Journal
SPATIAL STATISTICS
Volume 13, Issue -, Pages 106-122Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.spasta.2015.06.002
Keywords
Sampling design; Digital soil mapping; Regression
Categories
Funding
- German Research Foundation (DFG), Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador [PAK 825, LI 2360/1-1]
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With respect to sampling for regression-based digital soil mapping (DSM), the above all aim is to ensure that the spatial variability of the soil is well-captured without introducing any bias, while the design remains feasible according to operational constraints such as accessibility, man power and cost. Representativeness of the sample concerning the population to be sampled needs to be guaranteed in any regression-based modelling approach. Four selected sampling designs were adapted to show that basically any design may be optimised to represent the n-dimensional predictor space of a particular area, while selecting points is only permitted from a small accessible sub-area or from outside the area. Sampling efficiency may be evaluated based on the representation of the predictor space. However, not only each predictor's probability function but also the interaction between predictors may have to be considered, to select a representative sample. Instead of sampling a previously un-sampled area with limited accessibility, the four sampling designs may also be used to subsample an existing dataset and, thereby, optimise a suboptimal dataset based on the predictor space of the area which shall be mapped by DSM. (C) 2015 Elsevier B.V. All rights reserved.
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