Fire Lab tree list: A tree-level model of the western US circa 2009 v1

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012. Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.

美国西部森林内林木数量、规模与物种分布的地图，在诸多场景中具有重要应用价值，例如估算陆地碳资源、预测野火后林木死亡率以及开展森林资源清查。然而，当前技术难以实现大面积区域的精细化林木制图，但通过将森林样地数据与景观生物物理特征相匹配的统计方法，可为利用有限的森林样地清查数据生成覆盖全域的空间数据提供可行路径。本研究采用改进型随机森林（Random Forest）方法，以景观火灾与资源管理规划工具（Landscape Fire and Resource Management Planning Tools, LANDFIRE）的植被与生物物理预测因子作为目标数据，并将美国农业部林务局森林资源清查分析（Forest Inventory Analysis, FIA）采集的样地数据插补至30米（m）栅格分辨率的景观尺度上（Riley等，2016）。该方法基于决策树森林，为每一个栅格化景观数据像素匹配统计最优的样地数据并完成插补。本研究选用LANDFIRE数据集作为栅格化目标数据，原因在于该数据集可公开获取，能够完整覆盖火灾模型所需的各类变量，且与为美国本土生成的燃烧概率、火焰长度概率等其他数据集保持一致。本项目的核心产出（即本数据出版物中附带的GeoTIFF文件）为2009年左右美国西部景观条件下、空间分辨率为30×30米的插补样地标识符地图。该样地标识符地图可通过FIA数据集市（FIA DataMart）获取的FIA数据库，或本数据出版物附带的ACCDB/CSV文件进行关联，进而生成单木尺度地图或其他样地属性地图。上述ACCDB/CSV文件还包含以下属性信息：FIA样地CN（即每次样地测量的唯一标识符）、清查年份、州代码与州简称、单元代码、县代码、样地编号、副样地编号、林木记录编号，以及针对每株林木的相关属性：存活状态（活立木或枯立木）、物种、胸径、树高、实际树高（折断时）、冠幅比、每英亩林木株数，以及每株林木与每次林木调查的唯一标识符。研究人员若需将本数据集应用于与地上生物量和碳循环无关的其他研究问题，需先开展适用性验证。本数据集或可适用于其他应用场景及不同尺度（林分、景观、区域）的研究，但研究人员仍需先验证其针对特定研究问题的适用性后方可开展后续工作。诸多森林景观动态分析与模型均需要能够描述林木物种或森林结构的地理空间数据。森林数据需具备足够的分辨率与连续性，以反映山地地形下的立地梯度，以及野火、木材采伐等历史事件所形成的林分边界。美国大面积公有与私有土地目前尚无此类精细化森林结构数据，此类区域仅依赖低密度固定样地位置的森林清查数据。尽管诸如激光雷达（Light Detection and Ranging, LiDAR）等直接采样技术未来或可实现精细化森林清查的全域覆盖，但当前尚无覆盖美国西部全域的此类数据集。在通过样地CN字段（"PLOT"表中的"CN"字段及其他表中的"PLT_CN"字段）将林木列表栅格（"CN_text"字段）与FIA数据关联时，需注意该字段仅对应单次样地调查。该栅格包含"Value"字段，该字段同样出现在ACCDB/CSV文件的"tl_id"字段中，以便于此类关联操作。本分析中使用的所有样地CN均来自2012年12月从FIA获取的、位于落基山研究站（Rocky Mountain Research Station, RMRS）与太平洋西北研究站（Pacific Northwest Research Station, PNW）范围内的单条件、100%森林覆盖样地。原始元数据日期为2018年1月3日，元数据小幅更新于2019年4月30日。