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

Remote Sensing - Tập 4 Số 6 - Trang 1519-1543
Luke Wallace1, Arko Lucieer1, Christopher Watson1, Darren Turner1
1School of Geography and Environmental Studies, University of Tasmania, Hobart, TAS, 7001, Australia

Tóm tắt

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.

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Tài liệu tham khảo

Leckie, 2008, Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests, Int J. Remote Sens, 29, 1339, 10.1080/01431160701736489

Lefsky, 2002, Lidar remote sensing for ecosystem studies, Bioscience, 52, 19, 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2

Akay, 2009, Using LiDAR technology in forestry activities, Environ. Monit. Assess, 151, 117, 10.1007/s10661-008-0254-1

Erdody, 2010, Fusion of LiDAR and imagery for estimating forest canopy fuels, Remote Sens. Environ, 114, 725, 10.1016/j.rse.2009.11.002

Morsdorf, 2009, Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling, Remote Sens. Environ, 113, 2152, 10.1016/j.rse.2009.05.019

Lim, 2003, LiDAR remote sensing of forest structure, Prog. Phys. Geog, 27, 88, 10.1191/0309133303pp360ra

Barazzetti, 2010, Automation in 3D reconstruction: Results on different kinds of close-range blocks, Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci, 38, 55

Chiabrando, 2011, UAV and RPV systems for photogrammetric surveys in archaelogical areas: Two tests in the Piedmont region (Italy), J. Archaeol. Sci, 38, 697, 10.1016/j.jas.2010.10.022

Sugiura, 2005, Remote-sensing technology for vegetation monitoring using an unmanned helicopter, Biosyst. Eng, 90, 369, 10.1016/j.biosystemseng.2004.12.011

Laliberte, 2011, Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments, Remote Sens, 3, 2529, 10.3390/rs3112529

Hunt, 2010, Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring, Remote Sens, 2, 290, 10.3390/rs2010290

Tao, W., Lei, Y., and Mooney, P (July, January 29). Dense Point Cloud Extraction from UAV Captured Images in Forest Area. Fuzhou, China.

Dandois, 2010, Remote sensing of vegetation structure using computer vision, Remote Sens, 2, 1157, 10.3390/rs2041157

Jaakkola, 2010, A low-costmulti-sensoral mobile mapping system and its feasibility for tree measurements, ISPRS J. Photogramm, 65, 514, 10.1016/j.isprsjprs.2010.08.002

Lin, 2011, Mini-UAV-borne LIDAR for fine-scale mapping, IEEE Geosci. Remote S, 8, 426, 10.1109/LGRS.2010.2079913

Choi, 2009, Developing a UAV-based rapid mapping system for emergency response, Proc. SPIE, 7332, 733209, 10.1117/12.818492

Nagai, 2009, UAV-borne 3-D mapping system by multisensor integration, IEEE T. Geosci. Remote, 47, 701, 10.1109/TGRS.2008.2010314

Miller, R., and Amidi, O (, January June). 3-D Site Mapping with the CMU Autonomous Helicopter 3-D Site Mapping with the CMU Autonomous Helicopter. Sapparo, Japan.

Glennie, 2007, Rigorous 3D error analysis of kinematic scanning LIDAR systems, J. Appl. Geodes, 1, 147, 10.1515/jag.2007.017

Schwarz, 2004, Mobile Mapping Systems State of the art and future trends, Int. Arch. Photogr. Remote Sens. Spat. Inf. Sci, 35, 10

El-sheimy, N (2009). Emerging MEMS IMU and Its Impact on Mapping Applications, Photogrammetric Week.

Wallace, L., Lucieer, A., Turner, D., and Watson, C (2011, January 16–20). Error assessment and mitigation for hyper-temporal UAV-borne LiDAR surveys of forest inventory. Hobart, Australia.

Shin, E (2004, January 26–29). An Unscented Kalman Filter for In-Motion Alignment of Low-Cost IMUs. Huntsville, AL, USA.

