Tree View Assessment: Survey of Two Municipalities Located in the Brussels Capital Region

Abstract

Abstract Background Recent literature has highlighted the importance of visual accessibility to nature to reduce stress, anxiety, or depression amongst others. However, green visual accessibility is yet rarely considered in urban policy implementations. Reasons behind this are manifold, and include the challenges associated with the measurability of green views which require data-intensive pedestrian view computations, and assessment methods are yet to be agreed upon. Methods Two methods, Street View Images (SVI) and semantic classification, and geospatial viewshed analysis, were used to compute street level tree views. All street views contained within 2 municipalities from the Brussels Capital Region (BCR) have been studied. Using the SVI method, 15 green view indicators have been proposed. Using the viewshed analysis, the tree view area ratio (TVar) from each SVI geo-location has been computed. The independence between the indicators was evaluated, and using a random forest model, the principal SVI indicators to describe the TVarhave been studied. Results The variability explained by the random forest model was approximately 60% to 70%. The SVI indicators related to the horizontality of green infrastructure and tree canopy explained most of TVar. The results also reveal the tree canopy differences between both municipalities. Conclusions SVI tree view indicators provide acceptable predictions of the TVarwhich could be particularly useful for municipalities with no access to detailed geospatial data. The 30% to 40% of the unexplained variability, could be related to errors derived from the tree canopy geospatial layer, differences in the data collection dates, or geolocation errors of the SVIs.