Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal

Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable production. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum...

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Published in:Canadian Journal of Soil Science
Main Authors: Raphaël Deragon, Daniel D. Saurette, Brandon Heung, Jean Caron
Format: Text
Language:English
Published: Canadian Science Publishing 2023
Subjects:
DML
Online Access:https://doi.org/10.1139/cjss-2022-0031
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spelling ftbioone:10.1139/cjss-2022-0031 2024-06-02T08:05:47+00:00 Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal Raphaël Deragon Daniel D. Saurette Brandon Heung Jean Caron Raphaël Deragon Daniel D. Saurette Brandon Heung Jean Caron world 2023-01-05 text/HTML https://doi.org/10.1139/cjss-2022-0031 en eng Canadian Science Publishing doi:10.1139/cjss-2022-0031 All rights reserved. https://doi.org/10.1139/cjss-2022-0031 coprogenous soil peat thickness predictive digital soil mapping Text 2023 ftbioone https://doi.org/10.1139/cjss-2022-0031 2024-05-07T01:01:58Z Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable production. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these materials are critical for soil management. Therefore, five drained and cultivated peatlands were studied to estimate their maximum peat thickness (MPT)—a potential key soil property that can help identify management zones for their conservation. MPT can be defined as the depth to the mineral layer (DML) minus the coprogenous layer thickness (CLT). The objective of this study was to estimate DML, CLT, and MPT at a regional scale using environmental covariates derived from remote sensing. Three machine-learning models (Cubist, Random Forest, and k-Nearest Neighbor) were compared to produce maps of DML and CLT, which were combined to generate MPT at a spatial resolution of 10 m. The Cubist model performed the best for predicting both features of interest, yielding Lin’s concordance correlation coefficients of 0.43 and 0.07 for DML and CLT, respectively, using a spatial cross-validation procedure. Interpretation of the drivers of CLT was limited by the poor predictive power of the final model. More precise data on MPT are needed to support soil conservation practices, and more CLT field observations are required to obtain a higher prediction accuracy. Nonetheless, digital soil mapping using open-access geospatial data shows promise for understanding and managing cultivated peatlands. Text DML BioOne Online Journals Canadian Journal of Soil Science 103 1 103 120
institution Open Polar
collection BioOne Online Journals
op_collection_id ftbioone
language English
topic coprogenous soil
peat thickness
predictive digital soil mapping
spellingShingle coprogenous soil
peat thickness
predictive digital soil mapping
Raphaël Deragon
Daniel D. Saurette
Brandon Heung
Jean Caron
Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
topic_facet coprogenous soil
peat thickness
predictive digital soil mapping
description Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable production. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these materials are critical for soil management. Therefore, five drained and cultivated peatlands were studied to estimate their maximum peat thickness (MPT)—a potential key soil property that can help identify management zones for their conservation. MPT can be defined as the depth to the mineral layer (DML) minus the coprogenous layer thickness (CLT). The objective of this study was to estimate DML, CLT, and MPT at a regional scale using environmental covariates derived from remote sensing. Three machine-learning models (Cubist, Random Forest, and k-Nearest Neighbor) were compared to produce maps of DML and CLT, which were combined to generate MPT at a spatial resolution of 10 m. The Cubist model performed the best for predicting both features of interest, yielding Lin’s concordance correlation coefficients of 0.43 and 0.07 for DML and CLT, respectively, using a spatial cross-validation procedure. Interpretation of the drivers of CLT was limited by the poor predictive power of the final model. More precise data on MPT are needed to support soil conservation practices, and more CLT field observations are required to obtain a higher prediction accuracy. Nonetheless, digital soil mapping using open-access geospatial data shows promise for understanding and managing cultivated peatlands.
author2 Raphaël Deragon
Daniel D. Saurette
Brandon Heung
Jean Caron
format Text
author Raphaël Deragon
Daniel D. Saurette
Brandon Heung
Jean Caron
author_facet Raphaël Deragon
Daniel D. Saurette
Brandon Heung
Jean Caron
author_sort Raphaël Deragon
title Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
title_short Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
title_full Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
title_fullStr Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
title_full_unstemmed Mapping the maximum peat thickness of cultivated organic soils in the southwest plain of Montreal
title_sort mapping the maximum peat thickness of cultivated organic soils in the southwest plain of montreal
publisher Canadian Science Publishing
publishDate 2023
url https://doi.org/10.1139/cjss-2022-0031
op_coverage world
genre DML
genre_facet DML
op_source https://doi.org/10.1139/cjss-2022-0031
op_relation doi:10.1139/cjss-2022-0031
op_rights All rights reserved.
op_doi https://doi.org/10.1139/cjss-2022-0031
container_title Canadian Journal of Soil Science
container_volume 103
container_issue 1
container_start_page 103
op_container_end_page 120
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