Monitoring and mapping vegetation greenery in residential communities using street view images and a Normalized Vegetation Greenery Index: a case study in Beijing, China

Frontiers in Forests and Global Change

Forests and Global Change

Xicun Zhu¹  Jiaxuan Song¹  Xinyang Yu² 

[1] College of Resources and Environment, Shandong Agricultural University, Tai’an, China;College of Resources and Environment, Shandong Agricultural University, Tai’an, China;Tropical Research and Education Center, University of Florida, Homestead, FL, United States;

关键词: image processing;    spatial analysis;    urban planning;    support vector machine;    Beijing;   

DOI  :  10.3389/ffgc.2023.1071569

 received in 2022-10-16, accepted in 2023-05-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

Vegetation greenery is essential for the sensory and psychological wellbeing of residents in residential communities. To enhance the quality of regulations and policies to improve people’s living environments, it is crucial to effectively identify and monitor vegetation greenery from the perspective of the residents using effective images and methods. In this study, Baidu street view (BSV) images and a Normalized Vegetation Greenery Index (NVGI) based method were examined to distinguish vegetation greenery in residential communities of Beijing, China. The magnitude of the vegetation was quantified and graded, and spatial analysis techniques were employed to investigate the spatial characteristics of vegetation greenery. The results demonstrated that (1) the identified vegetation greenery using the proposed NVGI-based method was closely correlated with those of the reference classification (r = 0.993, p = 0.000), surpassing the comparison results from the SVM method, a conventional remote sensing classification means; (2) the vegetation greenery was distributed unevenly in residential communities and can be categorized into four grades, 63.79% of the sampling sites were found with relatively low (Grade II) and moderate (Grade III) vegetation greenery distribution, most of the districts in the study area contained zero-value green view index sites; and (3) there was significant spatial heterogeneity observed in the study area, with low-value clustering (cold spots) predominantly located in the central region and high-value clustering (hot spots) primarily concentrated in the peripheral zone. The findings of this study can be applied in other cities and countries that have street view images available to investigate greenery patterns within residential areas, which can help improve the planning and managing efforts in urban communities.

【 授权许可】

Unknown   
Copyright © 2023 Song, Zhu and Yu.