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

Large-Scale Observations Support Aboveground Vegetation as an Important Biological Mercury Sink in the Tibetan Plateau

Mercury, a pervasive global pollutant, primarily enters the atmosphere through human activities and legacy emissions from the land and oceans. A significant portion of this mercury subsequently settles on land through vegetation uptake. Characterizing mercury storage and distribution within vegetation is essential for comprehending regional and global mercury cycles. We conducted an unprecedented large-scale aboveground vegetation mercury survey across the expansive Tibetan Plateau. We find that mosses (31.1 ± 0.5 ng/g) and cushion plants (15.2 ± 0.7 ng/g) outstood high mercury concentrations. Despite exceptionally low anthropogenic mercury emissions, mercury concentrations of all biomes exceeded at least one-third of their respective global averages. While acknowledging the role of plant physiological factors, statistical models emphasize the predominant impact of atmospheric mercury on driving variations in mercury concentrations. Our estimations indicate that aboveground vegetation on the plateau accumulates 32-12+21 Mg (interquartile range) mercury. Forests occupy the highest biomass and store 82% of mercury, while mosses, representing only 3% of the biomass, disproportionally contribute 13% to mercury storage and account for 43% (2.5-1.4+3.0 Mg/year) of annual mercury assimilation by vegetation. Additionally, our study underscores that extrapolating aboveground vegetation mercury storage from lower-altitude regions to the Tibetan Plateau can lead to substantial overestimation, inspiring further exploration in alpine ecosystems worldwide.

Effectiveness of the Qilian Mountain Nature Reserve of China in Reducing Human Impacts

The management effectiveness of protected areas plays a key role in biodiversity and ecosystem services conservation. We evaluated the effectiveness of Qilian Mountain Nature Reserve (QMNR) in reducing human footprint (HF). Four dominant human activity factors, including population density, land use, road distribution, and night light, were incorporated for HF mapping. Comparisons of the HF value between inside and outside QMNR and its four functional zones were conducted. The results show that both the HF inside and outside of QMNR were increasing, but the difference between them was increasing, indicating partial management effectiveness. The north part of the central reserve has a good effect in reducing human impacts, while the effectiveness was poor at both ends of the reserve. The HF value of the most strictly managed core and buffer zones increased by 10.50 and 6.68%, respectively, for 2010–2020. The QMNR was effective in controlling population density and land use, but ineffective in reducing road construction, mining, and construction of hydropower facilities.

Prediction of Total Imperviousness from Population Density and Land Use Data for Urban Areas (Case Study: South East Queensland, Australia)

Total imperviousness (residential and non-residential) increases with population growth in many regions around the world. Population density has been used to predict the total imperviousness in large areas, although population size was only closely related to residential imperviousness. In this study, population density together with land use data for 154 suburbs in Southeast Queensland (SEQ) of Australia were used to develop a new model for total imperviousness estimation. Total imperviousness was extracted through linear spectral mixing analysis (LSMA) using Landsat 8 OLI/TIRS, and then separated into residential and non-residential areas based on land use data for each suburb. Regression models were developed between population density and total imperviousness, and population density and residential imperviousness. Results show that (1) LSMA approach could retrieve imperviousness accurately (RMSE < 10%), (2) linear regression models could be used to estimate both total imperviousness and residential imperviousness better than nonlinear regression models, and (3) correlation between population density and residential imperviousness was higher (R2 = 0.77) than that between population density and total imperviousness (R2 = 0.52); (4) the new model was used to predict the total imperiousness based on population density projections to 2057 for three potential urban development areas in SEQ. This research allows accurate prediction of the total impervious area from population density and service area per capital for other regions in the world.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971-2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen's slope estimator, and the Mann-Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies.