LiDAR accuracy on North American mountain summits

Mountainous terrain is increasingly being measured and mapped by airplane-based LiDAR (Light Detection and Ranging) techniques, but the accuracy of these measurements in such topographically variable terrain is not well understood. For this study, we measured 179 mountain summits with differential GNSS static surveys and compared summit elevation and location measurements to those measured by LiDAR in point cloud data sets. We measured summits in 13 US states (Washington, Idaho, Montana, Utah, California, Nevada, Arizona, New Mexico, Michigan, Wisconsin, Kentucky, Colorado, and Pennsylvania) and two Canadian provinces (British Columbia and Nova Scotia). Summits included icecapped peaks, open rocky peaks, and tree-covered peaks ranging in elevation from 490 m to over 4000 m. LiDAR-point-cloud-derived summit elevations and locations were computed using four different methods: manual processing, highest ground return, highest return, and Lastools reclassification. The average one-sigma LiDAR vertical errors for each method were 0.50 m, 1.09 m, 9.83 m, and 1.96 m, respectively. Average one-sigma horizontal errors were 3.03 m, 2.41 m, 5.17 m, and 3.78 m, respectively. Errors are also presented separately for each type of summit. Error sources include sharp summits being unsampled, dense vegetation misclassified as ground, human-created structures misclassified as ground, snow/ice that can melt over time, and summit erosion over time.

Authors

Institutions

Publication Details

Journal
Progress in Physical Geography Earth and Environment
Published
2025-11-26
DOI
https://doi.org/10.1177/03091333251401361
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.80
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LiDAR accuracy on North American mountain summits

Andrew Kirmse, R. Hensley, Kyle Bretherton, Kathryn Stanchak et al.
1 citations
Progress in Physical Geography Earth and Environment
Remote Sensing and LiDAR Applications
0.80
article

LiDAR accuracy on North American mountain summits

Andrew Kirmse, R. Hensley, Kyle Bretherton, Kathryn Stanchak, Eric Gilbertson
article en
1 citations

Abstract

Mountainous terrain is increasingly being measured and mapped by airplane-based LiDAR (Light Detection and Ranging) techniques, but the accuracy of these measurements in such topographically variable terrain is not well understood. For this study, we measured 179 mountain summits with differential GNSS static surveys and compared summit elevation and location measurements to those measured by LiDAR in point cloud data sets. We measured summits in 13 US states (Washington, Idaho, Montana, Utah, California, Nevada, Arizona, New Mexico, Michigan, Wisconsin, Kentucky, Colorado, and Pennsylvania) and two Canadian provinces (British Columbia and Nova Scotia). Summits included icecapped peaks, open rocky peaks, and tree-covered peaks ranging in elevation from 490 m to over 4000 m. LiDAR-point-cloud-derived summit elevations and locations were computed using four different methods: manual processing, highest ground return, highest return, and Lastools reclassification. The average one-sigma LiDAR vertical errors for each method were 0.50 m, 1.09 m, 9.83 m, and 1.96 m, respectively. Average one-sigma horizontal errors were 3.03 m, 2.41 m, 5.17 m, and 3.78 m, respectively. Errors are also presented separately for each type of summit. Error sources include sharp summits being unsampled, dense vegetation misclassified as ground, human-created structures misclassified as ground, snow/ice that can melt over time, and summit erosion over time.

Progress in Physical Geography Earth and Environment
University of Nevada, Las Vegas (US), Film Independent (US), Oldham Council (GB), Seattle University (US)
Openalex Percentile: Top 22%
Remote Sensing and LiDAR Applications
0.80
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.