The age of a shelterbelt is not only an important parameter for determining the function of a shelterbelt, it is also strongly related to the biomass and carbon flux of shelterbelt ecosystems. Therefore, timely and accurate identifications of shelterbelt ages are key for shelterbelt monitoring and management. This study developed a method for estimating shelterbelt age (i.e., years after planting) from a time series of remote sensing images. Firstly, the shelterbelts were divided into three states based on a single remote sensing image of each. Then, a three-stage growth process was established by analysis. Finally, the shelterbelt ages were determined based on time series remote sensing images over a two-year monitoring period in the study area. The actual shelterbelt ages based on field measurements were used to analyze the accuracy of the results. The total number of samples was 243. The results showed that the age identification accuracy was 68.7%. The main factors affecting the identification accuracy were missing images, cloud cover, and the length of the monitoring period. Despite some uncertainties, the proposed method may be used to obtain critical data for shelterbelt management and conducting quick surveys of current shelterbelt conditions over a large area.

Soil erosion is a notable contributor to carbon emissions. Distributed erosion model can be used to study erosion distribution in different land cover, identify the main influential factors, and hence guide soil and water conservation. In this study, Regional Soil Erosion model (RSEM) was used to simulate the soil erosion processes of Jihe Basin in 2015, and the Multiscale Geographically Weighted Regression (MGWR) implementation was applied to compare the erosion regression of Geographically Weighted Regression (GWR) and MGWR as well as study the impact of influential factors on sediment modulus in hillslopes. The results are as follows: (1) MGWR results indicated slope was the dominant factors affecting soil erosion at the catchment scales, where the average coefficients of slope, forest coverage, and grass coverage descended in the value of 0.90, −0.11, and −0.19, and the influences of factors operate over scales; (2) MGWR with the adoptive bandwidths performed well in the goodness of fit, t-test of variables, scales that variables operate, and interactive interpretation of soil erosion; (3) the coupling effects and scales of vegetation and topography factors are an important approach to study soil erosion at a larger scale.

Harmonizing economic growth and carbon emissions is key to reaching the “dual carbon” targets. This research centers on the seven key urban agglomerations within the Yellow River Basin (YRB) and establishes an integrated research framework of decoupling effect quantification–spatial association recognition–driving factor analysis. By combining the Tapio decoupling model, a modified gravity model, social network analysis (SNA), and the Logarithmic Mean Divisia Index (LMDI) method, the study systematically evaluates the decoupling states, spatial association structure, and driving mechanisms between regional carbon emissions and economic growth from 2001 to 2020. The results show that: (1) All seven urban agglomerations exhibit a simultaneous upward trend in both carbon emissions and GDP, but significant regional disparities exist, with some agglomerations demonstrating a green growth pattern where economic growth outpaces carbon emissions. (2) Weak decoupling is the predominant type among urban agglomerations and their constituent cities in the YRB. Notably, some regions have regressed to growing connection or growing negative decoupling during 2016–2020. (3) The spatial network of carbon emission decoupling effects exhibits a core-periphery structure characterized by stronger eastern regions and weaker western regions, with the Shandong Peninsula and Guanzhong Plain urban agglomerations serving as core nodes for regional linkage. (4) Per capita GDP and technological level play a dominant role in promoting decoupling, while energy intensity and the population carrying intensity of the real economy are the primary inhibiting factors; the impact of industrial structure shows an unstable direction. Grounded in these findings, this study formulates differentiated carbon reduction pathways tailored to regional heterogeneity, providing theoretical insights and actionable guidance to facilitate the low-carbon transition and coordinated governance of urban agglomerations.

To accurately grasp the land and ecological dynamics in the Henan section of the Yellow River Basin (YRB) and provide detailed local data for the ecological protection of the YRB, this article takes the Henan segment within the YRB as the research area, explores the spatio-temporal evolution of land use (LU) and landscape ecological risks (LERS), and predicts LU and LERS under various scenarios in the future based on the PLUS model. We found that: (1) From 2000 to 2020, object types in research area were given priority with cultivated land, forest land, and construction land, with construction land and cultivated land experiencing the largest changes of 5.71% and −6.34%, respectively. Changes in other land types varied within a ±3% range. The expansion of construction land principally encroached upon cultivated land, indicating significant urban sprawl. (2) The high-ecological-risk areas were clustered in the area centered in Zhengzhou, and the low-ecological-risk areas were distributed in the edge of the study area. As risk levels increased, the risk center gradually shifted towards the central regions, particularly around Luoyang and at the junction of Luoyang, Zhengzhou, and Jiaozuo. (3) The LU status in 2030 was projected using the PLUS model under three varied scenarios. The Kappa coefficient of the model was 0.81, and the overall accuracy was about 88.13%. Cultivated land, forest land, and construction land still accounted for the main part, and the area of cultivated land and construction land changed significantly. Based on this analysis of LERS prediction, the distribution of risk levels in different scenarios was different, but in general, high-ecological-risk areas and higher-ecological-risk areas accounted for the main part, while the study area’s edges were where low-ecological-risk zones were situated. Research can offer scientific and technological support for the sensible utilization and administration of resources, along with the protection of the ecological environment and regional sustainable development.

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications.

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services.