This study examines the spatial imbalance and driving mechanisms of human–environment coupling in the Xiuhe River Basin, an important agricultural–ecological watershed in the middle and lower reaches of the Yangtze River. We integrated the coupling-coordination degree (C–D) model, Coupling Elasticity Index (CEI), spatial autocorrelation analyses (global and local Moran’s I, LISA, and Getis–Ord Gi*), and GeoDetector to assess spatial heterogeneity, classify coupling types, and identify key human–ecosystem. The results reveal marked spatial variation in coordination levels, which are higher in the central–western mountains and lower in the southeastern plains (global Moran’s I = 0.7344, p < 0.01), indicating significant spatial clustering. Using the CEI, the basin was classified into ecology-dominated zones (ecological advancement with human retreat), human-dominated zones (human advancement with ecological retreat), and relatively balanced zones. Upstream areas (CEI < 0.6) exhibit ecological advantages, whereas downstream plains (CEI > 1.5) experience dominant human pressures. GeoDetector analysis identified population density, proportion of built-up land, and water quality indicators (COD and TP) as the primary drivers, with interactions substantially enhancing explanatory power (e.g., PD × COD q = 0.86). These findings underscore nonlinear feedbacks and cross-scale interactions that influence coordinated ecosystem services in agricultural landscapes. We recommend differentiated management strategies: conserving upstream ecological functions, promoting balanced development in midstream areas, and regulating development intensity and pollution downstream to sustain human–environment coordination. This study provides quantitative evidence and methodological insights to improve understanding of ecological complexity and optimize governance of the agricultural landscape.

The lotus (Nelumbo nucifera Gaertn) is an aquatic plant that grows in shallow water and has long been cultivated in South China. It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China. However, a modern method is required to accurately estimate the area of lotus fields. Lotus has spectral characteristics similar to those of rice, grassland, and shrubs. The features surrounding areas where it is grown are complex, small, and fragmented. Few studies have examined the remote sensing extraction of lotus fields, and automatic extraction and mapping are still challenging methods. Here, we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields. Using this method, the extraction accuracy of lotus was 96.3%. The Kappa coefficient was 0.926, which is higher than those of the unsupervised K-means classification, Mahalanobis distance, and support vector machine supervised classification, and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.

This study investigates the dynamics of soil CO₂ emissions during the cover period of Phyllostachys violascens and the impact of different cover measures, aiming to provide references for reducing the environmental effects of bamboo cover. An L₂₇ (9¹³) orthogonal experimental design was employed, setting the following variables: (1) heating materials: chicken manure, straw cake, and wheat ash; (2) thickness of husk layer: 15 cm, 25 cm, and 35 cm; (3) soil moisture levels before covering: moisture to 10 cm, 15 cm, and 20 cm. The soil CO₂ emission rate showed a unimodal curve, with a significant overall increase during the cover period. Throughout the entire cover period, the average soil CO₂ emission rate (25.39 μmol·m⁻²·s⁻¹) was 5.1 times higher than that of the uncovered Lei bamboo forest (5.02 μmol·m⁻²·s⁻¹) during the same period. Thicker husk layers (25 cm and 35 cm) corresponded to higher soil CO₂ emission rates, with significant differences noted among the thicknesses. When the soil was moist to 10 cm, the CO₂ emission rate was highest (62.51 μmol·m⁻²·s⁻¹); moisture to 15 cm and 20 cm resulted in significantly lower emission rates. Chicken manure produced the highest peak CO₂ emissions in the third week, at 70.64 μmol·m⁻²·s⁻¹, while straw cake and wheat ash reached their peaks in the fifth week, at 66.56 μmol·m⁻²·s⁻¹ and 57.58 μmol·m⁻²·s⁻¹, respectively. The interactions between the three factors (heating materials, husk layer thickness, and moisture levels) significantly affected the soil CO₂ emission rates. By optimally configuring these factors, CO₂ emissions can be regulated. This study recommends using wheat ash or straw cake as heating materials, combined with a 25 cm husk layer thickness, and moistening the soil to 15 cm before covering. This approach effectively reduces the peak and total soil CO₂ emissions while ensuring suitable soil temperatures for the growth of bamboo shoots in spring. This research provides a scientific basis for the environmental management of bamboo forests, aiding in the optimization of covering measures to achieve low-carbon and sustainable bamboo management.

To explore potential applications of GaoFen-3 (GF-3) fully polarimetric synthetic aperture radar (SAR) data in ground-feature classification, we selected Jiangxiang Town, Nanchang County as the research area and used GF-3 quad-polarization strip I (QPSI) data for classification. Pauli decomposition, Krogager decomposition, and the H / A / α decomposition model were used to decompose the GF-3 full-polarization data; combined with the backscattering coefficient values of GF-3’s HH, HV, VH, and VV polarization data, feature optimization was performed, and the optimal ground-object features for feature classification were obtained. The random forest (RF) method was used to classify the optimal features, and the results were then compared and verified. The results demonstrate that the component after polarization decomposition of H / A / α is more important in classification than the original backscattering coefficient values of HH, HV, VH, and VV. GF-3 single-phase SAR data were used for ground-feature classification in a crop-intensive area of South China, and the optimal feature optimization results included H / A / α decomposition and synthesis components of the alpha, anisotropy, red, entropy, blue, green, and polarization backscattering coefficients of VV. The RF method’s classification accuracy was higher than that of the maximum-likelihood, support-vector-machine, and neural-network methods. Using this approach, GF-3 data classification’s overall accuracy was up to 78.17%, and the Kappa coefficient was 0.738. This study has clear practical significance and reference value for the selection of classification methods for ground features based on GF- 3 data in cloudy and rainy areas.

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls.