TY - JOUR T1 - NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns JF - bioRxiv DO - 10.1101/2020.09.08.287839 SP - 2020.09.08.287839 AU - Ben. G. Weinstein AU - Sergio Marconi AU - Stephanie Bohlman AU - Alina Zare AU - Aditya Singh AU - Sarah J. Graves AU - Ethan White Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/09/09/2020.09.08.287839.abstract N2 - Forests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of 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 significant technical and computational challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source dataset 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. Tree crowns identified using this technique correspond well with hand-labeled crowns, exhibiting both high levels of overlap and good correspondence in height estimates. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses at scales of ~10,000 ha and cross-region comparisons encompassing forest types from most of the United States.Competing Interest StatementThe authors have declared no competing interest. ER -