Orbit optimization of spacecraft for remote sensing of Qinghai-Tibet plateau

2020 ◽  
Author(s):  
Hu Jiang ◽  
Lei Deng ◽  
Jinpei Yu ◽  
Yuesheng Jiang
Keyword(s):  
Remote Sensing ◽  
Tibet Plateau ◽  
Qinghai Tibet Plateau

scholarly journals Mapping Changing Population Distribution on the Qinghai–Tibet Plateau since 2000 with Multi-Temporal Remote Sensing and Point-of-Interest Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4059
Author(s):  
Lanhui Li ◽  
Yili Zhang ◽  
Linshan Liu ◽  
Zhaofeng Wang ◽  
Huamin Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Population Density ◽  
Rural Areas ◽  
Population Distribution ◽  
Local Population ◽  
Tibet Plateau ◽  
Point Of Interest ◽  
Tibet Autonomous Region ◽  
Multi Temporal ◽  
Qinghai Tibet Plateau

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.

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