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库的变化,对于评估当前森林气候利益及在各种政策,监管和管理情况下预测未来效益至关重要。基于国家森林清查的碳计量框架仅限于广泛的估计,而将这些清查与遥感数据结合的基于模型的方法可以产生横跨全景的细分精细分辨率森林生物量和碳库地图。这里我们描述了建立映射库存变化框架的基本步骤:在时间和空间上的所有纽约州地块(1990年至2019年)上使用免费数据和开源工具映射历史森林生物量,分辨率为年度和30米。利用Landsat影像,美国森林局森林清查和分析(FIA)数据和现成的LiDAR收集,我们开发了三种建模方法来映射历史森林地上生物量(AGB):基于FIA样地级别的AGB估计(直接),基于LiDAR衍生的AGB地图的训练(间接),以及从直接和间接模型预测的集成平均。模型预测表面(地图)在多个尺度上与FIA估计进行了测试。所有三种方法都产生了可靠的输出,但在模型复杂性,地图精度,饱和度和细尺度模式表示方面存在权衡。结果地图产品可以帮助识别由于人为和自然驱动因素而导致的森林碳库变化的位置,时间和方式。这些产品因此可以作为广泛应用的输入,包括库存变化评估,监测报告和验证框架,并优先考虑保护或纳入改进管理计划的地块。