NEON Tree Species Predictions
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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
美国国家生态观测站网络（National Ecological Observatory Network, NEON）1亿株树木的单木预测数据集。站点缩写查询方式详见：https://www.neonscience.org/field-sites/explore-field-sites。每个站点对应一份.zip压缩包与一份.csv文件：其中.zip压缩包包含1km分辨率的.shp格式瓦片；.csv文件为整合全部树木数据的单文件。详细研究方法请参阅相关手稿。 ### 数据集方法概述 本研究使用DeepForest Python工具包对RGB影像镶嵌图中的单木树冠位置进行预测（Weinstein等，2020a）。我们移除了激光雷达（LiDAR）衍生冠层高度模型中最大高度低于3米的树冠。在此流程阶段，每株单木均拥有唯一标识符、预测的树冠位置、树冠面积以及来自DeepForest树木检测模型的置信度评分。 完成单木检测后，我们基于RGB影像中的外观特征将每株单木分类为「存活」或「死亡」。由于NEON的航空影像采集于叶片茂盛季，因此任何无叶片覆盖的立木均被标注为「死亡」。预测阶段，我们将每个预测树冠的区域裁剪后输入存活-死亡分类模型，以类别置信度评分标记为「存活（0）」或「死亡（1）」。 针对单木树冠的物种分类任务，我们采用Weinstein等2023年提出的多时间层级分类模型。针对每个站点使用最优训练模型，对NEON航空观测平台（Airborne Observation Platform, AOP）覆盖范围内所有具备重叠RGB影像（用于树冠检测）与高光谱数据（用于物种分类）的区域进行预测。 本数据集的Shapefile文件中包含了预测的物种标签置信度评分，以及层级分类各高阶类别的标注结果。 #### 字段说明 | 字段名 | 详细说明 | | ------ | -------- | | `geometry` | 以UTM坐标系表示的四角边界框位置。 | | `indiv_id` | 唯一树冠标识符，由NEON航空瓦片的采集年份、站点名与地理索引（例如`732000_4707000`，即瓦片左上角的UTM坐标）组合而成。 | | `sci_name` | 与NEON分类命名体系对齐的预测物种完整拉丁学名。 | | `ens_score` | 物种分类预测的置信度评分，为多时间层级集成模型的输出结果。 | | `bleaf_taxa` | 阔叶树分类子模型的最高预测类别。 | | `bleaf_score` | 阔叶树分类子模型的置信度评分。 | | `oak_taxa` | 栎树分类子模型的最高预测类别。 | | `dead_label` | 基于RGB影像的存活/死亡二分类标签：0=存活，1=死亡。 | | `dead_score` | 存活/死亡分类预测的置信度评分。 | | `site_id` | NEON站点的四位字母代码，站点位置查询详见：https://www.neonscience.org/field-sites/explore-field-sites。 | | `conif_taxa` | 针叶树分类子模型的最高预测类别。 | | `conif_score` | 针叶树分类子模型的置信度评分。 | | `dom_taxa` | 优势类群分类子模型的最高预测类别。 | | `dom_score` | 优势类群分类子模型的置信度评分。 |
