Digital Elevation Data Fusion Using Multiple-Point Geostatistical Simulation

Abstract

This paper proposes advanced methods based on multiple-point geostatistical simulations (MPSs) for data fusion in digital elevation models (DEMs). MPS-based methods can capture similar patterns using the spatial correlation residing in the training images and then reproduce the area of interest in terms of spatial patterning through conditional simulation. For this, the FILTERSIM algorithm, which is based on MPS, can be applied to augment the prediction results from traditional geostatistical approaches to derive fused datasets from complementary sources. However, the FILTERSIM algorithm discerns similar patterns based on a filter-based prototyping process and does not account for nonstationarity of spatial structures. Thus, an enhanced data fusion method based on a modified FILTERSIM algorithm was proposed, in which new strategies for forming prototypes were incorporated. An experiment was carried out in a study area in Northwest China to test the proposed methods. The aim is to produce fused DEM datasets at fine spatial resolution by integrating fine-resolution but sparsely sampled SRTM data and regularly gridded coarse-resolution GMTED2010 data, and then to test three data fusion methods: 1) the traditional geostatistical interpolation, which provides baseline results for comparison; 2) the conventional FILTERSIM algorithm; and 3) a modified FILTERSIM algorithm. For both the first and second methods, three different kriging approaches were used; for the third method, three new schemes for building prototypes were tested for an improvement in performance. The results show that the improved FILTERSIM method can achieve greater accuracy and retain more spatial structure than the other two methods.