Abstract
Abstract. Remote sensing and photogrammetry techniques have demonstrated to be an important tool for the characterization of forest ecosystems. Nonetheless, the use of these techniques requires an accurate digital terrain model (DTM) for the height normalization procedure, which is a key step prior to any further analyses. In this manuscript, we assess the extraction of the DTM for different techniques (airborne laser scanning: ALS, terrestrial laser scanning: TLS, and digital aerial photogrammetry in unmanned aerial vehicle: UAV-DAP), processing tools with different algorithms (FUSION/LDV© and LAStools©), algorithm parameters, and plot characteristics (canopy and shrub cover, and terrain slope). To do this, we compare the resulting DTMs with one used as reference and extracted from classic surveying measurements. Our results demonstrate, firstly, that ALS and reference DTMs are similar in the different scenarios, except for steep slopes. Secondly, TLS DTMs are slightly less accurate than those extracted for ALS, since items such as trunks and shrubs cause a great occlusion due to the proximity of the instrument, and some of the points filtered as ground correspond to these items as well, therefore a finer setting of algorithm parameters is required. Lastly, DTMs extracted for UAV-DAP in dense canopy scenarios have a low accuracy, however, accuracy may be enhanced by modifying the processing tool and algorithm parameters. An accurate DTM is essential for further forestry applications, therefore, to know how to take advantage of the available data to obtain the most accurate DTM is also fundamental.
Highlights
The importance of remote sensing and photogrammetry to characterize forest ecosystems is well known due to the many studies addressed in recent decades (Torabzadeh et al, 2014; Masek et al, 2015; Minařík and Langhammer, 2016)
Laser scanning has played a key role in the characterization of the vertical forest structure (Lim et al, 2003; Smart et al, 2012), since laser beams go through the different vertical layers and several returns are registered by the sensor
Photogrammetry is more limited in detecting the vertical forest structure (Filippelli et al, 2019), since aerial images mostly capture the crown of the trees, and the rest of the vertical structure beneath them is occluded or in the shade
Summary
The importance of remote sensing and photogrammetry to characterize forest ecosystems is well known due to the many studies addressed in recent decades (Torabzadeh et al, 2014; Masek et al, 2015; Minařík and Langhammer, 2016). Laser scanning has played a key role in the characterization of the vertical forest structure (Lim et al, 2003; Smart et al, 2012), since laser beams go through the different vertical layers and several returns are registered by the sensor. One of the greatest common advantages over more traditional techniques is that their respective sensors may be airborne and spaceborne, collecting data over large areas These techniques present some differences regarding the format of the raw data - images and laser beams for photogrammetry and laser scanning, respectively - and the methodology used to process their respective products. Photogrammetry is more limited in detecting the vertical forest structure (Filippelli et al, 2019), since aerial images mostly capture the crown of the trees, and the rest of the vertical structure beneath them is occluded or in the shade
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