Abstract
Abstract. For a correct use of metrics derived from processing of the full-waveform return signal from airborne laser scanner sensors any correlation which is not related to properties of the reflecting target must be known and, if possible, removed. In the following article we report on an analysis of correlation between several metrics extracted from the full-waveform return signal and scan characteristics (mainly range) and type of land-cover (urban, grasslands, forests). The metrics taken in consideration are the amplitude, normalized amplitude, width (full width at half maximum), asymmetry indicators, left and right energy content, and the cross-section calculated from width and normalized amplitude considering the range effect. The results show that scan geometry in this case does not have a significant impact scans over forest cover, except for range affecting amplitude and width distribution. Over complex targets such as vegetation canopy, other factors such as incidence angle have little meaning, therefore corrections of range effect are the most meaningful. A strong correlation with the type of land-cover is also shown by the distribution of the values of the metrics in the different areas taken in consideration.
Highlights
Airborne LiDAR in the past ten years has seen rapid growth in various applications
For a clear report of results we include in figure 4 the plots of the distributions of the metrics extracted in the different strips
Metrics from FW can be used in various applications as they are related to the texture of the targets which they intercept and on the structure of elements which only partially obstruct the laser cone
Summary
Airborne LiDAR (or Airborne Laser Scanning - ALS) in the past ten years has seen rapid growth in various applications. In the case of discrete return (DR) data the available information is the point position in space, its ordinal position in terms of echoes (unique or first, intermediate or last), and its radiometric information commonly referred to as intensity (Shan & Toth 2008). The vegetation layer is differentiated from other classes (e.g. buildings and ground) by using height from ground methods and removing nonvegetation by classifiers which use LiDAR derived features both from raster products and distribution of point characteristics (Höfle et al 2012). Brandtberg (2007) uses a different approach, but the same characteristic of multiple return data, to define criteria to separate vegetation from other elements using digraphs (Ross & Wright 1992) for classifying points The spatial distribution of echoes and their return ordinal number is used as input in descriptors, such as the slope adaptive echo ratio, which is correlated with canopy structure and density (Höfle et al 2008, Rutzinger et al 2008, Eysn et al 2012). Brandtberg (2007) uses a different approach, but the same characteristic of multiple return data, to define criteria to separate vegetation from other elements using digraphs (Ross & Wright 1992) for classifying points
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