Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis

Abstract

We first provide a critical review of statistical procedures employed in the literature for testing uncertainty in digital terrain analysis, then focus on several aspects of spatial autocorrelation that have been neglected in the analysis of gridded elevation data. When applied to first derivatives of elevation such as topographic slope, a spatial approach using Moran’s I and the LISA (Local Indicator of Spatial Association) allows: (1) georeferenced data patterns to be generated; (2) error hot- and coldspots to be located; and (3) error propagation during DEM manipulation to be evaluated. In a worked example focusing on the Wasatch mountain front, Utah, we analyse the relative advantages of six DEMs resulting from different acquisition modes (airborne, optical, radar, or composite): the LiDAR (2 m), CODEM (5 m), NED10 (10 m), ASTER DEM (15 m) and GDEM (30 m), and SRTM (90 m). The example shows that (apart from the LiDAR) the NED10, which is generated from composite data sources, is the least error-ridden DEM for that region. Knowing error magnitudes and where errors are located determines where corrections to elevation are required in order to minimize error accumulation or propagation, and clarifies how they might affect expert judgement in environmental decisions. Ground resolution issues can subsequently be addressed with greater confidence by resampling the preferred grid to terrain resolutions suited to the landscape attributes of interest. Source product testing is an essential yet often neglected part of DEM analysis, with many practical applications in hydrological modelling, for predictions of slope- to catchment-scale mass sediment flux, or for the assessment of slope stability thresholds.

External Validation of the ASTER GDEM2, GMTED2010 and CGIAR-CSI- SRTM v4.1 Free Access Digital Elevation Models (DEMs) in Tunisia and Algeria

“…Gonga-Saholiariliva et al [15] gave an overview and mentioned various papers related to this topic. One approach of investigation uses the terms of internal and external validation depending on whether or not independent reference data are included in the assessment procedure [16].…”

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

“…where is the number of samples in the recorded echo, and and are the magnitudes calculated by Equation (1) and measured at each sample point , respectively. For each waveform, only the samples whose amplitudes were greater than a threshold were used in the Gaussian decomposition.…”

“…An accurate digital terrain model (DTM) is critical to many applications ranging from transportation planning and landform monitoring to forest and water resource management [1,2]. Although technologies, such as aerial photogrammetry, have been available in the past to generate DTMs, the use of airborne discrete LiDAR (Light Detection and Ranging) data revolutionizes the generation of the digital representation of a terrain surface in terms of accuracy and resolution [2,3].…”

Evaluating the effects of generalisation approaches and DEM resolution on the extraction of terrain indices in KwaZulu Natal, South Africa

“…This was done using a similar approach proposed by (Gillin, Bailey, McGuire and Prisley, 2015) and (Maynard and Johnson, 2014). We compared the values of terrain matrices derived from 1m resolution using the original LiDAR DEM resampled to six lower resolution LiDAR derived DEMS, (5,10,20,30,60 and 90m grid resolution intervals). Three interpolation methods namely, mean aggregation (MA), nearest neighbour resampling (NNR) and a hydrological conditioned approach known as topo-toraster interpolation (HCD) were evaluated to determine which generalisation and resolution combination best represented the 1m (LiDAR true ground measurement) terrain parameters.…”