Wenkai Liu

Improving water resource use efficiency (WRUE) is essential for achieving sustainable water management under increasing socioeconomic and environmental pressures. This study investigates the spatiotemporal evolution, associated factors, and spatial transition characteristics of WRUE across 11 provincial-level administrative regions in the Yangtze River Basin during 2010–2024. An integrated framework combining the super-efficiency SBM-window DEA model, Malmquist–Luenberger index, GeoDetector, and conventional and spatial Markov chain models was developed to characterize efficiency dynamics, productivity changes, explanatory factors, and state-transition pathways. The results showed that WRUE exhibited an overall fluctuating upward trend with a clear spatial gradient of lower reaches > middle reaches > upper reaches. The mean ML index was 1.004, indicating that technological change (TC) was the main contributor to productivity improvement. Urbanization rate, water use per CNY 10,000 of GDP, industrial water-use share, and primary-industry share exhibited relatively high explanatory power, and their interactions enhanced explanatory power. Markov analysis revealed strong persistence in WRUE states, while transition probabilities differed across spatial neighborhood conditions. Assuming stable transition probabilities, the high-efficiency state would reach a steady-state probability of 0.8155. These findings provide insights for differentiated water resource management and coordinated regional development.

Impervious surface expansion is a defining manifestation of urbanization, but changes in total area alone cannot reveal how transitions in development density reshape landscape structure. Adopting an integrated area–density perspective, this study examined impervious surface expansion and landscape reconfiguration in Zhengzhou, China, using Landsat observations from 1990, 2000, 2010, and 2019. Impervious surface fractions were estimated using dynamic endmember linear spectral mixture analysis. Impervious surface area (ISA) was assessed using expansion statistics, spatial autocorrelation, and standard deviational ellipses, while transitions among six impervious surface percentage (ISP) classes were analyzed using transition matrices and landscape metrics. Total ISA increased from 71.12 to 523.34 km². Absolute expansion peaked during 2000–2010, whereas annual expansion intensity declined from 14.70% in 1990–2000 to 4.07% in 2010–2019. ISA expansion exhibited significant positive spatial autocorrelation (Moran’s I = 0.404–0.663), and the footprint retained a southeast–northwest orientation. Natural surfaces declined from 92.16% to 12.75%, accompanied by growth in high-density and fully impervious classes. Decreasing CONTAG and increasing SHDI indicated weakened landscape continuity and greater configurational diversity. Urban development shifted from outward land conversion toward heterogeneous density reconfiguration. Sustainable planning should prioritize reconnecting residual natural surfaces, managing growth in fragmented transition zones, and promoting redevelopment in high-density areas.

The Shendong Mining Area, located in the transition zone between the northern Loess Plateau and the Mu Us Sandy Land, is a representative ecologically fragile region and desert coal base in China. Using Google Earth Engine (GEE) and Landsat imagery from 1999 to 2024, this study constructed a long-term remote sensing ecological index (RSEI) dataset and integrated the Theil–Sen median slope estimator, Mann–Kendall test, and Hurst exponent to examine the spatiotemporal evolution, future trajectories, and multi-stage driving mechanisms of eco-environmental quality (EEQ) at the mining-area and individual-mine scales. At the mining-area scale, RSEI showed pronounced interannual fluctuations and a weak downward trend, characterized by a two-stage, wave-like trajectory with a narrowing amplitude. The mean RSEI and standard deviation decreased from 0.5669 and 0.0855 during 1999–2010 to 0.5182 and 0.0501 during 2011–2024. Moderate and good grades predominated, with lower EEQ in the mining core and higher EEQ in peripheral buffer zones. Across 13 representative mines, ecological quality generally remained moderate but exhibited spatial and stage-dependent heterogeneity, with Liuta Mine showing the greatest variability. Slight degradation was the dominant trend, although local recovery occurred, and Hurst analysis revealed marked spatial differences in future trajectories. RSEI variations in the Shendong Mining Area exhibited pronounced spatial and stage-dependent associations with high-intensity mining, climatic water–heat conditions, topographic background, and ecological governance and restoration processes. No continuous and irreversible overall decline was observed; however, without an independent non-mining control, the findings represent integrated ecological responses within the mining area rather than causal estimates of mining’s net ecological effect.

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is −186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas.

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies.

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, emergency response, and post-disaster management. However, existing deep learning models for landslide segmentation predominantly rely on unimodal remote sensing imagery. In complex Karst landscapes characterized by dense vegetation and severe shadow interference, the optical features of landslides are difficult to extract effectively, thereby significantly limiting recognition accuracy. Therefore, synergistically utilizing multimodal data while mitigating information redundancy and noise interference has emerged as a core challenge in this field. To address this challenge, this paper proposes a Triple-Stream Guided Enhancement and Fusion Network (TriGEFNet), designed to efficiently fuse three data sources: RGB imagery, Vegetation Indices (VI), and Slope. The model incorporates an adaptive guidance mechanism within the encoder. This mechanism leverages the terrain constraints provided by slope to compensate for the information loss within optical imagery under shadowing conditions. Simultaneously, it integrates the sensitivity of VIs to surface destruction to collectively calibrate and enhance RGB features, thereby extracting fused features that are highly responsive to landslides. Subsequently, gated skip connections in the decoder refine these features, ensuring the optimal combination of deep semantic information with critical boundary details, thus achieving deep synergy among multimodal features. A systematic performance evaluation of the proposed model was conducted on the self-constructed Zunyi dataset and two publicly available datasets. Experimental results demonstrate that TriGEFNet achieved mean Intersection over Union (mIoU) scores of 86.27% on the Zunyi dataset, 80.26% on the L4S dataset, and 89.53% on the Bijie dataset, respectively. Compared to the multimodal baseline model, TriGEFNet achieved significant improvements, with maximum gains of 7.68% in Recall and 4.37% in F1-score across the three datasets. This study not only presents a novel and effective paradigm for multimodal remote sensing data fusion but also provides a forward-looking solution for constructing more robust and precise intelligent systems for landslide monitoring and assessment.

