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28 november 2022 10:21:48

 
Remote Sensing, Vol. 14, Pages 6019: Monitoring and Analysis of Population Distribution in China from 2000 to 2020 Based on Remote Sensing Data (Remote Sensing)
 


Accurately and precisely grasping the spatial distribution and changing trends of China’s regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R2 of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources.


 
94 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 6018: Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran (Remote Sensing)
Remote Sensing, Vol. 14, Pages 6020: Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field (Remote Sensing)
 
 
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