Urban Neighborhood Green Index – A measure of green spaces in urban areas

度量(数据仓库) 索引(排版) 城市绿地 同种类的 质量(理念) 绿化 环境科学 数学 统计 地理 计算机科学 生态学 空格(标点符号) 物理 数据挖掘 组合数学 操作系统 万维网 生物 量子力学
作者
Kshama Gupta,Pramod Kumar,S. K. Pathan,Kanika Sharma
出处
期刊:Landscape and Urban Planning [Elsevier BV]
卷期号:105 (3): 325-335 被引量:271
标识
DOI:10.1016/j.landurbplan.2012.01.003
摘要

Urban green spaces (UGS) form an integral part of any urban area and quantity and quality of UGS is of prime concern for planners and city administrators. Objective measure of greenness using remote sensing images is percentage area of green, i.e., Green Index (GI), which is insensitive to spatial arrangement within the areal units. Measuring UGS at neighborhood level is important as neighborhood is the working level for application of greening strategies. Neighborhood (NH) is synonymous of nearness and can be defined as an area of homogeneous characteristics. The Urban Neighborhood Green Index (UNGI) aims to assess the greenness and can help in identifying the critical areas, which in turn can be used to identify action areas for improving the quality of green. For the development of UNGI, four parameters, i.e., GI, proximity to green, built up density and height of structures were used and weighted using Saaty's pair wise comparison method. Four different types of NH were compared and it was found that mean GI (0.44) is equal for high-rise low density and low-rise low density NH, i.e., both areas have same quality of urban green based on GI. But mean UNGI is higher for low-rise low-density NH (0.62), as compared to high-rise low-density NH (0.54), hence, area of highrise NH requires more amounts of good quality properly distributed green as compared to low-rise NH. The input for UNGI is easily derivable from RS images, besides the developed method is simple, and easily comprehendible by city administrators and planners.
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