Abstract
The suburbs around Shanghai have a complex river network and a unique Chinese water-town culture. The riparian landscape in the rural Qingxi area has important regional, ecological, and social significance; it serves as an important part of the local bioclimate, but the existing studies on river vegetation did not pay enough attention to the riparian landscape in the countryside around the metropolis. The goal of this study was to examine a comprehensive evaluation model for the river plant landscape in the countryside surrounding a high-density metropolis such as Shanghai in the face of the national policy of rural revitalization and the low-carbon development problem, and to propose optimization strategies accordingly. Therefore, in this study, we selected 91 rivers in the Qingxi area and investigated their plant communities. According to the characteristics of the riparian landscape and its relationship with the river environment and local bioclimate, we classified the 91 riparian landscapes into four types of quadrats: natural landscape, residential recreation, roadside linear landscape, and agricultural landscape. In addition, based on the 13 indicator layers under the categories of ecological carrying capacity, landscape beauty, and social service, we calculated the comprehensive evaluation value (CEV) and comprehensive evaluation index (CEI) of 91 river quadrants using specific formulas to scientifically evaluate the riparian landscape in the rural Qingxi area of Shanghai. Finally, based on the existing problems summarized through data analysis, the researchers proposed five optimization directions: (1) increasing vegetation diversity, (2) choosing native and culturally representative species, (3) improving waterfront planting design, (4) achieving ecological riverbank construction, and (5) building greenway systems and recreational spaces. This study proposed an innovative evaluation model for the riparian vegetation landscape and tested its feasibility by site survey, which provided new visions for future rural landscape research.
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