An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery

Abstract : Urban areas are major places where intensive interactions between human and the natural system occur. Urban vegetation is a major component of the urban ecosystem, and urban residents benefit substantially from urban green spaces. To measure urban green spaces, remote sensing is an established tool due to its capability of monitoring urban vegetation quickly and continuously. In this study: (1) a Building’s Proximity to Green spaces Index (BPGI) was proposed as a measure of building’s neighbouring green spaces; (2) LiDAR data and multispectral remotely sensed imagery were used to automatically extract information regarding urban buildings and vegetation; (3) BPGI values for all buildings were calculated based on the extracted data and the proximity and adjacency of buildings to green spaces; and (4) two districts were selected in the study area to examine the relationships between the BPGI and different urban environments. Results showed that the BPGI could be used to evaluate the proximity of residents to green spaces at building level, and there was an obvious disparity of BPGI values and distribution of BPGI values between the two districts due to their different urban functions (i.e., downtown area and residential area). Since buildings are the major places for residents to live, work and entertain, this index may provide an objective tool for evaluating the proximity of residents to neighbouring green spaces. However, it was suggested that proving correlations between the proposed index and human health or environmental amenity would be important in future research for the index to be useful in urban planning.
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Soumis le : jeudi 10 mars 2016 - 14:39:20
Dernière modification le : mercredi 12 septembre 2018 - 01:27:45



Xiaojiang Li, Qingyan Meng, Weidong Li, Chuanrong Zhang, Tamas Jancso, et al.. An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery. Annals of GIS, Taylor & Francis 2014, 20 (3), pp.193--203. 〈10.1080/19475683.2014.945482〉. 〈hal-01286202〉



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