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<p>Wetlands play a critical role in the global carbon cycle, yet their ecological functions are highly vulnerable to intensive resource exploitation. Coal mining activities in eastern China have led to extensive land subsidence, fundamentally altering wetland landscapes and their carbon sequestration potential. Taking Jining City as a case study, this research integrated SBAS-InSAR deformation monitoring, object-oriented classification, landscape metrics, and the InVEST model to quantify wetland evolution and carbon storage changes from 1993 to 2023. Results showed that total wetland area increased by 125.57&#xa0;km² (10.43%) over three decades, driven largely by the rapid expansion of subsidence wetlands (+ 17.80%), while natural wetlands declined by 31.78%. Despite the increase in wetland area, total carbon storage decreased by 42.38 × 10⁶ t due to the loss of high carbon ensity natural wetlands. However, subsidence wetlands contributed an accumulation of 2.52 × 10⁶ t of carbon, providing partial compensation for the regional loss. Correlation analysis indicated that landscape shape complexity (LSI) and diversity (SHDI) were positively associated with carbon storage, whereas aggregation (AI) showed a significant negative correlation. These findings demonstrate that the spatial configuration of wetlands significantly influences carbon storage capacity. The study suggests that while mining disturbance generally reduces regional carbon stocks, optimizing landscape heterogeneity and connectivity in subsidence wetlands can support ecological restoration efforts in resource-based cities.</p> Graphical Abstract <p>This graphical abstract presents an integrated analytical framework for assessing wetland landscape dynamics and carbon storage in coal resource-based cities, taking Jining as an example. The framework integrates SBAS-InSAR deformation monitoring, object-based hierarchical classification, landscape metrics analysis, and the InVEST carbon storage model to jointly capture geomorphological and ecological processes. Multi-temporal satellite data from 1993 to 2023 were radiometrically and geometrically corrected to ensure long term consistency in monitoring coal mining induced subsidence and wetland evolution. This approach enables precise delineation of subsidence-induced wetlands, quantification of spatial configuration changes, and explicit linkage to variations in carbon storage. The results reveal that greater landscape complexity (LSI, SHDI) enhances carbon sequestration potential, whereas stronger aggregation (AI) constrains it. Although total carbon storage exhibited an overall decline, subsidence wetlands functioned as emerging carbon sinks during the early stages of restoration. Overall, the integrated approach enables the quantitative analysis of interactions between landscape structure and carbon stocks, providing a scientific reference for wetland assessment and management in coal resource-based regions.</p>

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An Integrated InSAR–Landscape–Carbon Framework Linking Wetland Dynamics and Carbon Storage in a High-Groundwater Coal Mining City

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Cankun Li,
  • Jiang Chang,
  • Shiyuan Zhou,
  • Shanshan Feng

摘要

Wetlands play a critical role in the global carbon cycle, yet their ecological functions are highly vulnerable to intensive resource exploitation. Coal mining activities in eastern China have led to extensive land subsidence, fundamentally altering wetland landscapes and their carbon sequestration potential. Taking Jining City as a case study, this research integrated SBAS-InSAR deformation monitoring, object-oriented classification, landscape metrics, and the InVEST model to quantify wetland evolution and carbon storage changes from 1993 to 2023. Results showed that total wetland area increased by 125.57 km² (10.43%) over three decades, driven largely by the rapid expansion of subsidence wetlands (+ 17.80%), while natural wetlands declined by 31.78%. Despite the increase in wetland area, total carbon storage decreased by 42.38 × 10⁶ t due to the loss of high carbon ensity natural wetlands. However, subsidence wetlands contributed an accumulation of 2.52 × 10⁶ t of carbon, providing partial compensation for the regional loss. Correlation analysis indicated that landscape shape complexity (LSI) and diversity (SHDI) were positively associated with carbon storage, whereas aggregation (AI) showed a significant negative correlation. These findings demonstrate that the spatial configuration of wetlands significantly influences carbon storage capacity. The study suggests that while mining disturbance generally reduces regional carbon stocks, optimizing landscape heterogeneity and connectivity in subsidence wetlands can support ecological restoration efforts in resource-based cities.

Graphical Abstract

This graphical abstract presents an integrated analytical framework for assessing wetland landscape dynamics and carbon storage in coal resource-based cities, taking Jining as an example. The framework integrates SBAS-InSAR deformation monitoring, object-based hierarchical classification, landscape metrics analysis, and the InVEST carbon storage model to jointly capture geomorphological and ecological processes. Multi-temporal satellite data from 1993 to 2023 were radiometrically and geometrically corrected to ensure long term consistency in monitoring coal mining induced subsidence and wetland evolution. This approach enables precise delineation of subsidence-induced wetlands, quantification of spatial configuration changes, and explicit linkage to variations in carbon storage. The results reveal that greater landscape complexity (LSI, SHDI) enhances carbon sequestration potential, whereas stronger aggregation (AI) constrains it. Although total carbon storage exhibited an overall decline, subsidence wetlands functioned as emerging carbon sinks during the early stages of restoration. Overall, the integrated approach enables the quantitative analysis of interactions between landscape structure and carbon stocks, providing a scientific reference for wetland assessment and management in coal resource-based regions.