Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region.

The construction and operation of onshore wind farms interfere with the succession of local plant communities, and the impacts on the local ecology and climate are of great concern. The study of the relationships between onshore wind farms and local ecology and climate, as well as the accurate assessment of the impacts of onshore wind farms on local areas, are the foundation for promoting the sustainable development of green energy. In this study, we summarize the existing research methods used for field data monitoring, remote sensing data inversion and numerical model simulation, and found that onshore wind farms have obvious impacts on the local vegetation index, near-surface temperature, wind speed, soil moisture, and other parameters. Onshore wind farms reduce the local soil moisture content, increase the near-surface air temperature, and significantly alter local wind speeds. They also cause a reduction in the local vegetation index, inhibition of plant growth, and an increase in the mortality rates of birds and bats inside the wind farms. However, onshore wind farms have positive effects on the plant communities outside the wind farms, especially in the downwind direction. Overall, there is regional variability in the results and the findings are not generalizable. The mechanisms by which the onshore wind farms influence the local climate, the impact of climate on local ecology, and the direct effects of onshore wind farms on local ecology have not been clearly and accurately explained. Related research is still needed to further improve the precision, accuracy, and continuity of observational data. The construction of modeling systems also needs to incorporate indicators such as land use type, local microclimatic indicators, and plant species. Based on these considerations, this review provides support for macroscopically understanding the impacts of onshore wind farms on climate and ecology.

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (T_max), daily minimum temperature (T_min), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to T_max, T_min, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. T_max and T_min mainly showed a decreasing trend in winter (p > 0.05). In spring, T_max mainly showed an increasing trend (p > 0.05) and T_min a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in T_min in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by T_min in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future.