Measuring Community Greening Merging Multi-Source Geo-Data

Abstract

Urban residential greening provides opportunities for social integration and physical exercise. These activities are beneficial to promoting citizens’ mental health, relieving stress, and reducing obesity and violent crimes. However, how to measure the distribution and spatial difference of green resources in urban residential areas have been controversial. This study takes the greening of urban residential units in Shenzhen City as its research object, measures the various greening index values of each residential unit, and analyses the spatial distribution characteristics of residential greening, regional differences, and influencing factors. A large sample of street view pictures, urban land use and high-resolution remote sensing image data are employed to establish an urban residential greening database containing 14,196 residential units. This study proposes three greening indicators, namely, green coverage index, green view index, and accessible public green land index, for measuring the green coverage of residential units, the visible greening of surrounding street space and the public green land around, respectively. Results show that (1) the greening level of residential units in Shenzhen City is generally high, with the three indicators averaging 32.7%, 30.5%, and 15.1%, respectively; (2) the types of residential greening differ per area; and (3) the level of residential greening is affected by development intensity, location, elevation and residential type. Such findings can serve as a reference for improving the greening level of residential units. This study argues that one indicator alone cannot measure the greenness of a residential community. It proposes an accessible public green land index as a measure for the spatial relationship between residential units and green lands. It suggests that future green space planning should pay more attention to the spatial distribution of green land, and introduce quantitative indicators to ensure sufficient green lands around the walking range of residential areas.

How Much Visual Greenery Can Street Trees Generate from a Humanistic Perspective? An Attempt to Quantify the Canopy Green View Index Based on Tree Morphology

“…The green view index (GVI) is the ratio of the vegetation area in the human view to the total human view area [31,32]. The research area of the GVI is widely used in evaluating the greening effect of cities [33][34][35][36][37][38] as well as landscape justice and equity [39][40][41][42][43] and in combining environmental psychology with human well-being and landscape preferences to provide feedback on environmental benefits [44][45][46]. However, most studies about GVI use street view images as the calculation methods, which are shot in the roadway, but there is a serious lack of GVI data from the pedestrian perspective, which directly affects residents' perception of green volume in the city.…”

Exploring the Quality of Urban Green Spaces Based on Urban Neighborhood Green Index—A Case Study of Guangzhou City

“…To some extent, the need for UGS is derived from where people live and how they access UGS, whereas UGS availability results from where UGS is distributed. Moreover, urban form indicators such as structural attributes (e.g., the density and height of buildings) [21], spatial patterns of the population and economy, proximity and accessibility to green areas [13,19]), and even Spatial Syntax approaches have been proposed and applied to understand the need for UGS [22]. Other urban form indicators, such as the network and topology of roads, have also been used as predictors of vegetation cover [2].…”

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

“…Profile imaging systems, such as Google street view, Tencent street view, and Baidu street view (BSV), have similar view angles to residents, and they can present the greenery environment at a very high-resolution level (Gu et al, 2019;Liu et al, 2020;Li et al, 2021). As a result, the street view images can be used to map and quantify the amount of street-side greenery.…”