Abstract
In forestry, aerial photogrammetry by means of Unmanned Aerial Systems (UAS) could bridge the gap between detailed fieldwork and broad-range satellite imagery-based analysis. However, optical sensors are only poorly capable of penetrating the tree canopy, causing raw image-based point clouds unable to reliably collect and classify ground points in woodlands, which is essential for further data processing. In this work, we propose a novel method to overcome this issue and generate accurate a Digital Terrain Model (DTM) in forested environments by processing the point cloud. We also developed a highly realistic custom simulator that allows controlled experimentation with repeatability guaranteed. With this tool, we performed an exhaustive evaluation of the survey and sensor settings and their impact on the 3D reconstruction. Overall, we found that a high frontal overlap (95%), a nadir camera angle (90°), and low flight altitudes (less than 100 m) results in the best configuration for forest environments. We validated the presented method for DTM generation in a simulated and real-world survey missions with both fixed-wing and multicopter UAS, showing how the problem of structural forest parameters estimation can be better addressed. Finally, we applied our method for automatic detection of selective logging.
Highlights
The use of Unmanned Aerial Systems (UAS) as remote sensing for environmental monitoring and precision forestry has grown considerably during the last years and has spread worldwide [1,2,3]
We focused on overlap values in the range between between 85% and 95%, considering that values are usually recommended for Structure from Motion (SfM) reconstruction of forests
In this work we approached the problem of forest structural parameters estimation using aerial photogrammetry
Summary
The use of Unmanned Aerial Systems (UAS) as remote sensing for environmental monitoring and precision forestry has grown considerably during the last years and has spread worldwide [1,2,3]. It has emerged as a promising complement to satellite imagery and fieldwork. Satellite imagery and vegetation indices obtained from these are useful for land and forest monitoring at the regional level, but not at a predial scale. Contrariwise, UAS present several advantages such as the possibility of performing precise full-coverage forest maps in a short time, arbitrary revisit lapse, high spatial resolution, cloudiness independence, low cost, and easy operation compared with its counterparts [4].
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