Peat depth and carbon storage of the Hudson Bay Lowlands, Canada

Version 1.0

Li, Yiyao; Han, Daorui; Rogers, Cheryl; Finkelstein, Sarah; Hararuk, Oleksandra; Waddington, James; Barreto, Carlos; Mclaughlin, James; Snider, James; Gonsamo, Alemu, 2024, "Peat depth and carbon storage of the Hudson Bay Lowlands, Canada", https://doi.org/10.5683/SP3/TIRAXJ, Borealis, V1, UNF:6:ZUcVzs4e/8ETtYSE5SgU4g== [fileUNF]

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Description	This dataset contains ground-measured peat depth and maps with the spatial distribution of peat depth, carbon stock and uncertainty in the Hudson Bay Lowlands, Ontario, Canada. The ground-measured peat depth data was collected by a Russian-type peat corer from 32 sites in 3 groups spanning from 51oN to 55oN in July and September 2022. The version 1 maps were produced in the Remote Sensing Lab, McMaster University, on March 2024. To generate the peat depth map, we used 495 peat depth records from Ontario Ministry of Natural Resources and Forestry data archive, topographic information, long-term satellite observations of land surface temperature, greenness, and polarization signatures in Synthetic-Aperture Radar (SAR) as well as machine learning models. Data was trained using multi machine learning algorithms. Based on the Root Mean Squared Error (RMSE) derived from 10-fold cross-validation, four models were selected for further prediction, including Gradient Boosting Machine (GBM), Deep Learning, Distributed Random Forest (DRF) and Extreme Gradient Boosting (XGBoost). For the final peat depth map, a second-level “meta-learner’ called stacked regression was applied to find an optimal combination of the 4 base models. Here we used generalized linear model (GLM) during the stacking process to map peat depth for the entire HBL. The uncertainty was estimated as ± one standard deviation around the mean estimates of all base models. The carbon stock map was estimated based on empirical relationship between the estimated peat depth and C stock. (2024-06-24)
Subject	Earth and Environmental Sciences
Keyword	peat depth, soil carbon stock, Hudson Bay Lowlands
Related Publication	Li, Y., Han, D., Rogers, C.A., Finkelstein, S.A., Hararuk, O., Waddington, J.M., Barreto, C., McLaughlin, J.W., Snider, J., & Gonsamo, A. (2024). Peat depth and carbon storage of the Hudson Bay Lowlands, Canada. ESS Open Archive. June 13, 2024. DOI: 10.22541/essoar.171828456.66424229/v1
License/Data Use Agreement	CC-BY 4.0

