Modelling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data

Abstract

Urban vegetation is important for the well-being of urban residents. Remotely sensed datasets can be used to efficiently quantify urban green spaces (UGSs) across broad spatial extents. Different methods have been developed to quantitatively describe UGSs using remotely sensed datasets. However, few studies have taken the vertical dimension into consideration in evaluating human interactions with nearby greenery. In this study, a new index, called the ‘3D building proximity to greenery index’ (3DBPGI), is proposed to evaluate the proximity of a building to its nearby urban greenery within a buffer distance by accounting for the building’s height and different vegetation types. The 3DBPGI values for buildings in a Hungarian city, Székesfehérvár, were calculated. The results of the case study show that this index can indicate to some extent the human proximity to greenery for each building block in urban areas, which further can help planners to find critical areas for urban greening programmes.

Keywords:

• urban green space (UGS)
• 3D building proximity to greenery index (3DBPGI)
• building’s nearby greenery (BNG)

Acknowledgements

We thank the editor and anonymous reviewers for their constructive comments. This research was supported by USA NSF [Grant No. 1414108] and China National Natural Science Foundation Grant [No. 41471310].