Sampling design and uncertainty based on spatial variability of spectral variables for mapping vegetation cover

Optimal sampling design for collecting ground data is critical in order to accurately map vegetation cover using remotely sensed data. Traditional simple random sampling often leads to a duplication of information and to a larger sample than is required. An optimal sampling grid spacing based on regionalized variable theory can greatly reduce the number of sample plots needed given a precision level for a study area. However, this method requires a set of ground data that exists or can be obtained via a pilot survey in order to derive a semivariogram for measuring the spatial variability of the variable of interest. In this study, we first developed a method to estimate the semivariogram of a ground or primary variable-vegetation cover from remotely sensed data instead of ground data-and then used it for determining optimal grid spacing for sampling the primary variable. The method developed can avoid the need for a pilot survey to obtain a ground dataset that has a good spatial distribution of plots and can be used to calculate the unbiased semivariogram of the ground variable when unbiased historical data are not available. This can reduce the total cost of collection of ground data. The accuracy of mapping vegetation cover based on this approach was compared to that generated with simple random sampling. A simple sensitivity analysis was conducted. The results show that this new method is very promising for determining optimal sampling grid spacing for estimating regional averages. When it is applied to determining sampling grid spacing for local estimation, a high correlation between vegetation cover and spectral variables is required.