Optimal altitude, overlap, and weather conditions for computer vision uav estimates of forest structure

Remote Sensing

Volumn 7, Issue 10, 2015, Pages 13895-13920

  Dandois, Jonathan P ^a,b   Olano, Marc ^c   Ellis, Erle C ^a  

^a UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^b SMITHSONIAN TROPICAL RESEARCH INSTITUTE   (Panama)

^c University of Marylandw Baltimore County   (United States)

Author keywords

Canopy height; Computer vision; Ecosynth; Optimal; Point cloud; SFM; UAV

Indexed keywords

ANTENNAS; FORESTRY; LIGHTING; OPTICAL DATA PROCESSING; OPTICAL RADAR; REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

CANOPY HEIGHTS; DIGITAL TERRAIN MODEL; ECOSYNTH; OPTIMAL; POINT CLOUD; STRUCTURE FROM MOTION ALGORITHM; TEMPERATE DECIDUOUS FOREST; THREEDIMENSIONAL (3-D);

COMPUTER VISION;

EID: 84945956589     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71013895     Document Type: Article

References (81)

1. Lindquist, E.J.; D'annunzio, R.; Gerrand, A.; Macdicken, K.; Achard, F.; Beuchle, R.; Brink, A.; Eva, H.D.; Mayaux, P.; San-Miguel-Ayanz, J.; Stibig, H.-J. Global Forest Land-Use Change 1990-2005; FAO & JRC: Rome, Italy, 2012.
2. Houghton, R.A.; Hall, F.; Goetz, S.J. Importance of biomass in the global carbon cycle. J Geophys. Res. 2009, 114, 1-13.
3. Asner, G.P.; Martin, R.E. Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 2009, 7, 269-276.
4. Defries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, T.R. A new global 1-km dataset of percentage tree cover derived from remote sensing. Glob. Change Biol. 2000, 6, 247-254.
5. Hansen, M.C.; Roy, D.P.; Lindquist, E.; Adusei, B.; Justice, C.O.; Altstatt, A. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens. Environ. 2008, 112, 2495-2513.
6. Lefsky, M.A.; Cohen, W.B.; Parker, G.G.; Harding, D.J. LIDAR remote sensing for ecosystem studies. Bioscience 2002, 52, 20-30.
7. Parker, G.G.; Russ, M.E. The canopy surface and stand development: Assessing forest canopy structure and complexity with near-surface altimetry. For. Ecol. Manage. 2004, 189, 307-315.
8. Richardson, A.D.; Braswell, B.; Hollinger, D.Y.; Jenkins, J.C.; Ollinger, S.V. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 2009, 19, 1417-1428.
9. Zhang, X.; Friedl, M.; Schaaf, C.; Strahler, A.; Hodges, J.; Gao, F.; Reed, B.; Huete, A. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471-475.
10. Lefsky, M.; Cohen, W. SELECTION of remotely sensed data. In Remote Sensing of Forest Environments; Wulder, M., Franklin, S., Eds.; Kluwer Academic Publishers: Boston, MA, USA, 2003; pp. 13-46.
11. Beuchle, R.; Eva, H.D.; Stibig, H.-J.; Bodart, C.; Brink, A.; Mayaux, P.; Johansson, D.; Achard, F.; Belward, A. A satellite data set for tropical forest area change assessment. Int. J. Remote Sens. 2011, 32, 7009-7031.
12. Anderson, K.; Gaston, K.J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 2013, 11, 138-146.
13. Turner, W. Sensing biodiversity. Science 2014, 346, 301-302.
14. 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.
15. Rango, A.; Laliberte, A.; Herrick, J.E.; Winters, C.; Havstad, K.; Steele, C.; Browning, D. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. J. Appl. Remote Sens. 2009, 3, doi:10.1117/1.3216822.
16. 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.
17. Getzin, S.; Wiegand, K.; Schöning, I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 2012, 3, 397-404.
18. Koh, L.P.; Wich, S.A. Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 2012, 5, 121-132.
19. Hunt, J.; Raymond, E.; Hively, W.D.; Fujikawa, S.; Linden, D.; Daughtry, C.S.; McCarty, G. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2010, 2, 290-305.
20. Snavely, N.; Seitz, S.; Szeliski, R. Modeling the world from internet photo collections. Int. J. Comp. Vis. 2008, 80, 189-210.
21. 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.
22. Dandois, J.P.; Ellis, E.C. Remote sensing of vegetation structure using computer vision. Remote Sens. 2010, 2, 1157-1176.
23. Puliti, S.; Ørka, H.; Gobakken, T.; Næsset, E. Inventory of small forest areas using an unmanned aerial system. Remote Sens. 2015, 7, 9632-9654.
24. Castillo, C.; Pérez, R.; James, M.R.; Quinton, J.N.; Taguas, E.V.; Gómez, J.A. Comparing the accuracy of several field methods for measuring gully erosion. Soil Sci. Soc. Am. J. 2012, 76, 1319-1332.
