Geostatistics for conflation and accuracy assessment of digital elevation models

A geostatistical methodology is proposed for integrating elevation estimate s derived from digital elevation models (DEMs) and elevation measurements o f higher accuracy, e.g., elevation spot heights. The sparse elevation measu rements (hard data) and the abundant DEM-reported elevations (soft data) ar e employed for modeling the unknown higher accuracy (reference) elevation s urface in a way that properly reflects the relative reliability of the two sources of information. Stochastic conditional simulation is performed for generating alternative, equiprobable images (numerical models) of the unkno wn reference elevation surface using both hard and soft data. These numeric al models reproduce the hard elevation data at their measurement locations, and a set of auto and cross-covariance models quantifying spatial correlat ion between data of the two sources of information at various spatial scale s. From this set of alternative representations of the reference elevation, the probability that the unknown reference value is greater than that repo rted at each node in the DEM is determined. Joint uncertainty associated wi th spatial features observed in the DEM, e.g. the probability for an entire ridge existing, is also modeled from this set of alternative images. A case study illustrating the proposed conflation procedure is presented fo r a portion of a USGS one-degree DEM. It is suggested that maps of local pr obabilities for over or underestimation of the unknown reference elevation values from those reported in the DEM, and joint probability values attache d to different spatial features, be provided to DEM users in addition to tr aditionally reported summary statistics used to quantify DEM accuracy. Such a metadata element would be a valuable tool for subsequent decision-making processes that are based on the DEM elevation surface, or for targeting ar eas where more accurate elevation measurements are required.