Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year⁻¹, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.

Quantifying the impact of climate change and human activities on grassland dynamics is an essential step for developing sustainable grassland ecosystem management strategies. However, the direction and magnitude of climate change and human activities in driving alpine grassland dynamic over the Tibetan Plateau remain under debates. Here, we systematically reviewed the relevant studies on the methods, main conclusions, and causes for the inconsistency in distinguishing the respective contribution of climatic and anthropogenic forces to alpine grassland dynamic. Both manipulative experiments and traditional statistical analysis show that climate warming increase biomass in alpine meadows and decrease in alpine steppes, while both alpine steppes and meadows benefit from an increase in precipitation or soil moisture. Overgrazing is a major factor for the degradation of alpine grassland in local areas with high level of human activity intensity. However, across the entire Tibetan Plateau and its subregions, four views characterize the remaining controversies: alpine grassland changes are primarily due to (1) climatic force, (2) nonclimatic force, (3) combination of anthropogenic and climatic force, or (4) alternation of anthropogenic and climatic force. Furthermore, these views also show spatial inconsistencies. Differences on the source and quality of remote sensing products, the structure and parameter of models, and overlooking the spatiotemporal heterogeneity of human activity intensity contribute to current disagreements. In this review, we highlight the necessity for taking the spatiotemporal heterogeneity of human activity intensity into account in the models of attribution assessment, and the importance for accurate validation of climatic and anthropogenic contribution to alpine grassland variation at multiple scales for future studies.

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

The Qinghai-Tibetan Plateau (QTP) is a global center of cold and dry, where the most extensive fragile alpine vegetation exists. Quantitative analysis of drought event characteristics and vegetation response to drought on the QTP is indispensable for understanding the increasing drought events in a warming climate which exacerbate adverse influence on extremely alpine ecosystems. Here, using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) from 2000 to 2018, this study analyzed the characteristics of drought events, their temporal impacts, and the stability of vegetation response to drought on the QTP. Results showed that: the characteristics of drought events on the QTP have clear spatial heterogeneity. When compared to the east monsoon region, most of the western regions have higher frequency and lower intensity of drought events. Drought has significant temporal effects on vegetation in grassland areas of the QTP during the growing season, which reach their peak in July and August. The 0–1-month and 3-month time scales were the optimal lagged and accumulated time during the growing season, respectively. The stability of vegetation response to drought showed significant spatial heterogeneity and varied with eco-geographical regions and vegetation types. Generally, forest areas showed high resistance (74.09) and resilience (2.26), followed by crop and grassland areas.