Information on urban dynamics is crucial for tracking expansion and investigating urbanization-related environmental impacts. Accurate and timely monitoring of long-term urbanization trends, however, presents significant challenges. Leveraging impervious surface area as a primary metric, this research analyzes spatio-temporal urbanization dynamics and landscape pattern responses in the Zhengzhou metropolitan region between 1990 and 2019, using a novel dual-analytical approach. The total ISA of Zhengzhou increased from 71.12 km ² in 1990 to 523.34 km ² in 2019, with an annual growth area of 15.59 km ² . There was significant positive spatial autocorrelation in the expansion speed of ISA between different regions, and the hot spots with rapid development varied from period to period, namely Erqi, Zhongyuan, and Huiji districts from 1990 to 2000, Huiji, Jinshui, and Guangchegn districts from 2000 to 2010, and Zhongyuan, Huiji, and Guancheng districts from 2010 to 2019. Overall, the impervious surface expansion presented a pattern dominated by the direction of "southeast to northwest". The ISP of each level increased or decreased to varying degrees during the study period, and the change of ISP levels indicated significant change of the pattern of urban landscape, and the overall landscape shape showed a trend from simple to complex. The depiction of the urban impervious surface development trajectory could provide a meaningful resource for urban managers and policy makers implement the national central city construction strategy in Zhengzhou metropolis.

Urban park green space (UPGS) is a key component of urban green infrastructure, yet it faces multiple contradictions, such as insufficient quantity and uneven distribution. Taking Zhengzhou City as a case study, this research explored the impacts of temporal thresholds and the modifiable areal unit problem (MAUP) on UPGS accessibility and equity. An improved multi-modal Gaussian two-step floating catchment area (G2SFCA) method was employed to measure UPGS accessibility, while the Gini coefficient and Lorenz curve were used to analyze its equity. The results show that (1) UPGS presents a dual-core agglomeration feature, with accessibility blind spots surrounding the edge of the study area and relatively low equity in the western and southern regions; (2) changes in temporal thresholds and spatial scales have a significant impact on UPGS accessibility (p < 0.001), whereas their impact on equity is minor; and (3) UPGS distribution suffers from spatial imbalance, with a huge disparity in resource allocation. This study overcomes the limitations of traditional evaluation methods that rely on a single mode or ignore scale effects and provides a more scientific analytical framework for accurately identifying the spatial heterogeneity of UPGS accessibility and the imbalance between supply and demand.

Lakes play a crucial role in regional economic development and ecological construction. The variation in lake water depth has a direct impact on local economic activities, such as agriculture, livestock farming, and fisheries, as well as the stability of hydrological conditions and water ecology. In response to the lack of unified evaluation in the application of remote sensing water-depth estimation models for inland lakes, this study systematically compares the performance of numerical models and machine learning models for water-depth estimation in inland lakes. A machine learning-based water-depth estimation model construction methodology suitable for inland lakes is proposed. This study introduces an innovative approach by integrating machine learning techniques with multispectral remote sensing data, improving the accuracy and applicability of water-depth estimation models for inland lakes. The results show the following: (1) The machine learning models based on random forest (RF), BP neural networks (BP), and AdaBoost demonstrate better performance (R² = 0.88, 0.72, and 0.61; MAE = 0.12 m, 0.24 m, and 0.31 m; RMSE = 0.32 m, 0.48 m, and 0.57 m) compared to the multi-band logarithmic ratio (MLR) model (R² = 0.59; MAE = 0.32 m; RMSE = 0.58 m); (2) the machine learning water-depth estimation model constructed based on this methodology exhibits improved precision (R² = 0.92, 0.89, and 0.80; MAE = 0.11 m, 0.17 m, and 0.25 m; RMSE = 0.25 m, 0.30 m, and 0.41 m). This suggests that the methodology is more suitable for the estimation of water depth in medium- and small-sized lakes; (3) The machine learning model developed in this study, combined with multispectral remote sensing imagery, achieves the accuracy required for the evaluation of water depths for practical water resources. This model enables the rapid acquisition of high-precision underwater three-dimensional topographic maps, providing more accurate and timely hydrological data support for lake water resource management.

The dynamic fluctuations in the real estate market significantly impact the development of the national economy. Investigating the spatiotemporal characteristics of housing prices can assist the government in formulating rational regulatory policies. Taking Zhengzhou City as the research subject, this study analyzed the spatiotemporal characteristics of housing prices based on housing price data and POI (Point of Interest) data from January 2022 to March 2024, utilizing a spatial scale of 500 m × 500 m grids. A hedonic price model and a geographically weighted regression (GWR) model were constructed to examine the mechanisms of 12 influencing factors on housing prices. The results indicate that housing prices in the eastern part of Zhengzhou are higher than those in the west, with an overall declining trend observed in Zhengzhou’s housing prices. Among the influencing factors, the age of the house exerts the greatest impact on housing prices, while finance has the least influence. The GWR model demonstrates superior fitting performance compared to the hedonic price model. The mechanisms of the influencing factors exhibit spatial heterogeneity. This study provides valuable insights for relevant government departments in Zhengzhou City, contributing to the optimization of urban planning and the regulation of the real estate market.