Progress of Research on Qinghai-Tibet Plateau Satellite Remote Sensing

2012 ◽  
Author(s):  
Ge Wang ◽  
Lin Han
Keyword(s):  
Remote Sensing ◽  
Satellite 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.

scholarly journals Evaluation of the Accuracy of the Field Quadrat Survey of Alpine Grassland Fractional Vegetation Cover Based on the Satellite Remote Sensing Pixel Scale

2019 ◽  
Vol 8 (11) ◽  
pp. 497 ◽  
Author(s):  
Chen ◽  
Zhao ◽  
Zhang ◽  
Qin ◽  
Yi
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Alpine Grassland ◽  
Tibet Plateau ◽  
Sample Plot ◽  
Fractional Vegetation Cover ◽  
Quadrat Survey ◽  
Pixel Scale ◽  
Degree Of Fragmentation

The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p < 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored.

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