Quantifying the Accuracy of LiDAR-Derived DEM in Deciduous Eastern Forests of the Cumberland Plateau

Abstract

Digital elevation models (DEMs) derived from light detection and ranging (LiDAR) technology are becoming the standard in representing terrain surfaces. They have numerous applications in forestry, agriculture, and natural resources. Although elevation errors are much lower than those derived from traditional methods, accuracies have been reported to decrease with terrain slope and vegetation cover. In this study, we quantified the accuracy of airborne Li-DAR-derived DEM in deciduous eastern forests of the Cumberland Plateau. We measured relative elevation changes within field plots located across different slope and ruggedness classes to quantify DEM accuracy. We compared elevation change errors of DEMs derived from three LiDAR datasets: a low-density (~1.5 pts•m ), and a combined dataset. We also compared DEMs obtained by interpolating the ground points using four interpolation methods. Results indicate that mean elevation change error (MECE) increased with terrain slope and ruggedness with an average of 73.6 cm. MECE values ranged from 23.2 cm in areas with lowest slope (0% -39%) and ruggedness (0% -28%) classes to 145.5 cm in areas with highest slope (50% -103%) and ruggedness (46% -103%) classes. We found no significant differences among interpolation methods or LiDAR datasets; the latter of which indicates that similar accuracy levels can be achieved with the low-density datasets.

Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison

“…A mesh is a collection of faces, edges, and vertices that make up a 3D shape [59]. Our plots had flat terrain as the slope, and ruggedness would induce uncertainty and influence our accuracy assessment [60][61][62]. Blender™ uses a Cartesian coordinate system (X, Y, Z); as such, our plane center was located at (0, 0, 0).…”

Data collection tools for river geomorphology studies : LiDAR and traditional methods

“…During summer growing seasons, vegetation can affect the vertical data point collection extent and accuracy. Thick, heavy vegetation cover or understory generally increase LiDAR ground elevation errors (Contreras et al 2017). For example, in the LiDAR-HGR study (Haring et al 2019), LiDAR data were collected over several years and at different times during the year.…”

“…LiDAR data usage provides important lessons learned on the use of hydro-modifications, data resolution enhancement capabilities, criteria for the best riverine geomorphic assessments, and identification of data dropout areas. Moreover, there are time and costs saving advantages for LiDAR surveying applications as they will continue to be improved as the technological advances increase (Mayfield 2015;Contreras et al 2017). Further applications of LiDAR in geomorphic studies are currently being developed using the FG toolbox (Haring et al 2020).…”

“…AIRBORNE LiDAR ADVANTAGES: Mayfield (2015) states that some traditional surveys are required to support LiDAR accuracy, but overall, the LiDAR surveys produce similarly accurate measurements that have advantages over traditional surveys. LiDAR data quality greatly increased over the past 20 yr with increased intensity (signal return strength), laser technology in the number of pulses and returns, and point cloud densities (Contreras et al 2017). The resolution increase in the LiDAR data was described by Dietterick et al (2012) as averaging 9.8 ft (3.0 m) in 2002; 3.3 ft (1.0 m) in 2008; and 1.6 ft (0.49 m) in 2010.…”

“…LiDAR ground elevation measurement errors generally increase with vegetation cover, amount of understory, and variability in terrain slopes (Contreras et al 2017). However, this can also affect ground survey using high-resolution GPS as satellite, base-station sighting, and lines of sight can be problematic for ground survey crews as well.…”