Linking carbon sequestration function to plant community typology for designing modular units of carbon-smart urban green spaces: Evidence from Shanghai

Accurate planning of carbon-smart urban green spaces urgently requires a classification system linking plant community structure to carbon sequestration (CS) function. This study integrates multi-source remote sensing data and machine learning algorithms to identify vegetation types, tree height, diameter at breast height, leaf area index, and three-dimensional spatial structural parameters at high resolution in a typical water-network area in Shanghai. Through a five-level hierarchical classification of “characteristic zoning - dominant plant group-CS class - structural type - dominant species,” a multi-level classification framework for plant community spatial units oriented to CS optimization is constructed, generating 2639 encodable community functional units. The core findings are as follows. First, the constructed classification system effectively captures the differentiation patterns of CS efficiency, promoting the shift of community classification toward multi-level clustering that integrates CS characteristics and spatial configuration. Second, the system also reveals a decoupling phenomenon between CS efficiency (NPP, NEP) and CS storage (VegC, TotalC): the communities with high CS storage do not necessarily have high current CS efficiency, the two are independent of each other. Third, the system achieves multi-dimensional differentiation of CS influencing factors. By constructing a Carbon Sequestration-Structure Synergy Index, it subdivides climax communities into mature forest and over-mature forest, confirming that over-mature forest is the key type of functional decline, and simultaneously identifies the multi-layer coverage structure at 0.5 to 0.7 canopy density as the ideal spatial pattern that achieves an optimal balance between CS storage and efficiency. This classification system provides a directly applicable modular design menu for low-carbon planning, design, and management of green spaces in urban spaces, and establishes a more scientific classification foundation for the assessment and precise quantification of their CS enhancement.