Using remote sensing images to design optimal field sampling schemes

Abstract

At this presentation, the author discussed a statistical method for deriving optimal spatial sampling schemes. First I focus on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective. Secondly, the author described a quantitative methodology for deriving optima exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought were considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then input to an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, results in optimal exploration target zones, many of which coincide spatially with at least one of the occurrences of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study show usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map