Rare and endangered plants are irreplaceable "living fossils." They underpin global biodiversity. Yet fragmented policies and static reserves expose most habitats and corridors to rapid climate and land-use change. We present a climate-adaptive framework. It combines MaxEnt models, hotspot detection, circuit-theory connectivity and network optimization. We apply this workflow to Sichuan Province. Here, 89.96 % of ecological sources and 89.41 % of corridors lie outside protected areas. Under SSP126/370/585 scenarios, 31.5 % of today's hotspots will lose that status by 2071-2100. A five-dimensional human disturbance index reveals that corridors face 4.8-fold more moderate-to-high pressure than sources. Guided by these findings, we have designed three ecological network optimization schemes for different future scenarios. All of them have effectively improved the network topology structure indicators and are economically feasible. The framework offers transferable tools for network-oriented governance of rare and endangered plants under rapid global change.

Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them.

Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows and doors. To address these limitations, this study proposes an automatic 3D building reconstruction method driven by façade geometry. The proposed method introduces three key contributions: (1) a façade-guided footprint generation strategy that eliminates geometric distortions associated with roof projection methods; (2) robust detection and reconstruction of façade openings, enabling reliable identification of windows and doors even under sparse ALS conditions; and (3) an integrated volumetric modeling pipeline that produces watertight models with embedded façade details, ensuring both structural accuracy and semantic completeness. Experimental results show that the proposed method achieves geometric deviations at the decimeter level and feature recognition accuracy exceeding 97%. On average, the reconstruction time of a single building is 91 s, demonstrating reliable reconstruction accuracy and satisfactory computational performance. These findings highlight the potential of the method as a robust and scalable solution for large-scale ALS-based urban modeling, offering substantial improvements in both structural precision and semantic richness compared with conventional roof-based approaches.

Wetlands play an irreplaceable role in maintaining ecological balance and conserving biodiversity. However, driven by both natural and human factors, vast wetland areas worldwide are being rapidly converted into agricultural or urban land, leading to a sharp decline in their extent and a degradation of their quality. Against this backdrop, remote sensing technology, with its unique advantages in macro-level monitoring and multi-temporal dynamic capture, provides indispensable technical support for the precise identification, classification, and change detection of wetlands, making it an essential tool for wetland monitoring, restoration assessment, conservation planning, and SDG-related evaluation. This review examines recent advances in remote sensing for detailed wetland classification over the past 5 years. It elaborates on commonly used remote sensing data sources, local and international wetland classification standards, diverse classification methods, accuracy evaluation metrics, and prospects. This review provides a comprehensive reference to remote sensing-based wetland classification studies and applications.

Land-use/land-cover change (LULC) fundamentally alters regional ecosystem service provisioning, necessitating multi-scenario simulations coupled with multi-objective optimization (MOP) to inform territorial spatial governance. This study develops an integrated PLUS-InVEST-MOP framework to simulate land-use dynamics and their ecological consequences in the Haibei Tibetan Autonomous Prefecture, a critical ecological barrier on the northeastern Qinghai–Tibet Plateau. Utilizing GlobeLand30 data (2000–2020), we projected 2030 land-use patterns under four scenarios (natural development, ecological conservation, economic development, and climate change) and quantified spatiotemporal variations in carbon storage and habitat quality. Results showed that grassland degradation accelerated during 2010–2020, with 1202.43 km² converted primarily to bare ground, contrasting with restoration gains observed in 2000–2010. Elevation emerged as the predominant natural driver of glacial and forestland expansion, whereas demographic and economic factors governed land encroachment on artificial surfaces. Under the climate change scenario, habitat quality declined by 3.78% (to 0.596), and carbon storage decreased by 70.04 Tg relative to 2020, underscoring the vulnerability of alpine ecosystems to warming. Conversely, the MOP-optimized scenario achieved synergistic improvements in carbon sequestration (+1.8% vs. 2020) and habitat quality (+2.3% vs. 2020) while adhering to arable land preservation targets (≥54,800 ha) and constraints on artificial surfaces. These findings provide quantitative decision support for balancing ecological conservation and sustainable development in high-altitude cold regions.

With the advancement of urbanization and motorization, urban traffic conditions increasingly affect both travel efficiency and system stability, yet existing studies based on high-frequency OD data mainly focus on single aspects such as congestion patterns or travel time variability, lacking a unified analytical framework that jointly captures time-varying travel impedance, reliability, and anomaly risks under comparable conditions, especially in cross-city contexts. This study constructs a standardized analytical framework with a novel integration based on a “city × weekday × 5 min interval” structure, using high-frequency navigation OD data from eight major cities in China over four consecutive weeks, totaling approximately 560,000 valid samples. Travel Time per Unit Distance (TTUD) is employed as the core metric, and a distance-stratified weighting approach is adopted to improve cross-city comparability. Reliability is characterized by variability, dispersion, and tail risk, and anomalous events are identified using a dynamic baseline. The results reveal clear intra-week temporal regularity and significant inter-city heterogeneity, with weekday evening peaks generally lasting longer than those on weekends, reflecting sustained commuting pressure and slower dissipation of travel demand. A total of 249 anomaly events are detected, with higher frequency and persistence on weekdays, highlighting the increased vulnerability of traffic systems during peak commuting periods and indicating that commuting periods are more prone to sustained deviations due to higher system load and demand instability. Overall, the proposed framework provides a unified and comparable basis for cross-city traffic performance evaluation and supports practical applications such as peak-period traffic management, congestion mitigation, and traffic risk monitoring.