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For centuries, the rapid development of human society has already made human activity the dominant factor in the terrestrial ecosystem. As the city of greatest importance in China, the capital Beijing has experienced eco-environmental changes with unprecedented economic and population growth during the past few decades. To better understand the ecological transition and its correlations in Beijing, Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images were used to investigate vegetation coverage changes using a dimidiate pixel model. Piecewise linear regression, bivariate-partial correlation analysis, and factor analysis were applied to the probing of the relationship between vegetation coverage changes and climatic/human-induced factors. The results showed that from 2000 to 2005, 2005 to 2010, and 2010 to 2015, Beijing experienced both restoration (6.33%, 10.08%, and 12.81%, respectively) and degradation (13.62%, 9.35%, and 9.49%, respectively). The correlation analysis results between climate and vegetation changes demonstrated that from 2000 to 2015, both the multi-year annual mean temperature (r = −0.819, p < 0.01) and the multi-year annual mean precipitation (r = 0.653, p < 0.05) had a significantly correlated relationship with vegetation change. The Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) has shown beneficial spatial effects on vegetation restoration; the total effectiveness in conservation areas (84.94 in 2000-2010) was much better than non-BTSSCP areas (34.34 in 2000-2010). The most contributory socioeconomic factors were the population (contribution = 54.356%) and gross domestic product (GDP) (contribution = 30.677%). The population showed a significantly negative correlation with the overall vegetation coverage (r = −0.684, p < 0.05). The GDP was significantly negatively correlated with vegetation in Tongzhou, Daxing, Central city, Fangshan, Shunyi, and Changping (r = −0.601, p < 0.01), while positively related in Huairou, Miyun, Pinggu, Mentougou and Yanqing (r = 0.614, p < 0.01). These findings confirm that human activity is a very significant factor in impacting and explaining vegetation changes, and that some socioeconomic influences on vegetation coverage are highly spatially heterogeneous, based on the context of different areas.

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Extensive ephemeral wetlands at Poyang Lake, created by dramatic seasonal changes in water level, constitute the main wintering site for migratory Anatidae in China. Reductions in wetland area during the last 15 years have led to proposals to build a Poyang Dam to retain high winter water levels within the lake. Changing the natural hydrological system will affect waterbirds dependent on water level changes for food availability and accessibility. We tracked two goose species with different feeding behaviors (greater white‐fronted geese Anser albifrons [grazing species] and swan geese Anser cygnoides [tuber‐feeding species]) during two winters with contrasting water levels (continuous recession in 2015; sustained high water in 2016, similar to those predicted post‐Poyang Dam), investigating the effects of water level change on their habitat selection based on vegetation and elevation. In 2015, white‐fronted geese extensively exploited sequentially created mudflats, feeding on short nutritious graminoid swards, while swan geese excavated substrates along the water edge for tubers. This critical dynamic ecotone successively exposes subaquatic food and supports early‐stage graminoid growth during water level recession. During sustained high water levels in 2016, both species selected mudflats, but also to a greater degree of habitats with longer established seasonal graminoid swards because access to tubers and new graminoid growth was restricted under high‐water conditions. Longer established graminoid swards offer less energetically profitable forage for both species. Substantial reduction in suitable habitat and confinement to less profitable forage by higher water levels is likely to reduce the ability of geese to accumulate sufficient fat stores for migration, with potential carryover effects on subsequent survival and reproduction. Our results suggest that high water levels in Poyang Lake should be retained during summer, but permitted to gradually recede, exposing new areas throughout winter to provide access for waterbirds from all feeding guilds.

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Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation.

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