The Zoige Plateau is typical of alpine wetland ecosystems worldwide, which play a key role in regulating global climate and ecological balance. Due to the influence of global climate change and intense human activities, the stability and sustainability of the ecosystems associated with the alpine marsh wetlands are facing enormous threats. It is important to establish a precise risk assessment method to evaluate the risks to alpine wetlands ecosystems, and then to understand the influencing factors of ecological risk. However, the multi-index evaluation method of ecological risk in the Zoige region is overly focused on marsh wetlands, and the smallest units of assessment are relatively large. Although recently developed landscape ecological risk assessment (ERA) methods can address the above limitations, the final directionality of the evaluation results is not clear. In this work, we used the landscape ERA method based on land use and land cover changes (LUCC) to evaluate the ecological risks to an alpine wetland ecosystem from a spatial pixel scale (5 km × 5 km). Furthermore, the boosted regression tree (BRT) model was adopted to quantitatively analyze the impact factors of ecological risk. The results show the following: (1) From 1990 to 2016, the land use and land cover (LULC) types in the study area changed markedly. In particular, the deep marshes and aeolian sediments, and whereas construction land areas changed dramatically, the alpine grassland changed relatively slowly. (2) The ecological risk in the study area increased and was dominated by regions with higher and moderate risk levels. Meanwhile, these areas showed notable spatio-temporal changes, significant spatial correlation, and a high degree of spatial aggregation. (3) The topographic distribution, climate changes and human activities influenced the stability of the study area. Elevation (23.4%) was the most important factor for ecological risk, followed by temperature (16.2%). Precipitation and GDP were also seen to be adverse factors affecting ecological risk, at levels of 13.0% and 12.1%, respectively. The aim of this study was to provide more precise and specific support for defining conservation objectives, and ecological management in alpine wetland ecosystems.

The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra daily snow cover product MOD10A1 was compared with in situ climate station measurements and a snow map generated from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Snow-covered area (SCA) dynamics were assessed using the MODIS 8-day snow cover composite product MOD10A2 for the 2001–2005 snow seasons in northern Xinjiang, China. The results indicate that the snow-mapping agreement between MODIS daily snow maps and surface observations is high at 94.6% over the four snow seasons under clear-sky conditions. The snow classification accuracy in a mountainous area was lower than that in a plain area and caused higher omission errors, probably resulting in an underestimation of the SCA. The omission errors were mainly determined by snow depth, land cover types, the terrain and mixed pixels. The cloud agreement was 95.9%, and approximately 4.1% of cloud was misclassified as snow when the sky view at the climate stations was covered by clouds. An improvement was found in suppressing clouds using the 8-day products, with MOD10A2 reducing about 88.4% of the average cloud cover compared with MOD10A1. SCA in northern Xinjiang retrieved using MOD10A2 shows a clear seasonal trend. The air temperature plays an important role in the fractional SCA, and the spatial distribution of the snow cover differs considerably in the various areas in the northern Xinjiang region.

Climate change and human activities are two key factors that affect grassland ecosystem. Accurately estimating the effects of these two factors on grassland dynamics and understanding the driving forces of the dynamics are important in controlling grassland degradation. In this study, the potential Net Primary productivity (NPP_P) and the difference between NPP_P and actual NPP (NPP_A) are used as indicators of climate change and human activities on grassland ecosystem in Xinjiang. An overall grassland NPP_A increase than decrease (69.7% vs 30.3%) is found over the study period of 2000 to 2014. While human activities played a dominant role for such a NPP_A increase, both human activities and climate change contributed almost equally to the grassland NPP_A decrease. Within the three types of grasslands in Xinjiang, the desert grassland showed the greatest NPP_A increasing trend that mostly attributed to human activities; the meadow showed an overall NPP_A decreasing trend that was mainly caused by human activities; the steppe showed similar NPP_A decreasing and increasing trend in terms of area percentage. Based on this study, our recommendations are (1) to continue the grazing prohibition policy in desert grassland and (2) to extensively implement the rest grazing policy in steppe and meadow grasslands.

Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher resolution) products, but are also characterized by high snow and overall classification accuracies (~85% and ~98%, respectively), substantially greater than those of the existing daily snow-cover products for all sky conditions and very similar to, or even slightly greater than, those of the daily MODIS products for clear-sky conditions. Using the snow-cover products, we analyzed the snow cover dynamics over the Tibetan Plateau and determined that the maximum number of snow-covered days (SCD) in a year followed a decreasing tendency from 2003 to 2010, with a decrease in snow-covered area (SCA) equivalent to 55.3% of the total Tibetan Plateau area. There is also a slightly increasing tendency in the maximum snow cover area (SCA), and a slightly decreasing tendency in the persistent snow cover area (i.e., pixels of SCD > 350 days) was observed for the 8-year period, which was characterized by increases in temperature (0.09 °C/year) and in precipitation (0.26 mm/year). This suggests that, on the Tibetan Plateau, changes in temperature and precipitation exert a considerable influence on the regional SCD and SCA, as well as the distribution of persistent snow cover.

Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000–2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America.