Augmentation of traditional forest inventory and Airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring

Remote Sensing

Volumn 10, Issue 10, 2018, Pages

  Fankhauser, Kathryn E ^a   Strigul, Nikolay S ^a   Gatziolis, Demetrios ^b  

^a WASHINGTON STATE UNIVERSITY   (United States)

^b PACIFIC NORTHWEST RESEARCH STATION   (United States)

Author keywords

Forest 3D models; Oblique imagery; Point cloud; Remote sensing; Structure from motion (SfM); Unmanned aerial system (UAS)

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; DATA HANDLING; LASER APPLICATIONS; PHOTOGRAMMETRY; REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

3D MODELS; OBLIQUE IMAGERY; POINT CLOUD; STRUCTURE FROM MOTION; UNMANNED AERIAL SYSTEMS;

FORESTRY;

EID: 85055445077     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10101562     Document Type: Article

References (62)

1. UN Food and Agriculture Organization. FRA 2015 Pprocess Document; United Nations: New York, NY, USA, 2016
2. USDA Forest Service. Forest Inventory and Analysis National Core Field Guide, Volume 1: Field Data Collection Procedures for Phase 2 Plots; version 7.2; USDA Forest Service: Washington, DC, USA, 2017
3. Mlambo, R.; Woodhouse, I.H.; Gerard, F.; Anderson, K. Structure from motion (SfM) photogrammetry with drone data: A low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests 2017, 8
4. United Nations. UN-REDD Programme Strategy 20112015; United Nations: New York, NY, USA, 2011
5. Goodbody, T.R.H.; Coops, N.C.; Marshall, P.L.; Tompalski, P.; Crawford, P. Unmanned aerial systems for precision forest inventory purposes: A review and case study. For. Chron. 2017, 93, 71-81
6. Næsset, E. Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 2007, 22, 433-442
7. Anderson, K.; Gaston, K.J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 2013, 11, 138-146
8. Hummel, S.; Hudak, A.T.; Uebler, E.H.; Falkowski, M.J.; Megown, K.A. A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National Forest. J. For. 2011, 267-273
9. Snavely, N.; Seitz, S.M.; Szeliski, R. Modeling the World from Internet Photo Collections. Int. J. Comput. Vis. 2008, 80, 189-210
10. Probst, A.; Gatziolis, D.; Liénard, J.F.; Strigul, N. Intercomparison of photogrammetry software for three-dimensional vegetation modelling. R. Soc. Open Sci. 2018, 5
11. Wallace, L.; Lucieer, A.; Malenovský, Z.; Turner, D.; Vopěnka, P. Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 2016, 7
12. Wallace, L.; Lucieer, A.; Watson, C.S. Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7619-7628
13. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97
14. Oborne, M.Mission Planner v.1.3.45. Available online: www.ardupilot.org/planner (accessed on 6 August 2018)
15. DroidPlanner Labs. Tower v.1.4.0.1 Beta 1. Available online: www.play.google.com(accessed on 6 August 2018)
16. Agisoft, L.L.C. Photoscan Professional Edition v.1.4.2. Available online: www.agisoft.com (accessed on 6 August 2018)
17. McGaughey, R.J. FUSION/LIDAR Data Viewer and LIDAR Toolkit v.3.6. Available online: www.forsys.cfr. washington.edu/fusion/fusion_overview.html (accessed on 6 August 2018)
18. Tang, L.; Shao, G. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791-797
19. Messinger, M.; Asner, G.P.; Silman, M. Rapid assessments of Amazon forest structure and biomass using small unmanned aerial systems. Remote Sens. 2016, 8
20. Dandois, J.P.; Ellis, E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 2013, 136, 259-276
21. Gatziolis, D.; Liénard, J.F.; Vogs, A.; Strigul, N.S. 3D tree dimensionality assessment using photogrammetry and small unmanned aerial vehicles. PLoS ONE 2015, 10
22. Lisein, J.; Pierrot-Deseilligny, M.; Bonnet, S.; Lejeune, P. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 2013, 4, 922-944
23. Puliti, S.; Ørka, H.O.; Gobakken, T.; Næsset, E. Inventory of small forest areas using an unmanned aerial system. Remote Sens. 2015, 7, 9632-9654
24. Gougeon, F.A. A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images. Can. J. Remote Sens. 1995, 21, 274-284
25. Wulder, M.; Niemann, K.O.; Goodenough, D.G. Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery. Remote Sens. Environ. 2000, 73, 103-114
26. Alonzo, M.; Andersen, H.-E.; Morton, D.C.; Cook, B.D. Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion. Forests 2018, 9
27. Dalponte, M.; Frizzera, L.; Ørka, H.O.; Gobakken, T.; Næsset, E.; Gianelle, D. Predicting stem diameters and aboveground biomass of individual trees using remote sensing data. Ecol. Indic. 2018, 85, 367-376
28. Jeronimo, S.M.A.; Kane, V.R.; Churchill, D.J.; McGaughey, R.J.; Franklin, J.F. Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes. J. For. 2018, 116, 336-346
29. Mohan, M.; Silva, C.A.; Klauberg, C.; Jat, P.; Catts, G.; Cardil, A.; Hudak, A.T.; Dia, M. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests 2017, 8
30. Malambo, L.; Popescu, S.C.; Murray, S.C.; Putman, E.; Pugh, N.A.; Horne, D.W.; Richardson, G.; Sheridan, R.; Rooney, W.L.; Avant, R.; et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int. J. Appl. Earth Obs. Geoinf. 2018, 64, 31-42
31. Dempewolf, J.; Nagol, J.; Hein, S.; Thiel, C.; Zimmermann, R. Measurement of within-season tree height growth in a mixed forest stand using UAV imagery. Forests 2017, 8
