Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

翻译：理解历史森林动态，特别是森林生物量和碳储量的变化，对于评估当前森林气候效益以及预测不同政策、法规和管理情景下的未来效益至关重要。基于国家森林清查的碳核算框架仅局限于大尺度估算，而将此类清查数据与遥感数据相结合的模型驱动方法，能够生成跨景观连续的高分辨率森林生物量与碳储量时序图。本文描述了构建基于地图的碳储量变化框架的关键步骤：利用公开数据与开源工具，以年际30米分辨率对1990-2019年纽约州全境历史森林生物量进行精细时空制图。基于Landsat影像、美国林务局森林清查与分析（FIA）数据及现有机载激光雷达（LiDAR）数据集，我们发展了三种历史森林地上生物量（AGB）制图建模方法：基于FIA样地AGB估算值的直接训练法、基于LiDAR衍生AGB图的间接训练法，以及综合直接与间接模型预测的集成平均法。模型预测图在多个尺度上与FIA估算值进行验证。三种方法均能产生有效输出，但在模型复杂度、制图精度、饱和效应及细尺度格局表征方面存在明显权衡。最终生成的图产品有助于识别由人为与自然驱动因子共同导致的森林碳储量变化的位置、时间与机制。此类产品可服务于碳储量变化评估、监测报告与核查框架，以及优先保护地块或纳入改良管理计划等广泛应用场景。