Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest

Aryal, Raja Ram; Latifi, Hooman; Heurich, Marco; Hahn, Michael

doi:10.1007/s41064-017-0023-2

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…Most of these studies use purely automatic filtering methods to identify ground points, whereas the point clouds used in the current study were classified using a combination of semiautomatic and manual filtering techniques (Sithole and Vosselman, ). Automatic filtering methods are not good at identifying ground points in highly sloping areas with vegetation cover (Aryal et al., ). Therefore, if errors were produced in the filtering process for this type of area in the aforementioned studies, the errors may be wrongly attributed as a direct effect of the slope and not as a consequence of the use of a filter or filtering parameters.…”

Section: Resultsmentioning

confidence: 99%

PpC: a new method to reduce the density of lidar data. Does it affect theDEMaccuracy?

Buján

González-Ferreiro

Cordero

et al. 2019

The Photogrammetric Record

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In cost-benefit analysis of lidar data acquisition, point density is often artificially reduced in order to examine how this affects the quality of derived products. However, the performance of the different density reduction methods has not yet been compared and their influence on the accuracy of the models and results has not been evaluated. A novel method for reducing the point density, termed Proportional per Cell (PpC), is presented and compared with the performance of three other reduction methods, examining their influence on the accuracy of lidar-derived digital surface models using ISPRS reference data. The results indicate that the PpC method was better at conserving the characteristics of the original data. However, point density, sample type and slope had a greater influence than the reduction method used.Hyperlinks to the 3D point clouds (online graph). Land cover information was obtained from Buj an (2019). Surface percentage for bare earth (and low vegetation) in orange, buildings in blue and medium/high vegetation in green for each sample.The Photogrammetric Record

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Section: Resultsmentioning

confidence: 99%

PpC: a new method to reduce the density of lidar data. Does it affect theDEMaccuracy?

Buján

González-Ferreiro

Cordero

et al. 2019

The Photogrammetric Record

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“…The point density of the raw point cloud data obtained by the Riegl Q680i laser scanner is approximately 16 points/m². The production of bare-earth DEM from the LiDAR point cloud involves two main steps: ground filtering and processing of filtered ground points in an interpolation routine (Aryal et al, 2017). In the separating the ground and non-ground points, TIN, slope, interpolation, segmentation, morphological or interpretation -based approaches are widely used (Dragos and Karstenb, 2008;Polat and Uysal, 2015;Dong and Chen, 2017).…”

Section: Dem Generationmentioning

confidence: 99%

“…Digital elevation model (DEM) is the basis of computer-based topographic modeling and is one of the most important data for terrain-related applications. DEMs are widely applied in the fields of forestry (Aryal et al, 2017;Goodbody et al, 2018), agriculture (Tarolli et al, 2019), hydrology (Beven and Kirkby, 1979;Tarboton, 2003), soil (Blöschl and Sivapalan, 1995;Behrens et al, 2010;Florinsky, 2016;Behrens et al, 2018), landform (Flores-Prieto et al, 2015), military (Talhofer et al, 2015), etc.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

Yurtseven¹

2019

Eurasian Journal of Forest Science

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DEMs (Digital Elevation Model) generated with different remote sensing techniques and technologies. DEMs are used to determine the changes of vegetation in forests depending on topographical factors. The accuracy of DEMs has a major impact on the planning and management of forests. In this study, the accuracy of two different DEM data sources, which are frequently used in the modeling of topographic changes in large field studies in forestry, was compared with the LiDAR-based DEM dataset on a forest site. In this context, three different DEM source were used. One of them was the 10 m interval contour lines of 1:25,000 scale aerial photogrammetry based standard topographical maps which are produced by National General Directorate of Mapping. Topomap contour lines are transformed to grid based DEMs by using TIN and ANUDEM based approaches at 2.5, 5, 10 and 30 m resolutions. The other data was ASTER GDEM (1 arc-second ASTER GDEM Version 2, approximately 30 m resolution). The final and reference data is the LiDAR based, 0.25 m resolution DEM. In total, 33 DEM datasets are compared with the LiDAR-based DEM dataset. For these data sets, five difference metrics were calculated: pixel based difference models, the areal and volumetric difference of surface models, the areal difference of slope classes and the areal difference of aspect classes. According to the results of the analysis, the resolution, according to the topographic characteristics of the area and selected interpolation approaches has an effect on DEM modeling and DEM-derived metrics. In addition, the forest structure has a major impact on the accuracy of ASTER GDEM data.

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“…Tree height is highly variable throughout forest succession, and it is considered an important old-growth attribute (Spies 2004, McElhinny et al 2006a. Tree height was extracted from the difference between the Digital Surface Model (DSM) and DTM, where DSM is derived from the first returns and DTM from the last (Hopkinson et al 2006, Andersen et al 2006, Aryal et al 2017. A list and description of the LiDAR metrics, mostly derived from height returns, are available in Table 2.3.…”

Section: Old-growth Attributesmentioning

confidence: 99%

“…The framework utilized in this study offers a holistic view of old-growth and ESs values in the landscape, which provides the opportunity to set targets for their conservation relative to the landscape provision. All ecosystem services layers generated for this work were derived from field measurements and LiDAR surveys, which can offer a great insight into forests and ecosystem services (Andrew et al 2014, Campbell et al 2017, Aryal et al 2017. Even though the ESs estimates are surrogates or partial measures of the actual ecosystem services and old-growth values, it does not prevent their utilization in this work (Margules and Pressey 2000).…”

Section: Ecosystem Services Reservesmentioning

confidence: 99%

Managing old-growth forests for multiple ecosystem services

Barros¹

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Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest

Cited by 10 publications

References 19 publications

PpC: a new method to reduce the density of lidar data. Does it affect theDEMaccuracy?

PpC: a new method to reduce the density of lidar data. Does it affect theDEMaccuracy?

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

Managing old-growth forests for multiple ecosystem services

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