Chiang, 2006, The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments, IEEE T. Instrum. Meas, 55, 1606, 10.1109/TIM.2006.881033

Chiang, 2009, An artificial neural network embedded position and orientation determination algorithm for low cost MEMS INS/GPS integrated sensors, Sensors, 9, 2586, 10.3390/s90402586

Bryson, M., and Sukkarieh, S (2011, January 25–30). A Comparison of Feature and Pose-Based Mapping Using Vision, Inertial and GPS on a UAV. San Francisco, CA, USA.

Andersen, E., and Taylor, C (November, January 29). Improving MAV Pose Estimation Using Visual Information. San Diego, CA, USA.

Gajdamowicz, K., Öhman, D., and Horemuz, M. (2007, January 28–31). Mapping and 3D Modelling of Urban Environment Based on Lidar, Gps/Imu and Image Data. Padova, Italy.

Snavely, 2006, Photo tourism: exploring photo collections in 3D, ACM Trans. Graph, 25, 835, 10.1145/1141911.1141964

Morsdorf, 2006, Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction, Remote Sens. Environ, 104, 50, 10.1016/j.rse.2006.04.019

Zhang, 2010, Improved multi-position calibration for inertial measurement units, Meas. Sci. Technol, 21, 15107, 10.1088/0957-0233/21/1/015107

Bouget, J.Y. Available online: http://www.vision.caltech.edu/bouguetj/calib_doc/ (accessed on 12 January 2012).

Van Der Merwe, R., and Wan, E (2004, January 7–9). Sigma-Point Kalman Filters for Integrated Navigation. Proceedings of the 60th Annual Meeting of the Institute of Navigation (ION), Dayton, OH, USA.

Wan, E., and van der Merwe, R (2000, January 1–4). The Unscented Kalman Filter for Nonlinear Estimation. Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, Lake Louise, AB, Canada.

Crassidis, 2006, Sigma-point Kalman filtering for integrated GPS and inertial navigation, IEEE T. Aero. Elec. Sys, 42, 750, 10.1109/TAES.2006.1642588

Gavrilets, V (2003). [Autonomous Aerobatic Maneuvering of Miniature Helicopters]. PhD Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA.

Van Der Merwe, R. (2004). Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, PhD Thesis, Oregon Health and Science University, Portland, OR, USA.

Dellaert, F., Seitz, S.M., Thorpe, C.E., and Thrun, S (2000, January 13–15). Structure from Motion without Correspondence. Hilton Head, SC, USA.

Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis, 60, 91, 10.1023/B:VISI.0000029664.99615.94

2004, An efficient solution to the five-point relative pose problem, IEEE T. Pattern. Anal, 26, 756, 10.1109/TPAMI.2004.17

Hol, 2010, Modeling and calibration of inertial and vision sensors, Int. J. Rob. Res, 29, 231, 10.1177/0278364909356812

Isenberg, M Available online: http://www.cs.unc.edu/~isenburg/lastools/ (accessed on 15 December 2011).

Shrestha, 2012, Estimating biophysical parameters of individual trees in an urban environment using small footprint discrete-return imaging lidar, Remote Sens, 4, 484, 10.3390/rs4020484

Blanchard, 2011, Object-based image analysis of downed logs in disturbed forested landscapes using lidar, Remote Sens, 3, 2420, 10.3390/rs3112420

Adams, T (2011, January 16–20). Remotely Sensed Crown Structure as an Indicator of Wood Quality. A Comparison of Metrics from Aerial and Terrestrial Laser Scanning. Hobart, Australia.

Raber, 2007, Impact of lidar nominal post-spacing on DEM accuracy and flood zone delineation, Photogramm. Eng. Remote Sensing, 7, 793, 10.14358/PERS.73.7.793

Goulden, 2010, The forward propagation of integrated system component errors within airborne lidar data, Photogramm. Eng. Remote Sensing, 5, 589, 10.14358/PERS.76.5.589

Yu, 2010, Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes, Remote Sens, 2, 1481, 10.3390/rs2061481

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Remote Sensing

Tập 4 Số 6

1519-1543

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