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

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.

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

“…Previous standard errors obtained in the same site [35] have ranged from 5-6% (for backpack and hand-held laser scanning) up to 6-8% (for high-density abovecanopy UAV laser scanning data). In Wang et al [25], the standard error of tree heights estimated from UAV data was 6.1% for trees with a DBH exceeding 15 cm, which is similar to the results reported in [35]. Our results imply that under-canopy UAV laser scanning may provide tree height estimates with a slightly better accuracy than conventional highdensity above-canopy UAV laser scanning measurements.…”

“…Let us then compare our stem detection results against those reported in previous studies using under-canopy UAVs for forest field reference measurements [16,25]. The studies in [16] were conducted on the same test sites as the current study.…”

“…In the paper, we were able to compute stem volume estimates for the detected trees with a relative RMSE of 10% in both sparse and obstructed forest sites by combining stem curves extracted from the under-canopy UAV laser scanner data with tree heights obtained from separate high-density above-canopy UAV laser scanning data. Wang et al (2021) tested the single-flight integration of above-and under-canopy UAV laser scanning for forest investigation in order to obtain accurate canopy height estimates [25]. Recent developments in under-canopy navigation include Higuti et al [26], Schultz et al [27], Cui et al [28], and Lin and Stol [29].…”

Automatic extraction and measurement of individual trees from mobile laser scanning point clouds of forests

“…Above-canopy data could be used to complement the terrestrial viewpoints as shown in Bienert et al (2010) , with above-canopy scans from towers or with intracanopy scans acquired using a drone. Wang et al (2021) present intra- and above-canopy drone flights and compare them with ground-based TLS scans. The dense point clouds in the upper crown area have a geometric accuracy comparable with those from MLS data and have an RMSE of the DBH of 2–4 cm and of the stem curve of 4–7 cm.…”

Looking to the Skies: Realising the Combined Potential of Drones and Thermal Infrared Imagery to Advance Hydrological Process Understanding in Headwaters

“…While TIR has been demonstrated readily capable of identifying stream network extension in bare/unforested regions (such as those common in parts of Europe or high arctic locations), forested regions present a problem whereby dense tree canopies limit the ability of TIR sensors to resolve ground‐level hydrological properties, particularly in coniferous woodland where winter leaf loss does not occur. While understory drone acquisition flights are technically possible (e.g., Hyyppä et al., 2020; Wang et al., 2021), this introduces a substantial risk to both equipment and researchers and is unlikely to provide a universal solution. Research is therefore needed to understand the accuracy with which headwater network extension/surface saturation dynamics can be quantified as a function of varying forest density, as well as the potential for fusion of ground‐ and drone‐based data to provide seamless coverage in areas of patchy forest.…”