25. Javernick, L.; Brasington, J.; Caruso, B. Modeling the topography of shallow braided rivers using structure-from-motion photogrammetry. Geomorphology 2014, 213, 166-182.
26. Dey, D.; Mummert, L.; Sukthankar, R. Classification of plant structures from uncalibrated image sequences. In Proceedings of the 2012 IEEE Workshop on Applications of Computer Vision WACV, Breckenridge, CO, USA, 9-11 January 2012; pp. 329-336.
27. Mathews, A.; Jensen, J. Visualizing and quantifying vineyard canopy lai using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens. 2013, 5, 2164-2183.
28. Torres-Sánchez, J.; López-Granados, F.; Serrano, N.; Arquero, O.; Peña, J.M. High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE 2015, 10, e0130479.
29. de Matías, J.; Sanjosé, J.J.D.; López-Nicolás, G.; Sagüés, C.; Guerrero, J.J. Photogrammetric methodology for the production of geomorphologic maps: Application to the veleta rock glacier (sierra nevada, granada, spain). Remote Sens. 2009, 1, 829-841.
30. Harwin, S.; Lucieer, A. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sens. 2012, 4, 1573-1599.
31. James, M.; Applegarth, L.J.; Pinkerton, H. Lava channel roofing, overflows, breaches and switching: Insights from the 2008-2009 eruption of mt. Etna. Bull. Volcanol 2012, 74, 107-117.
32. James, M.; Robson, S. Straightforward reconstruction of 3d surfaces and topography with a camera: Accuracy and geoscience application. J Geophys. Res.: Earth Surf. 2012, 117, 1-23.
33. Rosnell, T.; Honkavaara, E. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors 2012, 12, 453-480.
34. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. 'Structure-from-motion' photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012, 179, 300-314.
35. Næsset, E. Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sens. Environ. 2009, 113, 148-159.
36. Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, R.A. National-scale biomass estimators for united states tree species. For. Sci. 2003, 49, 12-35.
37. Dandois, J.P.; Boswell, D.; Anderson, E.; Bofto, A.; Baker, M.; Ellis, E.C. Forest census and map data for two temperate deciduous forest edge woodlot patches in Baltimore MD, USA. Ecology 2015, 96, 1734.
38. McMahon, S.M.; Parker, G.G.; Miller, D.R. Evidence for a recent increase in forest growth. Proc. Natl. Acad. Sci. 2010, 107, 3611-3615.
39. ForestGeo. Available online: http://www.forestgeo.si.edu (accessed on 25 July 2015).
40. Mikrokopter. Available online: http://www.mikrokopter.de (accessed on 9 September 2015).
41. Arducopter. Available online: http://copter.ardupilot.com (accessed on 9 September 2015).
42. Ecosynth Wiki. Available online: http://wiki.ecosynth.org (accessed on 9 September 2015).
43. Runyan, C. Methodology for Installation of Eddy Covariance Meteorological Sensors and Data Processing; UMBC/CUERE Technical Memo 2009/002: Baltimore, MD, USA, 2009.
44. Beaufort Wind Force Scale. Available online: http://www.spc.noaa.gov/faq/tornado/beaufort.html (accessed on 20 May 2014).
45. Habib, A.; Bang, K.I.; Kersting, A.P.; Lee, D.-C. Error budget of LIDAR systems and quality control of the derived data. Photogramm. Eng. Remote Sens. 2009, 75, 1093-1108.
46. Ecosynther v1.0. Available online: https://bitbucket.org/ecosynth/ecosynther (accessed on 9 September 2015).
47. Furukawa, Y.; Ponce, J. Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 1362-1376.
48. Wang, Y.; Olano, M. A framework for GPS 3D model reconstruction using structure-from-motion. In Proceedings of ACM SIGGRAPH' 11, Vancouver, BC, Canada, 7-11 August 2011.
49. Wu, C. SIFTGPU: A GPU implementation of scale invariant feature transform (SIFT). Available online: http://www.cs.unc.edu/~ccwu/siftgpu/(accessed on 27 July 2015).
50. Szeliski, R. Computer Vision; Algorithms and Applications, Texts in Computer Science, Springer-Verlag: London, UK, 2011.
51. Li, J.; Allinson, N.M. A comprehensive review of current local features for computer vision. Neurocomputing 2008, 71, 1771-1787.
52. Mikolajczyk, K.; Schmid, C. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27, 1615-1630.
53. Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 2004, 60, 91-110.
54. Ecosynth Aerial. Available online: http://code.ecosynth.org/EcosynthAerial (accessed on 9 September 2015).
