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Accuracy of a high-resolution lidar terrain model under a conifer forest canopy

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Reutebuch, Stephen E, McGaughey, Robert J, Andersen, Hans-Erik, Carson, Ward W·2003·Canadian Journal of Remote Sensing

Abstract

Airborne laser scanning systems (commonly referred to as light detection and ranging or lidar systems) can provide terrain elevation data for open areas with a vertical accuracy of 15 cm. Accuracy in heavily forested areas has not been thoroughly tested. In this study, a high-resolution digital terrain model (DTM) was produced from high-density lidar data. Vegetation in the 500-ha mountainous study area varied from bare ground to dense 70-year-old conifer forest. Conventional ground survey methods were used to collect coordinates and near-ground vegetation heights at 347 ground checkpoints distributed under a range of canopy covers. These points were used to check the DTM accuracy. The mean DTM error was 0.22 ± 0.24 m (mean ± SD). DTM elevation errors for four tree canopy cover classes were: clearcut 0.16 ± 0.23 m, heavily thinned 0.18 ± 0.14 m, lightly thinned 0.18 ± 0.18 m, and uncut 0.31 ± 0.29 m. These DTM errors show a slight increase with canopy density but the differences are strikingly small. In general, the lidar DTM was found to be extremely accurate and potentially very useful in forestry.

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Metadata

Authors
Reutebuch, S. E., McGaughey, R. J., Andersen, H.-E., & Carson, W. W.
Venue
Canadian Journal of Remote Sensing
Published
2003-10
Volume
29
Issue
5
Pages
527-535
ISSN
  • 0703-8992 (ISSN-L)
  • 1712-7971
OpenAlex
W2097631297
Cited by
382
References
0
FWCI
19.04
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. https://app.cochat.ai/papers/10.5589/m03-022
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Reutebuch, S. E., McGaughey, R. J., Andersen, H.-E., & Carson, W. W. (2003). Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing. https://doi.org/10.5589/m03-022

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.