32. Hawbaker, T.J.; Keuler, N.S.; Lesak, A.A.; Gobakken, T.; Contrucci, K.; Radeloff, V.C. Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. J. Geophys. Res. Biogeosci. 2009, 114
33. Woodall, C.; Williams, M.S. Sampling Protocol, Estimation, and Analysis Procedures for the down Woody Materials Indicator of the FIA Program; Gen. Tech. Rep. NC-256; USDA Forest Service, North Central Research Station: St. Paul, MN, USA, 2005
34. McGaughey, R.J.; Ahmed, K.; Andersen, H.-E.; Reutebuch, S.E. Effect of Occupation Time on the Horizontal Accuracy of a Mapping-Grade GNSS Receiver under Dense Forest Canopy. Photogramm. Eng. Remote Sens. 2017, 83, 861-868
35. Fritz, A.; Kattenborn, T.; Koch, B. UAV-based photogrammetric point clouds: Tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Rostock, Germany, 21-24 May 2013; Volume XL-1/W2, pp. 141-146
36. James, M.R.; Robson, S. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf. Process. Landf. 2014, 39, 1413-1420
37. Whitehead, K.; Hugenholtz, C.H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Veh. Syst. 2014, 2, 69-85
38. Triggs, B.; McLauchlan, P.F.; Hartley, R.I.; Fitzgibbon, A.W. Bundle Adjustment a Modern Synthesis. In Vision Algorithms: Theory and Practice; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2000; Volume 1883, pp. 298-372
39. McGaughey, R.J. FUSION/LDV: Software for LIDAR Data Analysis and Visualization; USDA Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 2016; Available online: http://forsys.cfr.washington. edu/FUSION/fusion_overview.html (accessed on 22 September 2018)
40. Chen, Q.; Baldocchi, D.; Gong, P.; Kelly, M. Isolating individual trees in a savanna woodland using small footprint Lidar data. Photogramm. Eng. Remote Sens. 2006, 72, 923-932
41. Monnet, J.-M.; Mermin, E.; Chanussot, J.; Berger, F. Tree top detection using local maxima filtering: A parameter sensitivity analysis. In Proceedings of the 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems Silvilaser, Freiburg, Germany, 14-17 September 2010
42. Keyser, C.Westside Cascades (WC) Variant Overview Forest Vegetation Simulator; U.S. Department of Agriculture, Forest Service, Forest Management Service Center: Fort Collins, CO, USA, 2008
43. Besl, P.; McKay, N.D. A Method for Registration of 3-D Shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 14, 239-256
44. CloudCompare, Version 2.6.1. GPL Software. 2015. Available online: http://www.cloudcompare.org (accessed on 22 September 2018)
45. St-Onge, B.A.; Achaichia, N. Measuring forest canopy height using a combination of lidar and aerial photography data. In Proceedings of the International Archives of Photogrammetry and Remote Sensing, Annapolis, MD, USA, 22-24 October 2001; Volume XXXIV-3/W4, pp. 131-137
46. Baltsavias, E.; Gruen, A.; Eisenbeiss, H.; Zhang, L.; Waser, L.T. High-quality image matching and automated generation of 3D tree models. Int. J. Remote Sens. 2008, 29, 1243-1259
47. Larjavaara, M.; Muller-Landau, H.C. Measuring tree height: A quantitative comparison of two common field methods in a moist tropical forest. Methods Ecol. Evol. 2013, 4, 793-801
48. Chen, S.; Yuan, X.; Yuan, W.; Cai, Y. Poor textural image matching based on graph theory. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 12-19 July 2016; Volume XLI-B3, pp. 741-747
49. Seely, H.E. Computing tree heights from shadows in aerial photographs. For. Chron. 1929, 5, 24-27
50. Carrivick, J.L.; Smith, M.W.; Quincey, D.J. Structure from Motion in Geosciences. Wiley-Blackwell: Oxford, UK, 2016; p. 208
51. Verma, N.K.; Lamb, D.W. The use of shadows in high spatial resolution, remotely sensed, imagery to estimate the height of individual Eucalyptus trees on undulating land. Rangel. J. 2015, 37, 467-476
52. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89-99
53. Turner, D.; Lucieer, A.; Watson, C. An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sens. 2012, 4, 1392-1410
54. Thiel, C.; Schmullius, C. Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective. Int. J. Remote Sens. 2016, 38, 1-16
55. Sankey, T.; Donager, J.; McVay, J.; Sankey, J.B. UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 2017, 195, 30-43
56. Gatziolis, D.; Fried, J.S.; Monleon, V.S. Challenges to estimating tree height via LiDAR in closed-canopy forest: A parable from western Oregon. For. Sci. 2010, 56, 139-155
57. Seidel, K.W. A Ponderosa Pine-Lodgepole Pine Spacing Study in Central Oregon: Results after 20 Years; USDA Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 1989
58. Lowery, D.P. Ponderosa Pine; An American Wood; USDA Forest Service: Washington, DC, USA, 1984
59. USDA NRCS Plant Materials Program. Plant Fact Sheet: Ponderosa Pine; USDA Natural Resources Conservation Science: Washington, DC, USA, 2002
60. Zhang, J.; Hu, J.; Lian, J.; Fan, Z.; Ouyang, X.; Ye, W. Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biol. Conserv. 2016, 198, 60-69
61. DOGAMI (Oregon Department of Geology and Mineral Industries). Collecting LiDAR. Available online: http://www.oregongeology.org/lidar/collectinglidar.htm (accessed on 17 June 2018)
62. U.S. Geological Survey the National Map: 3D Elevation Program (3DEP). Available online: https: //nationalmap.gov/3DEP/ (accessed on 5 August 2018)