Mature and Old-growth Forest Probability Maps for the Conterminous United States

This dataset contains maps of succession-related forest classes across the conterminous US: mature-forest, old-growth-forest, other-forest, and non-forest. The three forest classes have also been combined into an all-forest class. The first map type is a predicted, class presence probability map at 1-ha spatial resolution (the native mapping resolution of this study). The second map type is an aggregated set of class proportion maps at 5-km (25-km2) resolution, representing the proportions of total land area and the proportions of forest land area, for the mature-forest, old-growth-forest, and all-forest classes. To derive these data, a spatial Bayesian modeling framework was developed that integrated U.S. Forest Inventory and Analysis (FIA) plot-level mature and old-growth (MOG) definitions with a stack of data layers derived from both active and passive satellite remote sensing platforms. To reduce spatial heterogeneity in predictor response relationships, CONUS was segmented into 29 strata based on forest composition and U.S. Environmental Protection Agency Level II and III ecoregions, while trying to maintain at least 100 plots in each class within any given stratum. Quadratic discriminant analysis (QDA) models were calibrated and applied independently in each stratum, with the FIA plot MOG labels as the response variable and the remote sensing data as predictors. The maps represent the state of U.S. forests circa 2022, the year that best matches the range of dates used for the remote sensing data, although the inventory data span slightly a wider and earlier range of years of data collection. The data are provided in cloud optimized GeoTIFF (COG) format.

本数据集涵盖美国本土（conterminous US）范围内与森林演替相关的森林类型分布图，包含成熟林、原始老林、其他森林以及非森林四类。研究已将三类森林类别合并为全域森林类。第一种地图类型为1公顷空间分辨率下的预测性类别存在概率图，该分辨率为本研究的原生制图分辨率。第二种地图类型为聚合后的类别占比分布图，分辨率为5千米（对应25平方千米），分别统计了成熟林、原始老林及全域森林类在总陆地面积中的占比，以及其在森林用地面积中的占比。为生成此类数据，研究构建了空间贝叶斯建模框架，整合了美国森林资源清查与分析（U.S. Forest Inventory and Analysis，简称FIA）样地层面的成熟林与原始老林（MOG）定义，以及来自主动与被动卫星遥感平台的多图层数据堆栈。为降低预测变量与响应变量关联关系中的空间异质性，研究基于森林组成与美国环境保护署二级、三级生态区，将美国本土划分为29个分区，同时确保每个分区内的每类森林至少包含100个样地。针对每个分区独立校准并应用二次判别分析（quadratic discriminant analysis，简称QDA）模型，以FIA样地的MOG标签作为响应变量，以遥感数据作为预测变量。本数据集呈现的是2022年前后的美国森林状况，该年份最匹配遥感数据采集的时间范围，尽管样地清查数据的采集年份跨度略广且更早。数据集以云优化GeoTIFF（Cloud Optimized GeoTIFF，简称COG）格式提供。