In the heart of Kunming City, a groundbreaking study is reshaping our understanding of urban green spaces and their role in carbon sequestration. Researchers, led by Jianpeng Zhang from the Faculty of Geography at Yunnan Normal University, have developed a novel approach to estimate above-ground carbon (AGC) storage in urban trees, incorporating crown volume structural parameters. This innovative method not only enhances the accuracy of carbon storage estimates but also paves the way for more effective urban ecological planning and carbon management strategies.
The study, published in the journal ‘Trees, Forests and People’, leverages airborne laser scanning (ALS) point cloud data and GF-2 satellite imagery to create a detailed map of carbon storage in Chenggong District. The research team developed an above-ground biomass (AGB) estimation model using the XGBoost algorithm, incorporating tree height, diameter at breast height (DBH), and crown volume as key predictors. The inclusion of crown volume significantly improved the model’s accuracy, with the R² value of the training set increasing from 0.793 to 0.932 and the test set from 0.807 to 0.856.
“This improvement in accuracy is crucial for urban planners and policymakers,” said Zhang. “It allows us to make more informed decisions about urban green spaces and their contribution to carbon sequestration.”
The study estimated a total AGB of approximately 4.51×104 t and AGC storage of 2.26×104 t across the built-up area, with a mean carbon density of 6.69 kg/m2. Notably, Wujaying and Yuhua subdistricts were identified as AGC storage hotspots, highlighting the potential for targeted ecological planning in these areas.
The research also found that traditional vegetation indices and texture features had limited effectiveness in indicating individual tree AGB under high-resolution imagery. This insight underscores the importance of integrating advanced technologies like LiDAR and optical remote sensing for accurate carbon storage estimation.
The commercial implications of this research are substantial. For the agriculture sector, the ability to accurately estimate carbon storage in urban green spaces can inform the development of carbon credit programs and other market-based mechanisms for reducing greenhouse gas emissions. Additionally, the study’s findings can guide the design and management of urban green spaces to maximize their ecological benefits, including carbon sequestration, air quality improvement, and biodiversity conservation.
Looking ahead, this research sets the stage for further advancements in the field. As Zhang noted, “The rapid AGC estimation workflow we established provides robust technical support for urban carbon sink assessment and ecological planning. Future research can build on this foundation to develop even more sophisticated models and methods for carbon management.”
In conclusion, this study represents a significant step forward in our understanding of urban green spaces and their role in carbon sequestration. By incorporating crown volume structural parameters and leveraging advanced technologies, researchers have developed a more accurate and effective approach to estimating above-ground carbon storage. The implications for urban planning, ecological management, and the agriculture sector are profound, offering new opportunities for sustainable development and climate change mitigation.