Spatial Imbalance Patterns of Forest Carbon Density and Their Driving Mechanisms in the Xiuhe River Basin

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🔬研究者:Useful for researchers studying spatial carbon accounting and land-use impacts on forest sinks.

🏢実務担当者:Local governments and basin managers can apply the LISA and risk detection methods to prioritize conservation and restoration zones.

🏛政策担当者:Policymakers can use the stage-wise inequality and threshold findings to design spatially differentiated carbon sink enhancement policies.

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.

• semanticscholar https://doi.org/10.3390/f17030312first seen 2026-06-29 08:48:54