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This research aims to quantify the carbon sequestration capacity of restored degraded wetlands around Chaohu Lake.

July 15, 2026Sustainability0 citationsOpen Access

Evaluation of Carbon Sequestration of Restored Degraded Lakeside Wetlands Around Chaohu Lake Based on GIS and Machine Learning

ZWZifang WangChina Resources (China)CYChangming YangTongji University XZXi ZhangGeneral Cardiology

Key Points

This research aims to quantify the carbon sequestration capacity of restored degraded wetlands around Chaohu Lake.
Integrates remote sensing, GIS, and machine learning techniques.
Employs the XGBoost model to predict carbon sequestration based on historical data.
Analyzes carbon density changes from 2017 to 2024.
Average carbon density improved from 48.70 t ha−1 in 2017 to 90.18 t ha−1 in 2024 (85.2% increase).
Model validation showed moderate prediction errors (RMSE = 0.47–0.74) with stable performance.
Results suggest large-scale restoration enhances regional carbon sink capacity effectively.

Abstract

With the acceleration of global urbanization and intensified agricultural activities, approximately 61% of the world’s wetlands have degraded over recent decades, significantly weakening their carbon sequestration capacity. The Shibalianwei Wetland, a crucial tributary system of Lake Chaohu in China, has suffered severe degradation due to land use and cover change, nutrient loading and hydrological disruption. In response, large-scale ecological restoration has been implemented since 2018. To quantify the restoration outcomes, this study integrated remote sensing, GIS, and machine learning techniques, employing the XGBoost model to evaluate and predict carbon sequestration in 2017 and 2024 based on 2010 carbon data. The results reveal that the average carbon density increased from 48.70 t ha−1 in 2017 to 90.18 t ha−1 in 2024, representing an overall increase of 85.2% in total carbon storage. This substantial enhancement is primarily attributed to land use transitions and ecosystem-scale restoration effects, including vegetation recovery and hydrological rehabilitation. Model validation indicated moderate prediction errors (RMSE = 0.47–0.74), with consistent performance across repeated iterations. Together with complementary MAE and R2 metrics, the results suggest that the XGBoost model is capable of capturing relative spatial patterns and restoration-induced changes in wetland carbon sequestration, while retaining reasonable predictive stability under changing landscape conditions. Overall, the findings demonstrate that large-scale wetland restoration can rapidly and effectively enhance regional carbon sink capacity and highlight the potential of data-driven modeling frameworks to support wetland management and carbon-neutrality strategies. This provides important guidance for policymakers to promote sustainable land use and optimize ecosystem management under China’s dual-carbon development goals.

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