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	1 to 6 of 6 Files	Original Format Archival Format (.tab)
	McMaster_Carbon_30m_kgm2_version1.0.zip ZIP Archive - 1.2 GB Published Jul 18, 2024 2 Downloads MD5: 74c203361255698b4e16901d00daa1e3 C storage of the Hudson Bay Lowlands, 30m spatial resolution, Lambert Conformal Conic projection, kg/m2 unit.	Preview "McMaster_Carbon_30m_kgm2_version1.0.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	McMaster_PeatDepth_30m_cm_version1.0.zip ZIP Archive - 1.2 GB Published Jul 18, 2024 2 Downloads MD5: d5290ec7f572bf8b8bf9a6975d00cde9 Peat depth of the Hudson Bay Lowlands, 30m spatial resolution, Lambert Conformal Conic projection, cm unit.	Preview "McMaster_PeatDepth_30m_cm_version1.0.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	McMaster_PeatDepth_HBL_2022.tab Tabular Data - 1.9 KB Published Jul 18, 2024 2 Downloads 6 Variables, 33 Observations UNF:6:ZUcVzs4e/8ETtYSE5SgU4g== Ground-measured peat depth data in 2022	Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata Variable Metadata Data File Citation EndNote XML RIS BibTeX Explore Options Data Explorer
	McMaster_Uncertainty_Carbon_30m_kgm2_version1.0.zip ZIP Archive - 1.3 GB Published Jul 18, 2024 1 Download MD5: b5a34839613ec08c9158944de7b909fa C storage uncertainty, 30m spatial resolution, Lambert Conformal Conic projection, kg/m2 unit.	Preview "McMaster_Uncertainty_Carbon_30m_kgm2_version1.0.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	McMaster_Uncertainty_PeatDepth_30m_cm_version1.0.zip ZIP Archive - 1.3 GB Published Jul 18, 2024 1 Download MD5: 75aad17dd0e0c437aaa0f699f56618f7 Peat depth uncertainty, 30m spatial resolution, Lambert Conformal Conic projection, cm unit.	Preview "McMaster_Uncertainty_PeatDepth_30m_cm_version1.0.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	README_McMaster_HBL_peat_depth_30m.txt Plain Text - 9.1 KB Published Jul 18, 2024 9 Downloads MD5: e1d99414e6fc2e951c0188aa9aec4c23 README file of the dataset	Preview "README_McMaster_HBL_peat_depth_30m.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.5683/SP3/TIRAXJ
Publication Date	2024-07-18
Title	Peat depth and carbon storage of the Hudson Bay Lowlands, Canada
Author	Li, Yiyao (McMaster University) - ORCID: 0009-0008-2271-7631 Han, Daorui (McMaster University) - ORCID: 0000-0002-1871-7056 Rogers, Cheryl (Toronto Metropolitan University) - ORCID: 0000-0003-2792-1128 Finkelstein, Sarah (University of Toronto) - ORCID: 0000-0002-8239-399X Hararuk, Oleksandra (Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada) - ORCID: 0000-0001-5694-6813 Waddington, James (McMaster University) - ORCID: 0000-0002-0317-7894 Barreto, Carlos (Ontario Ministry of Natural Resources and Forestry) - ORCID: 0000-0003-2859-021X Mclaughlin, James (Ontario Ministry of Natural Resources and Forestry) Snider, James (World Wildlife Fund Canada) - ORCID: 0000-0003-4500-8121 Gonsamo, Alemu (McMaster University) - ORCID: 0000-0002-2461-618X
Point of Contact	Use email button above to contact. Gonsamo, Alemu (McMaster University)
Description	This dataset contains ground-measured peat depth and maps with the spatial distribution of peat depth, carbon stock and uncertainty in the Hudson Bay Lowlands, Ontario, Canada. The ground-measured peat depth data was collected by a Russian-type peat corer from 32 sites in 3 groups spanning from 51oN to 55oN in July and September 2022. The version 1 maps were produced in the Remote Sensing Lab, McMaster University, on March 2024. To generate the peat depth map, we used 495 peat depth records from Ontario Ministry of Natural Resources and Forestry data archive, topographic information, long-term satellite observations of land surface temperature, greenness, and polarization signatures in Synthetic-Aperture Radar (SAR) as well as machine learning models. Data was trained using multi machine learning algorithms. Based on the Root Mean Squared Error (RMSE) derived from 10-fold cross-validation, four models were selected for further prediction, including Gradient Boosting Machine (GBM), Deep Learning, Distributed Random Forest (DRF) and Extreme Gradient Boosting (XGBoost). For the final peat depth map, a second-level “meta-learner’ called stacked regression was applied to find an optimal combination of the 4 base models. Here we used generalized linear model (GLM) during the stacking process to map peat depth for the entire HBL. The uncertainty was estimated as ± one standard deviation around the mean estimates of all base models. The carbon stock map was estimated based on empirical relationship between the estimated peat depth and C stock. (2024-06-24)
Subject	Earth and Environmental Sciences
Keyword	peat depth, soil carbon stock, Hudson Bay Lowlands
Related Publication	Li, Y., Han, D., Rogers, C.A., Finkelstein, S.A., Hararuk, O., Waddington, J.M., Barreto, C., McLaughlin, J.W., Snider, J., & Gonsamo, A. (2024). Peat depth and carbon storage of the Hudson Bay Lowlands, Canada. ESS Open Archive. June 13, 2024. DOI: 10.22541/essoar.171828456.66424229/v1
Depositor	Gonsamo, Alemu
Deposit Date	2024-06-24
Date of Collection	Start Date: 2022-07 ; End Date: 2022-09

Geospatial Metadata

Geographic Coverage	Canada
Geographic Bounding Box	-96.6376446511815 -74.9286602761815 59.35414697875683 49.437708264328236

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