A Methodology for Spatial Uncertainty Analysis Of Remote Sensing and GIS Products

When remote sensing and GIS products are generated, errors and uncertainties from collection, processing and analysis of image and ground data, and model development, accumulate and are propagated to the maps. The products thus possess many sources of uncertainties that vary spatially and temporally. Spatially identifying the sources of uncertainties, modeling their accumulation and propagation, and finally, quantifying them will be critical to control the quality of spatial data. This paper demonstrates a methodology and its applications for a case study in which uncertainty of predicted soil erosion is hierarchically partitioned into various primary components on a pixel-by-pixel basis. The methodology is based on a regionalized variable theory of variables. It integrates remote sensing aided co-simulation algorithms in geostatistics, and uncertainty and error budget methods in uncertainty analysis. The simulation algorithms generate realizations that can be used to calculate local estimates, and the variances and co-variances between them. Uncertainty and error budget methods partition the uncertainty of output into various input components and quantify their relative uncertainty contributions. The results can thus suggest the main uncertainty sources and their variation spatially, and further provide a rationale to reduce errors in map generation and application.