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 LiDAR-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−2), a high-density (~40 pts•m−2), 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.
Highlights
Digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) technology are becoming the standard in representing terrain surfaces due to its ability to accurately describe relief across large landscapes
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
Elevation changes were measured between plot center and multiple points located at different distances and azimuths using a star-shaped plot design. This alternative ground truth data collection method is more appropriate to assess the ability of LiDAR-derived DEM to accurately represent terrain surfaces instead of comparing individual point elevations
Summary
Digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) technology are becoming the standard in representing terrain surfaces due to its ability to accurately describe relief across large landscapes. The accuracy in altimetry of high-resolution LiDAR-derived DEMs is commonly reported by data providers to be between 15 - 25 cm [9] [10] [11]. Several studies have demonstrated that vegetation cover, terrain slope, and terrain variability can have a significant effect on elevation errors [21] [22] [23] [24] [25]. Elevation errors increase with vegetation cover and amount of understory as well as with increasing terrain slope and variability [26] [27] [28]
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