This study proposes the method of simulating spatial patterns and quantifying the uncertainty in multivariate distribution of heavy metals (Cr, Cu, Ni, and Zn) by sequential indicator simulation (SIS) combined with conditional Latin hypercube sampling (cLHS) in Changhua County, Taiwan. The cLHS is used for a sampling then for SIS mapping and assessing uncertainties of heavy metal concentrations. The indicator variogram results indicate that the 700 cLHS samples replicate statistical multivariate distribution and spatial structure of the 1,082 samples. Moreover, the SIS realizations based on 700 cLHS samples are more conservative and reliable than those based on 1,082 samples for delineating soil contamination by all heavy metals with the exception of Zn. Given adequate sampling, soil contamination simulation provides sufficient information for delineating contaminated areas and planning environmental management.
This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.
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