Investigating the Use of Sentinel-1 for Improved Mapping of Small Peatland Water Bodies: Towards Wildfire Susceptibility Monitoring in Canada’s Boreal Forest

Schultz, S.; Millard, Koreen; Darling, S.; Chénier, René

doi:10.3390/hydrology10050102

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…To ensure only permanent water bodies were used, only pixels with an occurrence greater than or equal to 99% were used to create 1000 training samples. This dataset is not able to capture peatland water bodies (Schultz et al, 2023). Therefore the training data did not include points for small peatland water bodies.…”

Section: Study Area and Reference Datamentioning

confidence: 99%

“…The GSW dataset (Pekel et al, 2016) was used to collect training data for the water class, which does not represent seasonal water bodies due to issues of resolution and mixed pixels (Schultz et al, 2023) . Similarly to the issues within the GSW, the moderate spatial resolution data, and seasonal mosaics used in this research reduces the ability for small or ephemeral water bodies within peatlands to be classified.…”

Section: Use Of Ancillary Datamentioning

confidence: 99%

“…A limitation of this research was the inability of the applied method to capture small and ephemeral peatland water bodies due to a lack of temporally-variable water extent training data. In the future, applying the methods recommended in Schultz et al, (2023) to detect peatland water bodies using Sentinel-1 data could improve this aspect of the map. 28 for variable names.…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

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The Classification and Characterization of Canadian Boreal Peatland Sub-classes

Pontone

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This research addresses the lack of suitable peatland maps and vegetation data in the Canadian Boreal Forest. This study aimed to create a peatland sub-class map and inventory peatland vegetation height. A three-stage hierarchical classification framework was developed for mapping peatland sub-classes circa 2020. A combination of multi-spectral data, L-band SAR backscatter and C-Band InSAR coherence, forest structure, and ancillary variables were used as model predictors. In the first stage, wetlands, uplands, and water were classified with 86.5% accuracy. The second stage achieved 93.3% accuracy in distinguishing peatland from mineral wetlands. The third stage, focusing on peatland areas, classified bogs, rich fens, poor fens, and permafrost peat complexes with 71.5% accuracy. ICESat-2 ATL08 data described regional and class-wise vegetation height variations. 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. i I would like to acknowledge the many people who made the writing of this thesis possible. First, I would like to thank my supervisor Dr. Koreen Millard for her invaluable advice, constant guidance, and continuous support. I would also like to acknowledge my committee members, Dr. Dan K. Thompson and Luc Guindon for their continuous guidance and contributions of expertise and data. I would like to acknowledge Dr. Ellen Whitman of the Canadian Forest Service for teaching me about wildfire and boreal vegetation during our field work in the Yukon.I would also like to more generally thank the Department of Geography and Environmental studies at Carleton, which has been my home away from home for the last six years. I am also grateful to my cohort members, classmates, and friends.Though there are too many to name individually, each one made an important contribution. I would be remiss to not mention my parents, who have provided an astronomical amount of support throughout my academic journey.

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Section: Study Area and Reference Datamentioning

confidence: 99%

Section: Use Of Ancillary Datamentioning

confidence: 99%

Section: Suggestions For Future Researchmentioning

confidence: 99%

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The Classification and Characterization of Canadian Boreal Peatland Sub-classes

Pontone

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A hierarchical, multi‐sensor framework for peatland sub‐class and vegetation mapping throughout the Canadian boreal forest

Pontone,

Millard,

Thompson

et al. 2024

Remote Sens Ecol Conserv

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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.

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