Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km² to 387.87 ± 11.02 km² from 1975 to 2021, with a decrease rate of 0.86 km² a⁻¹. The rate of area change at a decade timescale was −0.74 km² a⁻¹ (2000–2012) and −1.00 km² a⁻¹ (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km² a⁻¹, −1.83 km² a⁻¹ and −0.42 km² a⁻¹ for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a⁻¹ for 1975–2000 and −0.29 ± 0.02 m w.e. a⁻¹ for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a⁻¹ for 2000–2012 and −0.17 ± 0.01 m w.e. a⁻¹ for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a⁻¹ for 2012–2015, −0.03 ± 0.01 m w.e. a⁻¹ for 2015–2017 and −0.40 ± 0.03 m w.e. a⁻¹ for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.

The Heihe River Basin is located in the arid and semi-arid region of Northwest China; during the past 80 years, this basin has experienced water resource competition between irrigation agriculture and ecological demand in its middle and lower reaches, respectively. The land cover of the Ejin Delta in the lower reaches of the Heihe River Basin was interpreted and analysed for four different periods using a map created by Dr Sven Hedin in the 1930s, Corona satellite images taken in 1961, and Landsat Thematic Mapper (TM) images taken in 2000 and 2010. Overall, the results show that (1) the coarse resolution of the 1930s map increased the uncertainty of analysis in the study area and (2) the river area in the Ejin Delta decreased by 91.0% from the 1930s to 2000. In addition, two major terminal lakes, Gaxun Nuur Lake and Sogo Nuur Lake, dried up in 1961 and 1992, respectively, and the area of Populus euphratica decreased by 76.1% from the 1930s to 2000. Most reeds were overtaken by shrubs between the 1930s and 1961, which caused the area of reeds to decrease from 3481 to 1332 km² and the area of shrubs to increase from 805 to 2795 km². From the 1930s to 2000, the desert and alkaline land areas increased by 42.2% and 52.4%, respectively. (3) After the water transfer project was implemented in 2000, the area of Sogo Nuur Lake recovered to 40.58 km² by 2010. The areas of Populus euphratica, shrubland, and reedland showed a recovering trend, with increases of 4.5%, 6.5%, and 43.5%, respectively, by 2010. The desert and alkaline land areas decreased by 4.2% and 15.2%, respectively, by 2010. The area of cultivated land increased from 25 km² in 1961 to 85 km² in 2000 and rapidly approached 160 km² in 2010. These changes over time indicated that the ecological habitat in the Ejin Delta deteriorated between the 1930s and 2000. However, the water transfer project effectively changed the degradation trend.

Accurately estimating aboveground forest biomass is essential for understanding regional and global carbon cycles. In subalpine forest regions, complex topography and challenging forest inventories introduce significant uncertainties in estimating structural parameters and aboveground biomass. To generate continuous biomass maps over large-scale mountainous forests, it's necessary to extrapolate biomass from sample plots using appropriate models and remote sensing data. This study utilized ground survey data, UAV-LiDAR data, GEDI data, Sentinel-1, and Landsat 8 OLI imagery to estimate forest biomass in Qilian Mountain National Park. Key findings include: (1) UAV-LiDAR-derived parameters, when integrated with a random forest model, effectively predict forest biomass at the sample plot level; (2) combining GEDI data with UAV-LiDAR estimates provides accurate biomass predictions at footprint points; (3) by extrapolating biomass from discrete GEDI footprints and incorporating variables from Sentinel-1 and Landsat 8 OLI, a continuous, high-accuracy forest biomass map for the entire Qilian Mountain National Park was generated (R² = 0.66, RMSE = 19.08 Mg/ha, rRMSE = 11.04%). This study successfully scaled biomass estimates from plots to a regional level, enhancing biomass estimation accuracy in mountainous regions by leveraging complementary data sources.

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R² = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R² = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R² = 0.70, RMSE = 2.75) and medium density (R² = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m² can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China.