Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network's Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.


  • Trees are central organisms in maintaining the ecological function, biodiversity, and the health of the planet

  • We extend the workflow by filtering trees using the LiDAR-derived canopy height model (CHM) to remove objects identified by the model with heights of

  • Using the National Ecological Observatory Network (NEON) crowns data set for individual, landscape, and biogeographic scale applications

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Trees are central organisms in maintaining the ecological function, biodiversity, and the health of the planet. Field-based surveys of trees are generally conducted at local scales (~0.1–100 ha) with measurements of attributes for individual trees within plots collected manually. Connecting these local scale measurements at the plot level to broad scale patterns is challenging because of spatial heterogeneity in forests. Many of the central processes in forests, including change in forest structure and function in response to disturbances such as hurricanes and pest outbreaks, and human modification through forest management and fire, occur at scales beyond those feasible for direct field measurement


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