TreeMap 2016 Stand Size Code Field (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated 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. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

TreeMap 2016 提供了美国大陆森林的树级模型。该数据集将森林调查与分析（FIA）的森林样地数据与30x30米（米）网格进行匹配。TreeMap 2016 在私营和公共部门得到应用，用于包括燃料处理规划、树桩危害制图和陆地碳资源估算在内的项目。本研究采用随机森林机器学习算法，将森林样地数据插补至景观火灾与资源管理规划工具（LANDFIRE：https://landfire.gov）提供的靶栅格数据集。预测变量包括森林覆盖率百分比、高度、植被类型以及地形（坡度、海拔和方位）、位置（纬度和经度）、生物物理变量（光合有效辐射、降水量、最高温度、最低温度、相对湿度和蒸汽压亏缺），以及景观2016年左右的干扰历史（干扰时间及干扰类型）。本项目的主要输出（本数据出版物中包含的GeoTIFF文件）是针对美国大陆2016年左右景观条件的30x30米空间分辨率的插补样地标识栅格地图。在此栅格地图的属性表中，我们还展示了一组来自FIA数据库的属性，包括森林类型和活立木基面积。样地标识栅格地图可与通过FIA数据Mart（https://doi.org/10.2737/RDS-2001-FIADB）提供的FIA数据库进行关联。该数据集已通过包括活树覆盖率百分比、优势树高度、森林类型、基面积最大的树木种类、地上生物量、燃料处理规划和树桩危害在内的应用进行验证。在应用本数据集于其他验证范围以外的研究问题时，研究者应在继续之前进行调查。该数据集可能适用于其他应用，并可跨不同尺度（林分、景观和区域）使用，然而，研究者在继续之前应测试数据集对特定研究问题的适用性。此栅格数据集代表由随机森林方法生成的模型输出，该方法将森林调查分析样地标识分配至30x30米网格（Riley等，2016和Riley等，2021）。一些提供的属性已如以下所述进行验证，我们对其在林分、县和国家尺度分析中的适用性充满信心。其他属性截至2022年2月25日的写作时尚未进行验证。精度可能因地区而异。本数据集针对2016年左右的景观，未捕捉该日期之后的火灾和土地管理干扰。基于一组FIA验证样地，这些数据在森林覆盖、高度、植被群组和最近火灾及昆虫病害干扰的点位置上具有中等到高精度（Riley等，2021）。在Baileys部分和子部分级别上的汇总统计数据显示，与FIA的活立木基面积、直径大于或等于1的活树数量、活立方英尺体积和活树生物量统计数据相比，大多数部分和子部分的精度较高。直径大于或等于5的枯树数量和枯树地上生物量的估算与FIA估算的相关性较低，这主要是由TreeMap不包括活树覆盖率低于10%的区域，而FIA则包括这些区域这一事实所驱动的，意味着严重干扰区域未包含在制图中。总的来说，TreeMap数据适用于规划和政策层面的分析和决策。局部地图精度适合许多关于森林覆盖、高度、植被群组和最近干扰的局部尺度决策。对于此处提供的其他属性，尚未完成正式验证，建议在局部尺度上进行评估，并且必须由项目特定需求驱动。参考文献：Riley，Karin L.，Isaac C. Grenfell和Mark A. Finney。2016。《使用改进的随机森林插补FIA森林样地绘制西部美国森林植被图》。Ecosphere 7（10）：e01472。https://doi.org/10.1002/ecs2.1472。Riley，Karin L.，Isaac C. Grenfell，Mark A. Finney和John D. Shaw。2021。《TreeMap 2016：2016年左右美国大陆森林的树级模型》。https://doi.org/10.2737/RDS-2021-0074。本记录摘自向https://data.gov目录提供数据的USDA企业数据目录。本记录的数据包括以下资源：ISO-19139元数据、ArcGIS Hub数据集、ArcGIS GeoService。欲获取完整信息，请访问https://data.gov。