A new method for efficient green view index quantification using remote sensing

Abstract

The Green view index (GVI) measures the amount of visible greenery, and thereby is strongly connected to residents' visual perception of the environment, with higher GVI associated with improved health and well-being. GVI is typically calculated using street-view photos, which can only cover a small proportion of the city. This study presents a novel method, the Line-based green view index (LGVI), using remote sensing data, which makes it possible to calculate GVI at any given location in a city. We tested the method in a residential community, which resulted in a Root Mean Square Error (RMSE) of 0.156 when compared to values derived from panoramic photographs. The bias is primarily attributed to the inaccuracies of the input data. Compared with existing GVI methods using remote sensing data, LGVI substantially reduces computational complexity, and thereby enhances the calculation efficiency by converting 3D viewing into linear height projections along sightlines. It further ensures accuracy through distance-based visibility filtering, which realistically simulates human visual occlusion. Consequently, the method achieves an optimal balance between processing time and computational precision. With free access to data of building and tree canopy height for global cities, the proposed method facilitates fast and accurate GVI estimation for cities worldwide.