Abstract
The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.
Highlights
Unmanned Aerial Systems (UASs) are pre-programmed flying robots made up of an unmanned aerial vehicle (UAV) and a ground control system
Imagery can reach a sub-decimeter ground sample distance (GSD), and the revisit period between two acquisitions can be selected in order to fit diverse scales of ecological phenomena
The Structure from Motion” (SfM) algorithms originate from the field of computer vision and aim to automatically determine scene geometry, camera calibration, position and orientation from an unordered overlapping collection of images [7]
Summary
Unmanned Aerial Systems (UASs) are pre-programmed flying robots made up of an unmanned aerial vehicle (UAV) and a ground control system. UASs, in particular, are likely to become a versatile tool for scientists and environmentalists [2,3,4]. Along with this rising use of drones, dense three-dimensional reconstruction through the combined use of photogrammetry and “Structure from Motion” (SfM) state-of-the-art techniques has triggered the “come back of photogrammetry” [5]. The SfM algorithms originate from the field of computer vision and aim to automatically determine scene geometry, camera calibration, position and orientation from an unordered overlapping collection of images [7].
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