A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data

Abstract

For obtaining maps of good precision by the spatial inference method, the distribution of sampling sites in geographical and feature space is very important. For a regional variable with trends, the predicting error comes from trend estimation, variogram estimation and spatial interpolation. Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and spatial interpolation. MODIS data and simulated data were used as sampling fields to draw sample sets using scLHS, cLHS, cLHS with x and y coordinates as covariates, simple random and spatial even sampling methods, and the distribution and prediction errors of sample sets from different methods were evaluated. The results showed that scLHS performed well in balancing spreading in geographic and feature space, and can generate points pairs with small distances, and the sample sets drawn by scLHS produced smaller mapping error, especially when there were trends in the target variable.

Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

“…In the MB sampling, the relationship between the sample and population properties are quantified by the model, and the model prescribes ideal sample locations (Stevens, 2006). For example, the conditioned Latin hypercube Sampling (cLHS) is a frequently used MB sampling design in digital soil mapping (Gao et al, 2016). The choice between the two sampling strategies depends on the goals of the study, the availability of legacy and auxiliary data, availability of local resources, and local accessibility (Biswas and Zhang, 2018).…”

A soil sampling design for arable land quality observation by using SPCOSA–CLHS hybrid approach

“…It will be challenging to take full advantage of these indicators data to improve the representativeness of samples if the spatial instability of the sampling unit is ignored (B. Gao et al, 2016;J. Wang et al, 2010;Yang et al, 2013).…”

“…B. Gao et al (2016) proposed the spatial conditioned Latin hypercube sampling (SCLHS) to integrate optimization goals of both geographical and attribute space to sampling process by dividing the study area into spatially regular grids and extracting samples from each grid as equal as possible. As the no-data region increases, this method becomes inapplicable because its spatial grid delineation process ignores the effects of these no-data regions.…”

An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II

“…When covariate data of certain regions is abundant, with the aims of population trend estimation and interpolation, the sample points are expected to be optimized for evenly distribution in both the geographical and feature spaces [25]. Moreover, when prior knowledge regarding variogram parameters is absent, and information on covariates is accessible, the sampling scenario becomes complex and must be optimized in geographical space, feature space and point pair distribution to achieve population estimation, variogram estimation and interpolation [26]. The even more complex case is that multivariate need to be sampled for precise interpolation and population estimation [27,28].In the domain of spatial sampling, the popular means for multi-objective sampling optimization problems is transferring multiple objectives into one single objective by weighting.…”

“…Webster provided a description of the detailed optimization process with this algorithm, aiming to ensure the fitness of sample design both for the variogram estimation and kriging interpolation [32]. Gao proposed a spatial conditioned Latin hypercube sampling method by using SSA to optimize sample points evenly spreading in both feature and geographical spaces [26]. Further, Garbor applied the SSA algorithm in second-phase sampling for the optimization of multivariate soil mapping purposes [4].…”