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| DOI | 10.1002/rse2.384 |
| A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest | |
| 发表日期 | 2024 |
| EISSN | 2056-3485 |
| 英文摘要 | 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. We used a hierarchical multi-sensor approach to map peatlands and their sub-classes throughout the Canadian boreal forest. Additionally, we created a dataset of peatland vegetation height using ICESat-2 ATL08 data. image |
| 英文关键词 | Canadian boreal forest; ICESat-2; image classification; mapping framework; peatlands; vegetation characterization |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing |
| WOS类目 | Ecology ; Remote Sensing |
| WOS记录号 | WOS:001172748300001 |
| 来源期刊 | REMOTE SENSING IN ECOLOGY AND CONSERVATION
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/292227 |
| 作者单位 | Carleton University; Natural Resources Canada; Canadian Forest Service; Great Lakes Forestry Centre; Natural Resources Canada; Canadian Forest Service; Carleton University |
| 推荐引用方式 GB/T 7714 | . A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest[J],2024. |
| APA | (2024).A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest.REMOTE SENSING IN ECOLOGY AND CONSERVATION. |
| MLA | "A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest".REMOTE SENSING IN ECOLOGY AND CONSERVATION (2024). |
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