It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change. Remote sensing as a cost-effective tool offers the potential to update AGB maps for boreal forests in real time. This study evaluates different machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains, northwest China, the study integrated field measurements, space-borne LiDAR, optical remote sensing, and environmental data to develop a training dataset. Among 34 variables, 22 were selected for AGB estimation modeling. Our findings revealed that the LightGBM AGB model had the highest level of accuracy (R² = 0.84, RMSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overestimation. We also observed that the disparity in accuracy among the models widens with increasing altitude. Remarkably, the LightGBM AGB model consistently demonstrates optimal performance across all elevation gradients, with residuals generally below 25 Mg/ha for low-value overestimation and below −38 Mg/ha for high-value underestimation. The model developed in this study presents a viable and alternative approach for enhancing AGB estimation accuracy in boreal forests based on remote sensing technology.

The response of lake-terminating glaciers to climate change is complex, and their rapid changes are often closely linked to glacial-lake outburst floods. However, the eastern Tanggula Mountains, which are the only area where lake-terminating glaciers are found within the Tibetan Plateau, have received little attention to date. In this study, to address this gap, we generated updated glacier boundaries and estimated the interdecadal area changes for 2000–2020 based on the interpretation of Landsat-5/8 and Sentinel-2 images. In addition, based on the method of digital elevation model (DEM) differencing, we quantified the changes in glacier thickness and mass balance using TanDEM-X radar data and SRTM DEM over almost the same periods. The final results show that the glaciers in the eastern Tanggula Mountains, as a whole, have experienced accelerated area shrinkage (with a rate of area loss increasing from −0.34 ± 0.83 km² a⁻¹ to −0.93 ± 0.81 km² a⁻¹ for 2000–2013 and 2013–2020, respectively) and accelerated ice thinning (changing from −0.19 ± 0.05 m a⁻¹ and −0.53 ± 0.08 m a⁻¹ for 2000−2012 and 2012–2020, respectively). Furthermore, the region-wide glacier mass balance was −0.16 ± 0.04 m w.e. a⁻¹ and −0.45 ± 0.07 m w.e. a⁻¹ for these two sub-periods, corresponding to a 1.8 times acceleration of mass loss rate. The average mass balance during 2000–2020 was −0.23 ± 0.04 m w.e. a⁻¹, which is equivalent to a rate of mass loss of −0.04 Gt a⁻¹. More specifically, within the region, the lake-terminating glaciers have exhibited more significant acceleration of area loss and mass loss, compared to the land-terminating glaciers. However, interestingly, the average thinning rate of the lake-terminating glaciers is always lower than that of the land-terminating glaciers over all study periods, which is in contrast with previous findings in other high mountain areas (e.g., the Himalaya Mountains). Field study and proglacial lakes monitoring suggest that the local topography plays a vital role in the evolution of the glacial lakes in this region, which further affects the glacier changes. Furthermore, the present status of the glacier changes in this region can be attributed to the long-term increase in air temperature. Our findings provide a comprehensive overview of the current state of glacier changes across the eastern Tanggula Mountains and will help to improve the understanding of the heterogeneous response of glaciers to climate change.

Larix spp. are the key dominant species in boreal and temperate forests of the Northern Hemisphere, playing a vital role in the global carbon cycle and ecosystem stability while being highly sensitive to climate change. In this study, we applied an ensemble modeling framework integrating 12 algorithms to assess the potential suitable habitats and centroid shifts of 10 Larix species across the Northern Hemisphere under current and future climate scenarios. Model performance was high (true skill statistic, receiver operating characteristic, and Kappa all >0.9). Temperature, precipitation, and elevation were identified as the primary environmental drivers shaping species distributions. Substantial heterogeneity was observed in future habitat shifts: some species (e.g. Larix decidua, Larix sibirica, Larix lyallii ) are projected to undergo significant habitat contractions, while others (e.g. Larix potaninii, Larix gmelinii var. principis-rupprechtii ) may experience expansions. For most species, distribution centroids are expected to shift northeastward or northwestward, with more pronounced shifts under high-emission scenarios. These findings reveal the complex response patterns of Larix species to climate change and provide scientific insights for cross-regional forest conservation and adaptive management. We recommend prioritizing conservation of species projected to undergo substantial habitat contraction and safeguarding climate refugia to maintain biodiversity and ecosystem functioning.