Jiang Yu

As the world’s second-largest hydropower station, the reservoir drawdown zone of Baihetan Hydropower Station faces severe ecological disturbance from periodic water level fluctuations; exploring its ecological impact patterns is critical for global reservoir drawdown zone ecological restoration and assessment. Taking the drawdown zone of Baihetan Hydropower Station as the study area, an expert-guided PCA-based assessment framework is proposed: 13 key ecological indicators were screened through expert consultation to ensure ecological relevance and data reliability, followed by objective Principal Component Analysis (PCA) for indicator decorrelation, dimensionality reduction, and comprehensive scoring. Combined with spatiotemporal hot–cold spot and overlay analysis, spatially explicit ecological assessment maps were generated using multi-temporal remote sensing images (covering low-water exposure (July flood season), post-flood impoundment (November), and high-water stabilization (December) periods), spanning a continuous decade from 2016 to 2025 to construct a complete 10-year spatiotemporal cube, which include a 5 km buffer along the mainstream of the Jinsha River. Main results: (1) After the 2021 commissioning, comprehensive ecological quality declined overall, with weakened seasonal ecosystem responses, significant homogenization, and an oscillating cold spot-dominated pattern without stable high-quality hot spots. (2) Marked longitudinal differentiation existed: downstream had better, more stable ecological quality, while upstream showed declining quality and greater hot–cold spot variation associated with reservoir operation and anthropogenic disturbances. (3) Spatiotemporal active points only aggregated briefly in November, indicating autumn–winter impoundment inhibited ecological activity. (4) The first principal component explained 85.52% of variance, with VIIRS nighttime lights, wetness and NDVI as the dominant contributing indicators. The temporal changes reported here are associations with the commissioning of the hydropower station and are not presented as demonstrated causal effects. This study provides a transferable framework for the ecological assessment of hydropower-driven vulnerable areas worldwide.

Hyperspectral image classification (HSIC) plays a crucial role in fine-grained Earth observation tasks. However, balancing efficient long-range dependency modeling with the extraction of fine-grained local features remains a significant challenge, primarily due to the inherent high-dimensional spectral redundancy and complex spatial variability of hyperspectral data. Existing modeling paradigms exhibit distinct limitations: Convolutional Neural Networks (CNNs) are constrained by localized receptive fields, while Vision Transformers (ViTs), despite their global receptive capabilities, incur prohibitive quadratic computational complexity. Meanwhile, the emerging Mamba architecture has demonstrated remarkable effectiveness in sequence modeling with linear complexity, but it often lacks sufficient sensitivity to local textures when directly applied to non-causal 2D images. To address these limitations, this paper proposes a novel parallel hybrid architecture termed DualMambaFormer. Deviating from the traditional serial stacking paradigm, the proposed network utilizes a dual-stream design to achieve the complementary fusion of global static attention and dynamic sequence reasoning. Specifically, the model first employs an SS-ResNet for spectral dimensionality reduction and local feature embedding. Subsequently, the architecture bifurcates into a parallel encoding stage: one branch leverages Multi-Head Self-Attention (MHSA) to capture global spatial correlations, while the other introduces a Local Enhanced Mamba (LEM) branch. By integrating State Space Models (SSM) with depthwise separable convolutions, the LEM branch simultaneously captures long-range causal dependencies and local spatial context. Finally, a dual class token fusion strategy is designed to integrate heterogeneous representations at the decision level. Extensive experiments on four benchmark datasets—Indian Pines, Pavia University, Salinas, and WHU-HongHu—show that DualMambaFormer achieves OA values of 96.56%, 98.95%, 97.60%, and 96.09%, respectively, with consistently high AA and Kappa coefficients. These results demonstrate the effectiveness, robustness, and generalization capability of the proposed method for hyperspectral image classification. Compared with the second-best competing methods, DualMambaFormer improves OA by 5.55, 2.30, 1.68, and 4.30 percentage points on the Pavia University, Indian Pines, Salinas, and WHU-HongHu datasets, respectively.

The water-level fluctuation zone of the lower Jinsha River Basin is a fragile ecosystem with limited water resources in the southwest of China. Soil water is critical for maintaining ecological balance and promoting the healthy development of animals and plants in this region. In this study, eight typical soil profiles were monitored for hydrochemistry in soil-soluble salt from upstream to downstream of the lower Jinsha River Basin. The results show that the soil water content was low and had a negative correlation coefficient with major ions in the soil-soluble phase. The soil-soluble salt is slightly alkaline, and Ca²⁺, Na⁺, SO₄²⁻, and HCO₃⁻ are important components for ions in a soluble phase. Major ions in soluble salt originate from various sources, including precipitation, anthropogenic input, silicate weathering, and carbonate weathering, and the contribution rates from each end member were 0.7%, 45.1%, 25.2%, and 29%, respectively. The ion composition in surface soil soluble salt is mainly influenced by precipitation and human activities, while the ions in the 0–50 cm soil layer originate from the precipitation input and water-rock interaction. In addition, rock weathering is a vital process for releasing ions into soil-soluble salt from 50 to 100 cm soil layer. Our findings provide important references for ion sources and the eco-hydrological process in limited water resources in the water-level fluctuation zone of the lower Jinsha River Basin.