Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g/m² and 333 g/m², respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g/m². We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management.

Alpine grasslands are important ecosystems on the Qinghai–Tibet Plateau and are extremely sensitive to climate change. However, the spatial responses of plant species diversity and biomass in alpine grasslands to environmental factors under the background of global climate change have not been thoroughly characterized. In this study, a random forest model was constructed using grassland ground monitoring data with satellite remote sensing data and environmental variables to characterize the plant species diversity and aboveground biomass of grasslands in the Three-River Headwaters Region within the Qinghai–Tibet Plateau and analyze spatial variation in the relationship between the plant species diversity and aboveground biomass and their driving factors. The results show that (1) the selection of characteristic variables can effectively improve the accuracy of random forest models. The stepwise regression variable selection method was the most effective approach, with an R² of 0.60 for the plant species diversity prediction model and 0.55 for the aboveground biomass prediction model, (2) The spatial distribution patterns of the plant species diversity and aboveground biomass in the study area were similar, they were both high in the southeast and low in the northwest and gradually decreased from east to west. The relationship between the plant species diversity and aboveground biomass varied spatially, they were mostly positively correlated (67.63%), but they were negatively correlated in areas with low and high values of plant species diversity and aboveground biomass, and (3) Analysis with geodetector revealed that longitude, average annual precipitation, and elevation were the main factors driving variation in the plant species diversity and aboveground biomass relationship. We characterized plant species diversity and aboveground biomass, as well as their spatial relationships, over a large spatial scale. Our data will aid biodiversity monitoring and grassland conservation management, as well as future studies aimed at clarifying the relationship between biodiversity and ecosystem functions.

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.