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

Abstract

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.

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

“…This was because of the limitations of the terrestrial photography itself. For the extraction of the upper position that cannot be taken on the ground, regardless of the feasibility of extraction or the measurement accuracy, the terrestrial photogrammetry is clearly lagging behind the UAV photogrammetry [33,55,56]. In view of the limitation of terrestrial photography, this paper introduced the Kunze M. curve equation of the point cloud model to estimate individual tree height.…”

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

“…In order to minimize the data collection timeframe but still include all plots, light and weather conditions varied per flight. For each flight, the aircraft was set to "automatic mode", flying over the plots at 80 m above ground in a crisscross pattern using the onboard GNSS and compass for navigation following a preflight programmed flight plan (see [52] for details). The camera was aligned nadir and perpendicular to the flight direction and triggered automatically every 3-4 m by the drone, resulting in forward overlaps >95% and ground sampling distances (GSD) of about 1.1 cm.…”

“…As a result, flight heights were roughly stable within plots but occasionally varied between plots. The mean realized flight height was 96 m (SD: 19 m), which generated a sideward overlap between 83% and 91% and ground sampling distances that varied between one and two cm [52].…”

Plant composition, not richness, drives occurrence of specialist herbivores

“…Overall vegetation structure was characterised by the remotely sensed terrain ruggedness index (TRI) and the stand structural complexity index (SSCI). The mean TRI per plot was calculated from photogrammetrically derived digital surface models (5 cm resolution), which were generated from images taken by a drone during the vegetation period between 2016 and 2018 (Frey et al, 2018). The TRI describes the canopy structure as the mean height difference between a central pixel and its direct neighbours (Wilson et al, 2007).…”