Predictive mapping of wetland soil types in the Canadian Prairie Pothole Region using high-resolution digital elevation model terrain derivatives
نویسندگان
چکیده
Wetland soil types, which can be distinguished based on calcium carbonate content, vary in their effect ecosystem functions like phosphorus retention, salinity contributions, and greenhouse gas forcing. These types may predictively mapped with machine learning models that use terrain derivatives calculated from high-resolution digital elevation models. Soil profiles three Saskatchewan study sites were classified into functional categories—upland, calcareous wetland, or noncalcareous wetland—and used to train random forest for predictive mapping. Multiple included as predictor variables capture local- landscape-scale morphometry hydrology influences, including five developed this study. Models at spatial resolutions: 2, 5, 10 m, tested via internal cross-validation independent validation datasets previous studies. Predictive accuracies highest when mapping 2 m resolution (independent accuracy range = 64%–100%) but also successful 5 resolutions 63%–100%); however, visual inspection determined the maps generated less detailed occasionally featured questionable discontinuous distributions. Three of among most important (first, second, 10th important). trained using only data a specific site had slightly better performance than all sites, except regions where training lacking.
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ژورنال
عنوان ژورنال: Canadian Journal of Soil Science
سال: 2023
ISSN: ['0008-4271', '1918-1841']
DOI: https://doi.org/10.1139/cjss-2022-0034