Abstract
ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.
Highlights
Airborne Light Detection and Ranging (LiDAR) is an active remote sensing technology that combines laser and positioning measurements to survey and map with high accuracy objects over a given surface (Carter et al, 2012)
The accuracy of digital terrain modeling depends on several other factors: a) sensor parameters and flight characteristics, such as the light detection and ranging (LiDAR) pulse density, which is a consequence of factors such as elevation and flight speed (Ruiz et al, 2014); b) characteristics of the land surface, such as topography and presence of dense and complex vegetation coverage (Sithole & Vosselman, 2004); and c) the processing techniques used to generate the digital terrain models (DTMs), such as the ground filtering algorithm and subsequently the interpolation method, as well as the spatial resolution of the DTM (Liu, 2008)
The Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), and Progressive Triangulated Irregular Network (PTIN) ground filtering algorithms returned a similar classification of terrain points, whereas the Progressive Morphological Filter (PMF) showed a distinct pattern in relation to the others (Figure 3)
Summary
Airborne Light Detection and Ranging (LiDAR) is an active remote sensing technology that combines laser and positioning measurements to survey and map with high accuracy objects over a given surface (Carter et al, 2012). The Digital Terrain Model (DTM) derived from airborne LiDAR data is a representation of the bare surface, in other words, a surface free of any manmade and/or natural objects. The accuracy of digital terrain modeling depends on several other factors: a) sensor parameters and flight characteristics, such as the LiDAR pulse density (pulses∙m-2), which is a consequence of factors such as elevation and flight speed (Ruiz et al, 2014); b) characteristics of the land surface, such as topography and presence of dense and complex vegetation coverage (Sithole & Vosselman, 2004); and c) the processing techniques used to generate the DTM, such as the ground filtering algorithm and subsequently the interpolation method, as well as the spatial resolution of the DTM (Liu, 2008)
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