55. Besl, P.J.; McKay, H.D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 14, 239-256.
56. Asner, G.P.; Mascaro, J. Mapping tropical forest carbon: Calibrating plot estimates to a simple LIDAR metric. Remote Sens. Environ. 2014, 140, 614-624.
57. Baltsavias, E.; Pateraki, M.; Zhang, L. Radiometric and geometric evaluation of IKONOS GEO images and their use for 3D building modelling. Proceedings of the Joint ISPRS Workshop "High Resolution Mapping from Space 2001", Hanover, Germany, 19-21 September 2001; pp. 19-21.
58. Yang, Y.; Newsam, S. Geographic image retrieval using local invariant features. IEEE Trans. Geosci. Remote Sens. 2013, 51, 818-832.
59. Zahawi, R.A.; Dandois, J.P.; Holl, K.D.; Nadwodny, D.; Reid, J.L.; Ellis, E.C. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol. Conserv. 2015, 186, 287-295.
60. Ni, W.; Ranson, K.J.; Zhang, Z.; Sun, G. Features of point clouds synthesized from multi-view ALOS/PRISM data and comparisons with LIDAR data in forested areas. Remote Sens. Environ. 2014, 149, 47-57.
61. Ogunjemiyo, S.; Parker, G.; Roberts, D. Reflections in bumpy terrain: Implications of canopy surface variations for the radiation balance of vegetation. IEEE Geosci. Remote Sens. Lett. 2005, 2, 90-93.
62. Sithole, G.; Vosselman, G. Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds. ISPRS J. Photogramm. Remote Sens. 2004, 59, 85-101.
63. St. Onge, B.; Hu, Y.; Vega, C. Mapping the height and above-ground biomass of a mixed forest using LIDAR and stereo IKONOS images. Int. J. Remote Sens. 2008, 29, 1277-1294.
64. Tinkham, W.T.; Huang, H.; Smith, A.M.S.; Shrestha, R.; Falkowski, M.J.; Hudak, A.T.; Link, T.E.; Glenn, N.F.; Marks, D.G. A comparison of two open source LIDAR surface classification algorithms. Remote Sens. 2011, 3, 638-649.
65. Wasser, L.; Day, R.; Chasmer, L.; Taylor, A. Influence of vegetation structure on LIDAR-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions. PLoS ONE 2013, 8, e54776.
66. Chasmer, L.; Hopkinson, C.; Smith, B.; Treitz, P. Examining the influence of changing laser pulse repetition frequencies on conifer forest canopy returns. Photogramm. Eng. Remote Sens. 2006, 72, 1359-1367.
67. Hirschmugl, M.; Ofner, M.; Raggam, J.; Schardt, M. Single tree detection in very high resolution remote sensing data. Remote Sens. Environ. 2007, 110, 533-544.
68. Ofner, M.; Hirschmugl, M.; Raggam, H.; Schardt, M. 3D stereo mapping by means of UltracamD data. In Proceedings of the Workshop on 3D Remote Sensing in Forestry, Vienna, Austria, 14-15 February 2006.
69. Cox, S.E.; Booth, D.T. Shadow attenuation with high dynamic range images. Environ. Monit. Assess. 2009, 158, 231-241.
70. Bragg, D. An improved tree height measurement technique tested on mature southern pines. South. J. Appl. For. 2008, 32, 38-43.
71. Goetz, S.; Dubayah, R. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Manag. 2011, 2, 231-244.
72. 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.
73. Frazer, G.W.; Magnussen, S.; Wulder, M.A.; Niemann, K.O. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LIDAR-derived estimates of forest stand biomass. Remote Sens. Environ. 2011, 115, 636-649.
74. Swiftnav Piksi RTK-GPS. Available online: http://www.swiftnav.com/piksi.html (accessed on 9 September 2015).
75. Richards, J.A. Remote Sensing Digital Image Analysis; Springer: Berlin, Germany, 2006; p. 437.
76. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 2013, 5, 5006-5039.
77. Berni, J.; Zarco-Tejada, P.J.; Su˙rez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722-738.
78. Zarco-Tejada, P.J.; González-Dugo, V.; Berni, J.A.J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 2012, 117, 322-337.
79. Beijborn, O.; Edmunds, P.J.; Kline, D.I.; Mitchell, B.G.; Kriegman, D. Automated annotation of coral reef survey images. In Proceedings of Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 16-21 June 2012; pp. 1170-1177.
80. Nilsback, M.-E. An Automatic Visual Flora-Segmentation and Classication of Flower Images. Ph.D. Thesis, University of Oxford, Oxford, UK, 2009.
81. Kendal, D.; Hauser, C.E.; Garrard, G.E.; Jellinek, S.; Giljohann, K.M.; Moore, J.L. Quantifying plant colour and colour difference as perceived by humans using digital images. PLoS ONE 2013, 8, e72296.