Abstract
This study investigates the soil variability within the Northern Boreal Mountains Ecoprovince in British Columbia, with a particular focus on wetland soils and soil organic carbon mapping. Utilizing the BCSOIL2020 dataset and an array of environmental covariates, we employed Principal Component Analysis (PCA), k-means clustering, and conditioned Latin Hypercube Sampling (cLHS) to develop a comprehensive environmental covariate space. This approach allowed for the evaluation of the BCSOIL2020 dataset’s representativeness of the current distribution of wetland soils and the generation of new, strategically placed sampling plots aimed at enhancing future research efforts. Through this methodology, the study identifies critical data gaps in existing datasets and proposes a methodological framework for improving soil mapping practices, thereby contributing to more informed resource management and conservation strategies.
MGEM Student: Stephanie Yang
Key words: Soil Variability, Wetland Soils, Soil Organic Carbon Mapping, Principal Component Analysis, K-Means Clustering, Conditioned Latin Hypercube Sampling
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Yang, Stephanie, 2024, “Mapping the Unseen: Identifying Data Gaps and Proposing New Sampling Points in Northern Boreal Mountain Eco-province, BC Using K-Means Clustering and cLHS“, https://doi.org/10.5683/SP3/ZYIQ2U, Borealis, V1