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Impact of slope, aspect and habitat type on LiDAR-derived digital terrain models in a near-natural, heterogenuous temperate forest

Aryal, Raja Ram and Latifi, Hooman and Heurich, Marco and Hahn, Michael (2017) Impact of slope, aspect and habitat type on LiDAR-derived digital terrain models in a near-natural, heterogenuous temperate forest. Photogrammetrie, Fernerkundung, Geoinformation, 85 (4), pp. 243-255. E. Schweizerbartsche Verlagsbuchhandlung. doi: 10.1007/s41064-017-0023-2. ISSN 1432-8364.

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Official URL: https://link.springer.com/article/10.1007/s41064-017-0023-2


Developments in airborne LiDAR data acquisition have provided better horizontal and vertical ground information in the form of 3D point clouds. This has led to satisfactory results of LiDAR-derived digital terrain models (DTMs), also across complex ecosystems like natural forest stands. However, data and site-driven factors such as spatial resolution (point density), topography (slope and aspect), and variation in forest habitat types affect the DTM accuracy. In addition, processing steps like ground filtering and interpolation of ground points may also result in differences in DTM quality. Here, a comparative study was designed by extracting DTMs from two LiDAR data sources (high- and low-density point clouds) and three ground-filtering algorithms (adaptive TIN algorithm with and without the use of mirror points as well as an interpolation-based algorithm). The accuracy of the DTMs was assessed in association with terrain parameter and forest habitat types across heterogeneous forest sites of Bavarian Forest National Park in southeastern Germany. Qualitative analysis was carried out by taking 8300 independent sets of DGPS-recorded sample points. In addition to deriving root-mean-square error (RMSE) and bias, analysis of variance (ANOVA) type II was conducted in a factorial design to assess the influential factors on the observed DTM random error. Results revealed these errors in the DTMs with occasional over- and underestimations up to 1.98 m compared to reference elevation values. DTMs produced from high pulse density LiDAR data were more accurate than those extracted from low pulse density. Furthermore, topographic and forest habitat-type factors significantly contributed to the DTM accuracy. Slope increment showed a direct relationship with DTM error, with higher errors observed in south, southwest, and west aspects. Furthermore, stands dominated by deciduous trees were associated with higher DTM error than other forest habitat types. The applied adaptive TIN ground-filtering algorithms with mirror points and the interpolation-based algorithm both produced comparatively lower error rates, which are, therefore, suggested to reduce interpolation error in DTMs across rugged and heterogeneous forested terrains.

Item URL in elib:https://elib.dlr.de/115522/
Document Type:Article
Title:Impact of slope, aspect and habitat type on LiDAR-derived digital terrain models in a near-natural, heterogenuous temperate forest
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Latifi, Hoomanhooman.latifi (at) uni-wuerzburg.deUNSPECIFIED
Heurich, Marcobavarian forest national park, department of researchUNSPECIFIED
Hahn, Michaelmichael.hahn (at) hft-stuttgart.deUNSPECIFIED
Date:September 2017
Journal or Publication Title:Photogrammetrie, Fernerkundung, Geoinformation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1007/s41064-017-0023-2
Page Range:pp. 243-255
Publisher:E. Schweizerbartsche Verlagsbuchhandlung
Keywords:LiDAR, DTM, Ground filtering, Topography, Forest habitat types, Factorial analysis
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:21 Nov 2017 13:34
Last Modified:06 Sep 2019 15:29

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