Yifei Ma

Since the implementation of the Grain-for-Green Program (GGP), vegetation across the Loess Plateau (LP) has substantially recovered. However, whether the associated increase in ecosystem carbon gain was accompanied by a proportional increase in water consumption and whether groundwater storage changed synchronously remain unclear. This study integrated multi-source remote sensing products, GLDAS-Noah land-surface assimilation data, GRACE/GRACE-FO satellite gravimetry, irrigation water-use data, provincial water-use statistics, and coal-resource information to examine long-term changes in gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency (WUE), soil moisture (SM), and groundwater storage anomaly (GWSA) during 2002–2023. GPP increased significantly by 10.67 g C m⁻² yr⁻¹ ($p<0.01$), whereas ET increased more modestly by 1.97 mm yr⁻¹ ($p<0.05$). The relative growth rate of GPP (1.66%) was approximately 3.5 times that of ET (0.47%), and WUE increased by 0.018 g C m⁻² mm⁻¹ yr⁻¹ ($p<0.01$). In the XGBoost–SHAP models for 2004–2019, LAI showed the strongest model-based association with GPP and WUE, whereas ET was associated more broadly with LAI, air temperature, and precipitation. SM declined during 2002–2015 but showed an increasing tendency during 2016–2023, particularly in the middle and deep layers. The long-term GWSA slopes derived from CSR and JPL were −8.707 and −9.505 mm yr⁻¹, respectively, and the averaged CSR–JPL GWSA series showed a Sen’s slope of −9.131 mm yr⁻¹. GWSA declined during 2002–2020 and showed only a short-term, nonsignificant increase during 2020–2023 (4.110 mm yr⁻¹, $p>0.05$). These contrasting trajectories indicate that increases in surface carbon uptake and improvements in soil-water conditions were not accompanied by synchronous regional groundwater recovery. Overall, the ecological-restoration period was accompanied by increased carbon gain and WUE without a proportional increase in regional ET, while groundwater storage followed a distinct trajectory. These findings provide regional-scale evidence and a quantitative basis for coordinating sustainable water-resource management with ecological-restoration optimization on the LP.

Single-UAV dense visual SLAM is often limited by long trajectory accumulation, incomplete local observations, redundant map growth, and onboard computation constraints. Collaborative mapping can distribute a large mission across several local submaps, but dense 3D Gaussian Splatting (3DGS) maps are expensive to exchange and individual Gaussian primitives are not reliable cross-agent matching units. This paper proposes G³M-SLAM, an anchor-guided Gaussian memory framework for UAV-oriented dense SLAM with representation-level submap fusion. Stable geometric anchors organize local Gaussian primitives and form a compact structural interface for submap exchange, overlap recognition, and correction. A hybrid feature-render tracking strategy combines sparse geometric constraints with Gaussian rendering residuals. A generative completion module predicts candidate Gaussians in weakly observed regions, while multi-view geometric verification and an evidence-aware lifecycle mechanism reject unsupported candidates and control redundant map growth. A dual-graph loop bundle adjustment couples the camera pose graph and the anchor memory graph so that corrections can be propagated to anchor-associated Gaussian structures. Experiments on Replica, ScanNet, TUM RGB-D, and EuRoC MAV evaluate local tracking, dense rendering, runtime, and a split-agent fusion protocol. In the latter protocol, fusion reduces ATE from 0.045 m to 0.031 m, translational RPE from 0.030 m to 0.017 m, and rotational RPE from $1.{56}^{°}$ to $0.{93}^{°}$. The serialized anchor packet is 0.66 MB, approximately $101.2\times$ smaller than the complete Gaussian submap. On Jetson AGX Orin, the full system processes EuRoC V101 and V103 at 1.49 FPS and 1.42 FPS, respectively, while ATE increases by only 0.001 m relative to the RTX 3090 Ti workstation results. These gains represent recovery from split-agent degradation and compact representation exchange rather than an improvement over full-sequence single-agent processing. The present study therefore evaluates a representation-level fusion interface and does not reproduce the full conditions of a field-deployed decentralized multi-UAV system.