The alpine grasslands of the Qinghai–Tibet Plateau are one of the most famous grazing ecosystems in the world, providing a variety of ecosystem functions and services. The rate of grassland degradation has been slowed by the implementation of national grassland restoration projects, but the degradation of grasslands on the Qinghai–Tibet Plateau has not yet been fundamentally reversed, and some grasslands are still degraded to varying degrees. The main causes of grassland degradation on the Qinghai–Tibet Plateau are both human and natural factors. Human factors include overgrazing, over-cultivation, indiscriminate digging and mining, mineral resource development, infrastructure construction and use, and tourism development. Natural factors include climate change, wildlife destruction, pests, etc. Based on the principles of restoration ecology, a number of effective practices and integrated management responses for restoring degraded grasslands have been developed on the Qinghai–Tibet Plateau. The degraded grassland restoration practices include fencing, fertilization, sown grassland establishment, rodent control, and grazing management. Based on these practices, the comprehensive restoration of degraded grasslands and the establishment and sustainable management of sown grasslands in the alpine grasslands of the Qinghai–Tibet Plateau should be further strengthened, and research on the mechanisms of grassland degradation and restoration should be further developed.

An ecological restoration assessment aims to evaluate whether ecological restoration projects (ERPs) have achieved predefined ecological objectives, such as improving fractional vegetation cover (FVC) and enhancing ecosystem services (ESs), as well as to optimize restoration strategies based on assessment outcomes. Despite recent advancements, current studies still fall short of fully capturing the trade-offs among ESs and identifying the underlying drivers of different vegetation trends. To address these challenges, we applied the Theil–Sen method to delineate vegetation change zones in the Qilian Mountain National Park (QLMNP) between 2000 and 2020, employed bivariate Moran’s I statistics to analyze the trade-offs and synergies among four ESs within these zones, including carbon sequestration (CS), soil conservation (SC), water conservation (WC), and biodiversity maintenance (BIO), and utilized a spatial random forest (SRF) model to explore the main socio-ecological driving factors of vegetation trends and their spatial distribution. Our results revealed significant vegetation recovery in the QLMNP between 2000 and 2020, particularly in regions with initially low FVC. Positive trends in the CS, SC, and BIO highlighted the success of restoration efforts, primarily driven by land conversion to forests and increased precipitation. However, 8.82% of the QLMNP exhibited stagnation or degradation due to rising temperatures and overgrazing, leading to declines in the SC and BIO. Notably, vegetation restoration introduced trade-offs among the ESs, especially in the high FVC areas, where a strong trade-off emerged between FVC and WC. These findings highlight the need for refining restoration strategies to balance water resource allocation. Finally, we integrated vegetation trends, ES relationships, and driving factors to propose grid-based zonal governance plans for the QLMNP, prioritizing WC and FVC enhancement as critical components of future ecological planning. This study serves as a foundation for optimizing restoration strategies in the QLMNP, maintaining and enhancing ESs, while offering actionable insights for fine-grained restoration evaluation and sustainable development planning in other regions.

Scene Graph Generation (SGG) aims to represent visual scenes as structured graphs, where objects and their pairwise relationships are modeled as relational triples. However, conventional SGG methods often struggle with two crucial limitations: inadequate modeling of rich spatial relations beyond 2D layouts, and poor generalization in open-vocabulary scenarios. To address these limitations, we propose SPIN-SGG, a novel framework that explicitly integrates multi-dimensional spatial information—including planar geometry, relative depth, and topological structure—into an open-vocabulary SGG pipeline, without requiring ground-truth 3D annotations. Our approach builds upon a two-stage design. First, we generate pseudo-3D scene reconstructions from monocular images using multi-view synthesis and point cloud estimation, and construct a spatially enriched instruction-tuning dataset that includes fine-grained spatial predicates (e.g., left of, above, inside). Second, we propose a spatially aware visual-language model, trained with both static scene graph descriptions and dynamic spatial reasoning tasks such as question answering and multi-turn dialogue. To further enhance spatial layout consistency, we incorporate layer-aware clustering and object-level depth anchoring in the scene parsing module. Extensive experiments on the PSG benchmark and a newly curated SpatialSGG dataset demonstrate that SPIN-SGG significantly outperforms previous open-vocabulary SGG methods, with improvements of +1.3% in mR@50 on PSG and +2.7% QA accuracy on SpatialSGG, showcasing its robust and comprehensive spatial reasoning capabilities.

Low Earth orbit (LEO) satellites are increasingly required to process and securely stream video data for remote sensing, surveillance, and onboard perception applications. However, the strict constraints of onboard computing capability, power budget, and thermal dissipation make conventional encryption schemes difficult to apply to real-time video streaming tasks. To address this challenge, this paper proposes a low-complexity real-time video encryption algorithm for resource-constrained LEO satellites. The proposed method integrates selective encryption with a lightweight permutation–diffusion mechanism to reduce computational overhead while maintaining effective protection of continuous video streams. To enhance security, a chaotic pseudo-random sequence generator is employed to improve encryption randomness, and a dynamic key scheduling strategy is introduced to increase temporal key variability and strengthen resistance to statistical and differential attacks across successive frames. The algorithm is further designed for efficient deployment on embedded onboard platforms with limited hardware resources. Experimental results show that the proposed method achieves favorable performance in encryption speed, computational complexity, information entropy, adjacent pixel correlation, and differential attack resistance. Compared with conventional full-encryption methods, the proposed algorithm offers a more balanced trade-off between security and real-time efficiency, demonstrating its potential for secure video streaming in resource-constrained LEO satellite systems.

Panoptic Scene Graph Generation (PSG) aims to segment objects and predict the relation triplets within an image. Despite the impressive achievements in PSG, current methods still struggle to capture fine-grained visual context, eschewing spatial and situational information in favor of visual features related to object identity. This limitation naturally impedes the model’s ability to distinguish subtle visual differences between relation triplets, such as “cat-on-person” and “cat-lying on-person”. To address this challenge, we propose CVCPSG, a novel DETR-based method that uncovers composite visual clues for PSG. Specifically, drawing inspiration from how humans capture visual context using diverse visual clues, we first construct a composite visual clues bank based on three key aspects: object, spatial, and situational. Then, we introduce a multi-level visual extractor to align visual features from objects, interactions, and image levels with the composite visual clues bank. Additionally, we incorporate a cross-modal learning module with a multitower architecture to seamlessly integrate visual clues into the relation decoder, thereby improving PSG detection. Extensive experiments on two PSG benchmarks confirm the effectiveness and interpretability of CVCPSG.