The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.

Larix gmelinii is the fourth most important tree species in China and a typical zonal climax species in the cold temperate region, with high ecological and resource value. However, intensive logging, high-density afforestation, and insufficient scientific management have led to overly dense, homogeneous, and unstable plantations, severely limiting productivity. To clarify the mechanisms by which structural dynamics regulate productivity, we established a space-for-time sequence (T1–T3, T2-D, CK) under a consistent early-tending background. Using the “1 + 4” nearest-neighbor framework and six spatial structural parameters, we developed tree and forest spatial structure indices (TSSI and FSSI) and integrated nine structural–functional indicators for multivariate analysis. The results showed that TSSI and FSSI effectively characterized multi-level stability and supported stability classification. Along the stand-age gradient, structural stability and spatial use efficiency improved significantly, with FSSI and biomass per hectare (BPH) increasing by 91% and 18% from T1 to T3, though a “structural improvement–functional lag” occurred at T2. Moderate thinning markedly optimized stand configuration, reducing low-stability individuals from 86.45% in T1 to 42.65% in T2-D, while DBH, crown width, FSSI, and BPH (229.87 t·hm⁻²) increased to near natural-forest levels. At the tree scale, DBH, tree height, crown width, and TSSI were positive drivers, whereas a high height–diameter ratio (HDR) constrained growth. At the stand scale, canopy density, species richness, and mean DBH promoted FSSI and BPH, while mean HDR and stand density imposed major constraints. A critical management window was identified when DBH < 25 cm, HDR > 10, and TSSI < 0.25 (approximately 10–30 years post-planting). We propose a stepwise, moderate, and targeted thinning strategy with necessary underplanting to reduce density and slenderness, increase diameter and canopy structure, and enhance diversity, thereby accelerating the synergy between stability and productivity. This framework provides a practical pathway for the scientific management and high-quality development of L. gmelinii plantations.

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems.