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Open AccessArticle

UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline

1
Chair of Remote Sensing and Landscape Information Systems FeLis, University of Freiburg, D-79106 Freiburg, Germany
2
Chair of Geobotany, Faculty of Biology, University of Freiburg, D-79106 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 912; https://doi.org/10.3390/rs10060912
Received: 19 April 2018 / Revised: 29 May 2018 / Accepted: 8 June 2018 / Published: 9 June 2018
(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)
Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called ‘Structure from motion’ (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density. View Full-Text
Keywords: UAV; photogrammetry; SfM; image aggregation; forest; sensitivity analyses; reconstruction quality UAV; photogrammetry; SfM; image aggregation; forest; sensitivity analyses; reconstruction quality
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MDPI and ACS Style

Frey, J.; Kovach, K.; Stemmler, S.; Koch, B. UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline. Remote Sens. 2018, 10, 912.

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