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Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective

Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a... In this study, unmanned aerial vehicle (UAV) image (photograph)-based point clouds and products were compared to airborne lidar-based data and products over a forested area. The test site is located in Germany, 15 km southeast of Jena. A total area of approximately 175 ha was covered during a UAV flight campaign. For this study, a subset of 4 ha (200 m × 200 m) was defined. The UAV–lidar comparison was accomplished at three different data levels: (1) point-like level (raster of maxima), (2) surface level (canopy height models), and (3) tree level (detection rate). In general, a high match between lidar- and UAV-based data/products was observed. The UAV data exhibits more details which are of particular importance for the detection of small trees. While using lidar data, 45 out of 205 trees were not detected, however only 14 trees were missed out when UAV data was used. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Remote Sensing Taylor & Francis

Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective

Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective

International Journal of Remote Sensing , Volume 38 (8-10): 16 – May 19, 2017

Abstract

In this study, unmanned aerial vehicle (UAV) image (photograph)-based point clouds and products were compared to airborne lidar-based data and products over a forested area. The test site is located in Germany, 15 km southeast of Jena. A total area of approximately 175 ha was covered during a UAV flight campaign. For this study, a subset of 4 ha (200 m × 200 m) was defined. The UAV–lidar comparison was accomplished at three different data levels: (1) point-like level (raster of maxima), (2) surface level (canopy height models), and (3) tree level (detection rate). In general, a high match between lidar- and UAV-based data/products was observed. The UAV data exhibits more details which are of particular importance for the detection of small trees. While using lidar data, 45 out of 205 trees were not detected, however only 14 trees were missed out when UAV data was used.

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References (24)

Publisher
Taylor & Francis
Copyright
© 2016 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1366-5901
DOI
10.1080/01431161.2016.1225181
Publisher site
See Article on Publisher Site

Abstract

In this study, unmanned aerial vehicle (UAV) image (photograph)-based point clouds and products were compared to airborne lidar-based data and products over a forested area. The test site is located in Germany, 15 km southeast of Jena. A total area of approximately 175 ha was covered during a UAV flight campaign. For this study, a subset of 4 ha (200 m × 200 m) was defined. The UAV–lidar comparison was accomplished at three different data levels: (1) point-like level (raster of maxima), (2) surface level (canopy height models), and (3) tree level (detection rate). In general, a high match between lidar- and UAV-based data/products was observed. The UAV data exhibits more details which are of particular importance for the detection of small trees. While using lidar data, 45 out of 205 trees were not detected, however only 14 trees were missed out when UAV data was used.

Journal

International Journal of Remote SensingTaylor & Francis

Published: May 19, 2017

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