An evaluation of void‐filling interpolation methods for SRTM data

The Digital Surface Model that has been derived from the February 2000 Shuttle Radar Topography Mission (SRTM) has been one of the most important publicly available new spatial datasets in recent years. However, the ‘finished’ version of the data still contains data voids (some 836,000 km2) – and other anomalies - that prevents immediate use for a wide range of applications. These voids can be filled using a range of interpolation algorithms in conjunction with other sources of elevation data, but there is little guidance on the most appropriate void filling method. This paper describes; (i) a methodology to fill voids using a variety of methods, (ii) a methodology to determine the most appropriate void filling algorithms using a classification of the voids based on their size and a typology of their surrounding terrain; and (iii) the classification of the most appropriate algorithm to each of the 3,339,913 voids in the SRTM data. Based on a sample of 1,304 artificial but realistic voids across six terrain types and eight void size classes, we found that the choice of void filling algorithm is dependent on both the size and terrain type of the void. Contrary to some previous findings, the best methods can be generalised as: Kriging or Inverse Distance Weighting interpolation for small and medium size voids in relatively flat low-lying areas; Spline interpolation for small and medium sized voids in high altitude and dissected terrain; Triangular Irregular Network or Inverse Distance Weighting interpolation for large voids in very flat areas, and an advanced Spline Method (ANUDEM) for large voids in other terrains.