Linking structural traits, growth dynamics, and urban context to understand large urban tree distribution (Strasbourg, France): Research Data.

Description: This repository contains the datasets used to analyze the spatial distribution and 60-year growth trajectories of Large Urban Trees (LUTs) in Strasbourg, France (78 km²). The data were generated by integrating historical Digital Surface Models (DSMs) from 1966 to 2021 with airborne LiDAR and public tree inventories. The data are organized into two main files: 1. Strasbourg All Trees Map (strasbourg_all_trees_map.gpkg) This dataset provides a comprehensive spatial inventory of the urban forest across Strasbourg after Random Forest classification. Content: 219,064 individual trees. Key Variables: * tree_id: Unique identifier from the public inventory. height_m: Tree height in meters derived from the 2021 LiDAR. tree_type: Classification into Large Tree or Others. land_use: Urban context (e.g., parks, water-channel, streets). 2. Strasbourg Tree Trajectories (strasbourg_tree_trajectories.gpkg) This dataset focuses on 2,006 trees for which historical height evolution was reconstructed over six decades (1966–2021). It includes the results of the polynomial regression analysis used to define life-history trajectories. Content: 2,006 trees with long-term height tracking. Key Variables: trajectory_shape: Growth curve labels (e.g., stable_constant, increase_constant, increase_concave/convex). coef_order_1 & p_val_order_1: First-order coefficient (linear slope) and its significance. coef_order_2 & p_val_order_2: Second-order coefficient (polynomial curvature) and its significance. year_start / year_end: Temporal range analyzed. tree_type: Classification into Large Tree or Others. land_use: Urban context (e.g., parks, water-channel, streets).