Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

Author:

Wang Yunsheng,Kukko Antero,Hyyppä Eric,Hakala Teemu,Pyörälä Jiri,Lehtomäki Matti,El Issaoui Aimad,Yu Xiaowei,Kaartinen Harri,Liang XinlianORCID,Hyyppä Juha

Abstract

Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

Funder

Academy of Finland

Strategic Research Council

Publisher

Elsevier BV

Subject

Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics,Forestry

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