 
Remote Sensing, Vol. 11, Pages 2140: Night on South Korea: Unraveling the Relationship between Urban Development Patterns and DMSP-OLS Night-Time Lights (Remote Sensing)
 


Using artificial light data measured from satellites has the potential to change research methods in geography and urban planning. The Defense Meteorological Satellite Program Optical Linescan System (DMSP-OLS) night-time light datasets provided consistent and valuable data sources for investigating urbanization processes. This study intends to empirically investigate the relationship between night-time lights, population, and urban development patterns. A novel protocol was developed to integrate heterogeneous datasets into a standardized unit of analysis. Multivariate mixed-effects models were applied to detect correlations within and between provinces in South Korea. To capture physical variations of urban development, four landscape metrics were used and tested in the analyses. Diminishing returns of night-time lights to population were found in all models. In single landscape metric models, all coefficients of landscape metrics were positively related to night-time lights. In combination models, the aggregation index (AI) was no longer statistically significant. The protocol developed in this study provides an effective way to create analytical units for integrating heterogeneous forms of data. Creating standardized units of analyses will make it possible for researchers to compare their results with other studies. Landscape metrics used in this study for capturing the composition and configuration of urban development patterns will enrich the discussion in the future.


 
250 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2142: Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2141: Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach (Remote Sensing)
 
 
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