Conflation of sparsely sampled and gridded elevation data: scale mismatch and semantic differences

Abstract

Data conflation refers to the methods and processing of information fusion whereby multi-source data are integrated to derive required information that is thought to be of increased accuracy, finer resolution, better homogenised semantics, fuller coverage, enhanced representational and computational efficiency, or improved utility for certain purposes than any single data source alone. As spatial data can be conceived of as realisations of random fields, multivariate geostatistics provides a coherent framework for data conflation. As important metadata of spatial data, scale, which is considered synonymous with data support in this paper, and semantics, which concern the meanings given to ‘elevation’ in elevation data, for example, are complicating factors for data conflation, and need to be handled properly. This paper’s novelty lies in its handling of semantic differences via corrections to biases in local means, and its trend-residual decomposition-based structural modelling of terrain elevation data, constituting a data-driven, geostatistical strategy, which significantly increases accuracy or consistency in conflated data. Using SRTM (Shuttle Radar Topography Mission) and GTOPO30 data at a site in northwest China, experiments revealed that effects of the semantic difference on data conflation can be reduced through properly estimating and incorporating biases of local means in primary versus secondary data. It was also found that structural modelling adaptive of scale and non-stationarity is effective for achieving accuracy in data conflation: adaptive scale modelling is done through proper trend-residual decomposition of elevation data and their residuals while non-stationarity in spatial variability is handled through localised mean specification and covariance modelling.