Mapping the Unseen: Identifying Data Gaps and Proposing New Sampling Points in Northern Boreal Mountain Eco-province, BC Using K-Means Clustering and cLHS

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.本研究针对不列颠哥伦比亚省北部寒带山地生态省（Northern Boreal Mountains Ecoprovince）内的土壤空间异质性展开调查，重点聚焦湿地土壤与土壤有机碳制图。研究依托BCSOIL2020数据集与一系列环境协变量，采用主成分分析（Principal Component Analysis，PCA）、k-means聚类以及条件拉丁超立方采样（conditioned Latin Hypercube Sampling，cLHS）构建了完备的环境协变量空间。该方法可用于评估BCSOIL2020数据集对当前湿地土壤分布的代表性，并生成布局优化的新增采样样地，以助力未来相关研究推进。通过上述研究方法，本研究明确了现有数据集存在的关键数据缺口，并提出了一套可用于改进土壤制图实践的方法论框架，从而为更科学的资源管理与保护策略制定提供有力支撑。