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[Display omitted] • For population modelling, remote sensing nighttime light data and nighttime LBS data were utilized. • Population maps of the Tibetan Plateau at the city level were modelled by geographically weighted regression. • The crowd exposure frequency characterized by nighttime LBS data contributed significantly to population modelling. • The model predicted more accurate population maps than three mainstream international population datasets. • The operational mechanism of the new demographic model was revealed. Population mapping is one of the fundamental materials for regional sustainability studies. Most scholars applied remote sensing data with excessive indicators to fit the population distribution. Nevertheless, over-complex models were lack of accuracy. This paper proposed a population model in the Qinghai-Tibet Plateau as a study area; the model has consisted of Human Activity Extent and Crowd Exposure Frequency. Performed remote sensing land cover data, nighttime light data, and LBS geographic big data as candidate indicators for exploratory regression experiments eventually developed an optimal population model assembled by nighttime LBS data and nighttime light data. The model fits significantly better at the city level (R2values of 0.9922) and reduces the error compared with other studies and publicly available datasets (%RMSE values of 6.83%). For the first time, the model proposes that Crowd Exposure Frequency based on nighttime LBS data can provide effective population simulation in the global, nighttime light data gain compensation for it. Nighttime light data plays a dominant role in densely populated areas; at the same time, nighttime LBS revised its overestimation. They modified each other to make the model accuracy significantly elevated. The modelling framework allows dynamic and low-cost population estimates of ecologically vulnerable areas and thus serves sustainable regional development. [ABSTRACT FROM AUTHOR]

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images. [ABSTRACT FROM AUTHOR]

The existing research on watershed ecological compensation pays less attention to the special basin (without significant relationship between upstream and downstream or the compensation subject and object are distributed on the left and right banks of the basin), which makes the previous research methods difficult to be effectively used. Therefore, it is necessary to explore a new method to solve the problem of ecological compensation in special basin. Taking Taihu Lake Basin (Jiangsu section) as an example, this paper first calculates the ecosystem service value (ESV) of the region from 2005 to 2018, and makes a spatial-temporal analysis; through the construction of land use intensity index system and coupling coordination degree model, the relationship between ESV and land use intensity is explored, and the reasons for its change are explored; finally, this paper constructs a compensation standard accounting model based on GDP, basin area and population, and applies it to the accounting of compensation standard and the identification of compensation subject and object. The results show that: from 2005 to 2018, the land use of this area changed greatly, mainly reflected in farmland and urban land; ESV decreased by 21.41%, which was mainly affected by the change of land use, the downturn of grain market, and farmers' going out to work; the coupling coordination degree of land use intensity and ESV in this area entered the stage of coordinated development from 2010, but the situation of each city was quite different; population, area and GDP have a great influence on compensation subject and object, and compensation standard. This study can not only provide a complete methodology for governments to implement ecological compensation in special basins, and improve the existing ecological compensation theory. [ABSTRACT FROM AUTHOR]