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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2023
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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 |
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English |
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coprogenous soil peat thickness predictive digital soil mapping |
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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 |
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world |
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DML |
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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 |
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103 |
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1 |
container_start_page |
103 |
op_container_end_page |
120 |
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1800750673595203584 |