A Study of Urban Street Green Spaces Based on Green View Index and Normalized Difference Vegetation Index - Focused on Mingcheng District, Xi’an City, Shaanxi Province, China -

초록

As urbanization accelerates and environmental issues multiply, the importance of urban street green spaces becomes increasingly prominent. This study utilizes deep learning for semantic segmentation of street view images (SVI) to calculate the Green View Index (GVI), and combines satellite remote sensing technology to compute the Normalized Difference Vegetation Index (NDVI). A quantitative analysis of GVI and NDVI in the green spaces along the streets of Mingcheng District in Xi’an was conducted. The correlation and spatial distribution between GVI and NDVI were analyzed, and a multiscale geographically weighted regression model was employed to explore the spatial heterogeneity between GVI and NDVI. The results indicate that the GVI of most streets does not reach the values needed for comfortable visual perception, and the GVI decreases with the lowering of road levels. Additionally, the overall green spaces have not yet formed a coherent network, with few concentrated green areas and uneven distribution of greenery. Although there is a positive correlation between GVI and NDVI, spatial heterogeneity exists in the study area. The greening of old urban blocks and level 3 and 4 urban roads urgently need improvement. The findings of this study can provide references for the planning and design of street green spaces, and offer a scientific basis for the evaluation and spatial optimization of future urban street landscapes.

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