Peatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

中文翻译：

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加拿大北方森林泥炭地亚类和植被测绘的分层多传感器框架

加拿大北方森林的泥炭地正受到人为气候变化的负面影响，预计其影响将进一步恶化。泥炭地类型和亚类的生态水文特征各不相同，预计对气候变化有不同的响应。加拿大泥炭地模型、加拿大火灾行为预测系统和加拿大土地数据同化系统等大型建模框架需要泥炭地地图，其中包括有关子类型和植被的信息作为关键输入。此外，泥炭地类别和植被高度是野生动物栖息地管理的关键变量，并且与碳循环和野火燃料负荷有关。本研究旨在创建加拿大北方森林泥炭地亚类（沼泽、贫瘠沼泽、富沼泽永久冻土泥炭复合体）地图，并使用 ICESat-2 创建泥炭地植被高度特征清单。开发了一个三阶段分层分类框架，用于绘制 2020 年左右加拿大北方森林内的泥炭地子类。训练和验证数据包括来自各种来源（现场数据、航空照片解释、文献中记录的测量结果）的泥炭地位置。多光谱数据、L 波段 SAR 后向散射和 C 波段干涉 SAR 相干性、森林结构和辅助变量的组合被用作模型预测因子。辅助数据被用来掩盖农业地区和城市地区，并解释可能出现永久冻土的地区。在第一阶段的分类中，湿地、高地和水域的分类准确度为86.5%。在第二阶段，仅在湿地区域内，区分泥炭地和矿物湿地的准确度为93.3%。在第三阶段，仅限于泥炭地地区，对沼泽、富饶沼泽、贫瘠沼泽和永久冻土泥炭复合体进行分类，准确度为 71.5%。然后，使用 ICESat-2 ATL08 星载激光雷达数据来描述泥炭地植被高度特征的区域变化以及基于北方森林广泛样本的区域和类别变化。这项研究为加拿大北方森林引入了一个全面的大规模泥炭地子类制图框架，提出了此类中第一个中等分辨率的地图。