SRTM resample with short distance-low nugget kriging

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1080/13658810701730152. This is version 1 of this Preprint.

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Authors

Carlos Henrique Grohmann 

Abstract

The Shuttle Radar Topography Mission (SRTM), was flow on Space Shuttle Endeavour in February 2000, with the objective of acquire a digital elevation model of all land between 60º north latitude and 56º south latitude, using Interferometric Synthetic Aperture Radar (InSAR) techniques. SRTM data is distributed at horizontal resolution of 1 arc-second (aprox. 30m) for areas within the USA and at 3 arc-second (aprox. 90m) resolution for the rest of the world. A resolution of 90m can be considered suitable for small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it wont increase the level of detail of the original DEM, but it will lead to a surface where there is coherence of angular properties (i.e., slope, aspect) between neighbouring pixels, an important characteristic when dealing with terrain analysis.
This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3 to 1. We present for a test area in western USA, which include different adjustment schemes (changes in nugget effect value and in the interpolation radius) as well as comparisons with the original 1 model of the area, with the National Elevation Dataset DEMs, and with other interpolation methods (splines and IDW).
The basic concepts for using kriging to resample terrain data are: 1) to work only with the immediate neighbourhood of the predicted point, due the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; 2) add a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and 3) use a small value of nugget effect, to avoid smoothing that can obliterate terrain features.
Drainages derived from the surfaces interpolated by kriging and by splines have good agreement with streams derived from the 1 NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1 surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging.

DOI

https://doi.org/10.31223/osf.io/bg8a7

Subjects

Earth Sciences, Geographic Information Sciences, Geography, Geomorphology, Other Earth Sciences, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Tectonics and Structure

Keywords

geostatistics, Kriging, SRTM, Interpolation, Nugget value, Variogram

Dates

Published: 2017-10-31 10:58

License

CC BY Attribution 4.0 International

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