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Article citations


Vilikka, M., Packalén, P. and Maltamo, M. (2012) The Suitability of Leaf-Off Airborne Laser Scanning Data in an Area-Based Forest Inventory of Coniferous and Deciduous Trees. Silva Fennica, 46, 99-110.

has been cited by the following article:

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

    AUTHORS: Marco A. Contreras, Wesley Staats, Jian Yiang, David Parrott

    KEYWORDS: Elevation Change Error, Terrain Relief, Ruggedness and Slope, Remote Sensing

    JOURNAL NAME: Journal of Geographic Information System, Vol.9 No.3, June 21, 2017

    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.