Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R² = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model’s high accuracy, with coefficients of determination (R²) and root mean square errors (RMSE) indicating strong predictive performance (R² = 0.63, RMSE = 5.06 m for canopy height; R² = 0.64, RMSE = 2.88 m for average height; R² = 0.68, RMSE = 62.84 for density; and R² = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts’ by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.

The Kunlun–Pamir Plateau is a globally irreplaceable biodiversity reserve, yet it is still unclear what causes the distribution of species richness. Here, we relied on the productivity and the water–energy dynamics hypotheses to investigate the distribution pattern of species richness (and its determinants) in the Kunlun–Pamir Plateau. The productivity hypothesis is mainly based on five MODIS products (NDVI, EVI, FPAR, LAI and GPP), which were calculated for three Dynamic Habitat Indices (DHIs): (1) cumulative productivity (CumDHI), (2) minimum productivity (MinDHI) and (3) intra-annual variation productivity (VarDHI). The CumDHI was applied to assess whether or not more energy has a higher species richness value. The MinDHI was used to determine and evaluate the higher minimums, leading to a higher species richness. The VarDHI was the annual variation value in productivity and was utilized to assess if the reduced intra-annual variability triggers a higher species richness. We found that the DHIs based on the FPAR correlated slightly higher with the mammal, bird, breeding bird and non-breeding bird richness (than those based on the other four DHIs, and the values were 0.24, 0.25, 0.24 and 0.01, respectively). The correlation between the climate variables and the mammals, birds, breeding birds and non-breeding birds was bigger at 0.24, 0.54, 0.54 and 0.02, respectively, and was mainly dominated by the precipitation-related climatic factors. The water–energy dynamic hypothesis is better suited to the Kunlun–Pamir Plateau than the productivity hypothesis. Our results might provide valuable information regarding the biodiversity conservation in this region.