Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Effect of differing DEM creation methods on the results from a hydrological model
Computers & Geosciences
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Computers & Geosciences
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Computers & Geosciences
Cross-validation as a means of investigating DEM interpolation error
Computers & Geosciences
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This paper compares the applicability of three ground survey methods for modelling terrain: one man electronic tachymetry (TPS), real time kinematic GPS (GPS), and terrestrial laser scanning (TLS). Vertical accuracy of digital terrain models (DTMs) derived from GPS, TLS and airborne laser scanning (ALS) data is assessed. Point elevations acquired by the four methods represent two sections of a mountainous area in Cumbria, England. They were chosen so that the presence of non-terrain features is constrained to the smallest amount. The vertical accuracy of the DTMs was addressed by subtracting each DTM from TPS point elevations. The error was assessed using exploratory measures including statistics, histograms, and normal probability plots. The results showed that the internal measurement accuracy of TPS, GPS, and TLS was below a centimetre. TPS and GPS can be considered equally applicable alternatives for sampling the terrain in areas accessible on foot. The highest DTM vertical accuracy was achieved with GPS data, both on sloped terrain (RMSE 0.16m) and flat terrain (RMSE 0.02m). TLS surveying was the most efficient overall but veracity of terrain representation was subject to dense vegetation cover. Therefore, the DTM accuracy was the lowest for the sloped area with dense bracken (RMSE 0.52m) although it was the second highest on the flat unobscured terrain (RMSE 0.07m). ALS data represented the sloped terrain more realistically (RMSE 0.23m) than the TLS. However, due to a systematic bias identified on the flat terrain the DTM accuracy was the lowest (RMSE 0.29m) which was above the level stated by the data provider. Error distribution models were more closely approximated by normal distribution defined using median and normalized median absolute deviation which supports the use of the robust measures in DEM error modelling and its propagation.