Development of a UAV-LiDAR System with Application to Forest Inventory

By Luke Wallace, Arko Lucieer, Christopher Stephen Watson, Darren Turner. Remote Sensing, 2012

713 citations recorded by the source corpus.

We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

Subfields: Environmental Engineering, Geology, Insect Science

View Paper: https://www.semanticscholar.org/paper/4dc55ce73f5ab8497698f333a9c602bc4099c111

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Citations in the corpus

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “UAVs will carry LiDAR and hyperspectral sensors (e.g., Wallace et al. 2012; Zarco-Tejada et al. 2012) that will improve measurements of canopy condition that could then be associated with pest damage.”
Unmanned Aerial Vehicles (UAVs) in environmental biology: a review (2019, methodology): “Wallace et al. (2012) illustrated how an octocopter UAV-LiDAR system could be applied to tree-crown structural measurements of tree location and height.”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “This combination can locate with high accuracy a laser beam spot hitting an object [255].”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, methodology): “…imager, LIDAR, combing the methods including empirical statistical analysis of vegetation indices, estimation based on net primary productivity, and crop growth model have been widely deployed for biomass estimation (Hunt et al., 2005; Swain et al., 2010; Wallace et al., 2012; Bendig et al., 2014).”
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, methodology): “Recently, Wallace et al. (2012) used SfM algorithms to improve the calculation of sensor position and orientation on a lightweight UAS ( ≈ 5 kg with payload) carrying a mini-LIDAR sensor with lightweight GPS and new micro-electromechanical system (MEMS) based IMU equipment (2.4 kg), ﬁ nding…”
Forestry applications of UAVs in Europe: a review (2017, methodology): “In the international specific panorama of forest research, UAVs have been employed for inventory and stand volume estimation (Wallace et al. 2012), tropical forest recovery monitoring (Zahawi et al. 2015), crown closure retrieval (Wang et al. 2015), leaf angle distribution measuring in broadleaf…”
Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests (2019, methodology): “Wallace et al. (2012) presented an early research in the forestry application using UAV-LiDAR and UAV-DAP point clouds.”
Inventory of Small Forest Areas Using an Unmanned Aerial System (2015, background): “In some of the studies [1], the UASs were equipped with laser scanning sensors (UAS-LS).”
Lightweight unmanned aerial vehicles will revolutionize spatial ecology (2013, background): “Wallace et al. (2012) illustrated how these approaches could also be applied to tree-crown structural mapping from an octocopter, demonstrating that within-canopy structural information can be determined.”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, methodology): “Reported highly accurate UAV-borne LiDAR systems are normally those coming with di ﬀ erential GPS stations [48], where high-accuracy GPS measurements are obtainable [49] in addition to highly accurate IMU measurements.”