NEON Tree Species Predictions

Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file. Please see the manuscript for detailed methods. Summary We use the DeepForest python package to predict individual crown location in the RGB camera mosaic (Weinstein et al. 2020a). Tree crowns with less than 3m maximum height in the LiDAR derived canopy height model are removed. At this stage in the workflow each individual tree has a unique ID, predicted crown location, crown area and confidence score from the DeepForest tree detection model. Following individual tree detection, we classify each individual as Alive or Dead based on the appearance in the RGB data. Since NEON captures airborne data during the leaf-on season, any standing tree with no leaf cover was annotated as 'dead'. During prediction, the location of each predicted crown is cropped and passed to the Alive-Dead model for labeling as each Alive (0) or Dead (1) with a confidence score for each class. To classify each tree crown to species we use the multi-temporal hierarchical model in Weinstein et al. 2023. Using the best trained model for each site we predict all available areas within the NEON AOP footprint that have overlapping RGB data for crown prediction and hyperspectral data for species prediction. The predicted species label confidence score, as well labels from the higher levels are included in the shapefile. Column Name Definition Geometry A four pointed bounding box location in utm coordinates. indiv_id A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile. sci_name The full latin name of predicted species aligned with NEON's taxonomic nomenclature. ens_score The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model. bleaf_taxa Highest predicted category for the broadleaf model bleaf_score The confidence score for the broadleaf taxa submodel oak_taxa Highest predicted category for the oak model dead_label A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead. dead_score The confidence score of the Alive/Dead prediction. site_id The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. conif_taxa Highest predicted category for the conifer model conif_score The confidence score for the conifer taxa submodel dom_taxa Highest predicted category for the dominant taxa mode submodel dom_score The confidence score for the dominant taxa submodel