Tree Height Estimation Using Field Measurement and Low-Cost Unmanned Aerial Vehicle (UAV) at Phnom Kulen National Park of Cambodia

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s−1. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Simulation of UAV Systems

Acta Polytechnica ◽

10.14311/754 ◽

2005 ◽

Vol 45 (4) ◽

Author(s):

P. Kaňovský ◽

L. Smrcek ◽

C. Goodchild

Keyword(s):

Dynamical System ◽

Unmanned Aerial Vehicle ◽

Low Cost ◽

Design Tool ◽

Inertial System ◽

Principal Function ◽

Low Weight ◽

High Bandwidth ◽

Aerial Vehicle ◽

Selection Of

The study described in this paper deals with the issue of a design tool for the autopilot of an Unmanned Aerial Vehicle (UAV) and the selection of the airdata and inertial system sensors. This project was processed in cooperation with VTUL a PVO o.z. [1]. The feature that distinguishes the autopilot requirements of a UAV (Figs. 1, 7, 8) from the flight systems of conventional manned aircraft is the paradox of controlling a high bandwidth dynamical system using sensors that are in harmony with the low cost low weight objectives that UAV designs are often expected to achieve. The principal function of the autopilot is flight stability, which establishes the UAV as a stable airborne platform that can operate at a precisely defined height. The main sensor for providing this height information is a barometric altimeter. The solution to the UAV autopilot design was realised with simulations using the facilities of Matlab® and in particular Simulink®[2].

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Cotton Yield Estimation Using Very High-Resolution Digital Images Acquired with a Low-Cost Small Unmanned Aerial Vehicle

Transactions of the ASABE ◽

10.13031/trans.59.11831 ◽

2016 ◽

Vol 59 (6) ◽

pp. 1563-1574 ◽

Cited By ~ 5

Keyword(s):

High Resolution ◽

Unmanned Aerial Vehicle ◽

Digital Images ◽

Low Cost ◽

Cotton Yield ◽

Yield Estimation ◽

Aerial Vehicle ◽

Very High

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Design of a Low-Cost Fixed Wing UAV

MATEC Web of Conferences ◽

10.1051/matecconf/201815902045 ◽

2018 ◽

Vol 159 ◽

pp. 02045

Author(s):

Mochammad Ariyanto ◽

Joga D. Setiawan ◽

Teguh Prabowo ◽

Ismoyo Haryanto ◽

Munadi

Keyword(s):

Unmanned Aerial Vehicle ◽

Low Cost ◽

Flight Test ◽

Body Part ◽

Aerial Vehicle ◽

Straight Wing

This research will try to design a low cost of fixed-wing unmanned aerial vehicle (UAV) using low-cost material that able to fly autonomously. Six parameters of UAV’s structure will be optimized based on basic airframe configuration, wing configuration, straight wing, tail configuration, fuselage material, and propeller location. The resulted and manufactured prototype of fixed-wing UAV will be tested in autonomous fight tests. Based on the flight test, the developed UAV can successfully fly autonomously following the trajectory command. The result shows that low-cost material can be used as a body part of fixed-wing UAV.

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Aerial physical interaction via IDA-PBC

The International Journal of Robotics Research ◽

10.1177/0278364919835605 ◽

2019 ◽

Vol 38 (4) ◽

pp. 403-421 ◽

Cited By ~ 4

Author(s):

Burak Yüksel ◽

Cristian Secchi ◽

Heinrich H. Bülthoff ◽

Antonio Franchi

Keyword(s):

Unmanned Aerial Vehicle ◽

Control Method ◽

Low Cost ◽

Surface Inspection ◽

Physical Interaction ◽

External Disturbances ◽

Aerial Vehicle ◽

Passivity Based Control ◽

The Stability ◽

First Time

This paper proposes the use of a novel control method based on interconnection and damping assignment–passivity-based control (IDA-PBC) in order to address the aerial physical interaction (APhI) problem for a quadrotor unmanned aerial vehicle (UAV). The apparent physical properties of the quadrotor are reshaped in order to achieve better APhI performances, while ensuring the stability of the interaction through passivity preservation. The robustness of the IDA-PBC method with respect to sensor noise is also analyzed. The direct measurement of the external wrench, needed to implement the control method, is compared with the use of a nonlinear Lyapunov-based wrench observer and advantages/disadvantages of both methods are discussed. The validity and practicability of the proposed APhI method is evaluated through experiments, where for the first time in the literature, a lightweight all-in-one low-cost force/torque (F/T) sensor is used onboard of a quadrotor. Two main scenarios are shown: a quadrotor responding to external disturbances while hovering (physical human–quadrotor interaction), and the same quadrotor sliding with a rigid tool along an uneven ceiling surface (inspection/painting-like task).

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Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network

Remote Sensing ◽

10.3390/rs11111271 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1271 ◽

Cited By ~ 1

Author(s):

Haiqing He ◽

Yeli Yan ◽

Ting Chen ◽

Penggen Cheng

Keyword(s):

Neural Network ◽

Basis Function ◽

Rbf Neural Network ◽

Low Cost ◽

Tree Height ◽

Point Clouds ◽

Mountainous Terrain ◽

Forest Plantation ◽

Mountainous Areas ◽

Height Estimation

Tree heights are the principal variables for forest plantation inventory. The increasing availability of high-resolution three-dimensional (3D) point clouds derived from low-cost Unmanned Aerial Vehicle (UAV) and modern photogrammetry offers an opportunity to generate a Canopy Height Model (CHM) in the mountainous areas. In this paper, we assessed the capabilities of tree height estimation using UAV-based Structure-from-Motion (SfM) photogrammetry and Semi-Global Matching (SGM). The former is utilized to generate 3D geometry, while the latter is used to generate dense point clouds from UAV imagery. The two algorithms were coupled with a Radial Basis Function (RBF) neural network to acquire CHMs in mountainous areas. This study focused on the performance of Digital Terrain Model (DTM) interpolation over complex terrains. With the UAV-based image acquisition and image-derived point clouds, we constructed a 5 cm-resolution Digital Surface Model (DSM), which was assessed against 14 independent checkpoints measured by a Real-Time Kinematic Global Positioning System RTK GPS. Results showed that the Root Mean Square Errors (RMSEs) of horizontal and vertical accuracies are approximately 5 cm and 10 cm, respectively. Bare-earth Index (BEI) and Shadow Index (SI) were used to separate ground points from the image-derived point clouds. The RBF neural network coupled with the Difference of Gaussian (DoG) was exploited to provide a favorable generalization for the DTM from 3D ground points with noisy data. CHMs were generated using the height value in each pixel of the DSM and by subtracting the corresponding DTM value. Individual tree heights were estimated using local maxima algorithm under a contour-surround constraint. Two forest plantations in mountainous areas were selected to evaluate the accuracy of estimating tree heights, rather than field measurements. Results indicated that the proposed method can construct a highly accurate DTM and effectively remove nontreetop maxima. Furthermore, the proposed method has been confirmed to be acceptable for tree height estimation in mountainous areas given the strong linear correlation of the measured and estimated tree heights and the acceptable t-test values. Overall, the low-cost UAV-based photogrammetry and RBF neural network can yield a highly accurate DTM over mountainous terrain, thereby making them particularly suitable for rapid and cost-effective estimation of tree heights of forest plantation in mountainous areas.

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