Nketia Kwabena Abrefa, Asabere Stephen Boahen, Erasmi Stefan, Sauer Daniela
Physical Geography Dept., Georg-August-Universität Göttingen, Germany.
Council for Scientific and Industrial Research - Soil Research Institute, Kumasi, Ghana.
MethodsX. 2019 Feb 8;6:284-299. doi: 10.1016/j.mex.2019.02.005. eCollection 2019.
Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This methodology produce an optimized sampling stratification by analysing the local variability of the soil property, and the influence of environmental factors. The methodology captures the maximum local variances in the global auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less representative of the soil property of interest. Consequently, the method stratifies the geographical space of interest in order to adequately represent the soil property. We present results on the tested method () from the Guinea savannah zone of Ghana. •It defines the local structure and accounts for localized spatial autocorrelation in explaining variability.•It suppresses the occurrence of model-selected sampling locations in areas that are less representative of the soil property of interest.
分析景观中土壤属性的空间模式需要一种能充分覆盖土壤地形序列的采样策略。在此背景下，我们开发了一种将全局加权主成分分析（GWPCA）和成本约束条件拉丁超立方算法（cLHC）相结合的混合方法。该方法通过分析土壤属性的局部变异性和环境因素的影响，生成优化的采样分层。该方法利用GWPCA在全局辅助数据集中捕获最大局部方差，并使用cLHC优化代表性采样位置的选择以进行采样。该方法还抑制了来自对感兴趣的土壤属性代表性较差区域的辅助数据集的二次采样。因此，该方法对感兴趣的地理空间进行分层，以便充分表征土壤属性。我们展示了来自加纳几内亚草原地区的测试方法的结果。•它定义了局部结构，并在解释变异性时考虑了局部空间自相关。•它抑制了在对感兴趣的土壤属性代表性较差的区域中模型选择的采样位置的出现。
