NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Abstract

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.