Human activities have dramatically changed ecosystems. As an irreplaceable ecological barrier in western China, the Qilian Mountains (QLM) provide various ecosystem services for humans. To evaluate the changes in the intensity of human activities in the QLM and their impact on the ecosystem, the human footprint (HF) method was used to conduct a spatial dataset of human activity intensity. In our study, the NDVI was used to characterize the growth of vegetation, and six categories of human pressures were employed to create the HF map in the QLM for 2000–2015 at a 1-km scale. The results showed that the mean NDVI during the growing season showed a significant increasing trend over the entire QLM in the period 2000–2015, while the NDVI showed a significant declining trend of more than 70% concentrated in Qinghai. Human pressure throughout the QLM occurred at a low level during 2000–2015, being greater in the eastern region than the western region, while the Qinghai area had greater human pressure than the Gansu area. Due to the improvement in traffic facilities, tourism, overgrazing, and other illegal activities, grasslands, shrublands, forests, wetlands, and bare land were the vegetation types most affected by human activities (in decreasing order). As the core area of the QLM, the Qilian Mountains National Nature Reserve (NR) has effectively reduced the impact of human activities. However, due to the existence of many ecological historical debts caused by unreasonable management in the past, the national park established in 2017 is facing great challenges to achieve its goals. These data and results will provide reference and guidance for future protection and restoration of the QLM ecosystem.

With flooding events expected to increase in both intensity and frequency in the future due to climate change, ensuring the safety of river embankments is vital to withstand flood disasters. Piping is one of the most harmful river embankment hazards in the flood season, and recent advances in unmanned aerial vehicles (UAVs) and deep learning‐based object detection have enabled efficient and automated hazard detection. In this study, a novel approach that integrates a UAV with deep learning‐based object detection and edge computing was proposed for rapid and automatic piping detection. First, a total of 104 field simulation experiments were conducted across 12 different sites in flood‐prone areas to fill gaps in the high‐quality data set, and the UAV thermal infrared and visible data sets of river embankment piping were produced, including various times (forenoon, afternoon, and night), weather conditions (clear‐sky, cloudy, and rainy), locations (bare land, paddy, grassland, and pond) and flight altitudes (10, 20, and 30 m). Second, the deep learning‐based object detection model was selected and trained on the thermal infrared and visible data sets. The well‐trained infrared and visible models have detection precisions of 92.7% and 70.4%, respectively, with recalls of 84.9% and 69.7%. Furthermore, both models exhibited great resistance to interference from several types of aquatic vegetation and could effectively detect piping on rainy days. The integration of a UAV and edge computing enabled real‐time detection of piping. The proposed method enhances hazard detection efficiency, contributing to intelligent emergency embankment management.

As the “Third Pole” of the Earth, the Tibetan Plateau (TP) has been warming significantly, and the instability of extreme events related to climate and weather has enhanced exceptionally in recent decades. These changes have posed increasingly severe impacts on the population over the TP. So far, however, the impacts on the population have not been assessed systematically and comprehensively from the perspective of risk. In this paper, the hazard of climate change was assessed from a fresh look, not only considering extreme changes of air temperature, precipitation, and wind speed, but also their changes in mean and fluctuation, using daily meteorological data from 1961–2015. The population exposure and vulnerability to climate change were then evaluated using demographic data and considering population scale and structure. Finally, the population risk over the TP to climate change was quantitatively assessed within the framework of the Intergovernmental Panel on Climate Change (IPCC). The results showed that the climate change hazard was mainly at medium and heavy levels, in areas accounting for 64.60% of the total. The population exposure was relatively low; the land area at slight and light levels accounted for 83.94%, but high in the eastern edge area of the TP. The population vulnerability was mostly at medium and heavy levels, and the non-agricultural population rate was the key factor affecting the population vulnerability. Generally, the overall population risk over the TP was not very high: the number of counties with heavy and very heavy risk only accounted for 24.29%, and land area was less than 5%. However, more than 40% of the population was in high-risk areas, located in the eastern edge area of the TP. Population exposure was the decisive factor of the population risk to climate change, and high population exposure might lead to high risk. These findings were potentially valuable to improve cognition of risk, develop proactive risk mitigation strategies, and ensure sustainable development.