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BACKGROUND:The available evidence for positive associations between urban trees and human health is mixed, partly because the assessment of exposure to trees is often imprecise because of, for instance, exclusion of trees in private areas and the lack of three-dimensional (3D) exposure indicators (e.g., crown volume). OBJECTIVES: We aimed to quantify all trees and relevant 3D structural traits in Brussels (Belgium) and to investigate associations between the number of trees, tree traits, and sales of medication commonly prescribed for mood disorders and cardiovascular disease. METHODS: We developed a workflow to automatically isolate all individual trees from airborne light detection and ranging (LiDAR) data collected in 2012. Trait data were subsequently extracted for 309,757 trees in 604 census tracts. We used the average annual age-standardized rate of medication sales in Brussels for the period 2006 to 2014, calculated from reimbursement information on medication prescribed to adults (19-64 years of age). The medication sales data were provided by sex at the census tract level. Generalized log-linear models were used to investigate associations between the number of trees, the crown volume, tree structural variation, and medication sales. Models were run separately for mood disorder and cardiovascular medication and for men and women. All models were adjusted for indicators of area-level socioeconomic status. RESULTS: Single-factor models showed that higher stem densities and higher crown volumes are both associated with lower medication sales, but opposing associations emerged in multifactor models. Higher crown volume [an increase by one interquartile range ðIQRÞ of 1:4 × 10 4 m 3 =ha] was associated with 34% lower mood disorder medication sales [women, b = − 0:341

Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to urban trees has not yet been fully explored—due to the presence of heterogeneous tree species and backgrounds, severely complicating the classification of tree health using remote sensing information. In this study, tree health was represented by a set of field-assessed tree health indicators (defoliation, discoloration, and a combination thereof), which were classified using airborne laser scanning (ALS) and hyperspectral imagery (HSI) with a Random Forest classifier. Different classification scenarios were established aiming at: (i) Comparing the performance of ALS data, HSI and their combination, and (ii) examining to what extent tree species mixtures affect classification accuracy. Our results show that although the predictive power of ALS and HSI indices varied between tree species and tree health indicators, overall ALS indices performed better. The combined use of both ALS and HSI indices results in the highest accuracy, with weighted kappa coefficients (Kc) ranging from 0.53 to 0.79 and overall accuracy ranging from 0.81 to 0.89. Overall, the most informative remote sensing indices indicating urban tree health are ALS indices related to point density, tree size, and shape, and HSI indices associated with chlorophyll absorption. Our results further indicate that a species-specific modelling approach is advisable (Kc points improved by 0.07 on average compared with a mixed species modelling approach). Our study constitutes a basis for future urban tree health monitoring, which will enable managers to guide early remediation management.