Reference : Comparison of UAS photogrammetric products for tree detection and characterization of...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/2268/211826
Comparison of UAS photogrammetric products for tree detection and characterization of coniferous stands
English
Bonnet, Stéphanie mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
Lisein, Jonathan [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
Lejeune, Philippe mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
14-Jun-2017
International Journal of Remote Sensing
Taylor & Francis
38
19
5310-5337
Yes (verified by ORBi)
International
0143-1161
1366-5901
Abingdon
United Kingdom
[en] Unmanned Aerial Vehicle ; Local Maxima ; Individual Tree Detection ; Photogrammetry ; Canopy Height ; Forestry
[en] The use of Unmanned Aerial Systems (UAS) opens a new era for remote sensing and forest management, which requires accurate and regular quantification of resources. In this study, we propose a comprehensive workflow to detect trees and assess forest attributes in the particular context of coniferous stands in transformation from even-aged to uneven-aged stands, using UAS imagery, from data acquisition to model construction. We implement a local maxima detection to identify the tree tops, based on a fixed-radius moving window in a Canopy Height Model (CHM) and images produced from UAS surveys. To compare the contribution of different photogrammetric products, we analysed the local maxima detected from the CHM, from three image types (individual rectified and ortho-rectified images and ortho-mosaic) and from a combination of both CHM and images. The local maxima detection gave promising results, with low omission and true-positive rates of up to 89.2%. A filtering process of false positives was implemented, using a supervised classification which decreased the false positives up to 2.6%. Based on the local maxima combined with an area-based approach, we constructed models to assess top height (R2: 83%, root mean square error [RMSE]: 5.7%), number of stems (R2: 71%, RMSE: 28.3%), basal area (R2: 70%, RMSE: 16.2%), volume (R2: 69%, RMSE: 20.1%), and individual tree height (R2: 70%, RMSE: 7.2%). Despite a suboptimal data acquisition, our simple and flexible method has yielded good results and shows great potential for application.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/211826
10.1080/01431161.2017.1338839

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