Measuring vegetation height in linear disturbances in the boreal forest with UAV photogrammetry

Remote Sensing

Volumn 9, Issue 12, 2017, Pages

  Chen, Shijuan ^a,b   McDermid, Gregory J ^a   Castilla, Guillermo ^c   Linke, Julia ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b BOSTON UNIVERSITY   (United States)

^c NORTHERN FORESTRY CENTRE   (Canada)

Author keywords

3D; Accuracy assessment; Point clouds; Remote sensing; UAV; Vegetation

Indexed keywords

COST BENEFIT ANALYSIS; COST EFFECTIVENESS; COSTS; FORESTRY; GLOBAL POSITIONING SYSTEM; MEAN SQUARE ERROR; OPTICAL RADAR; PHOTOGRAMMETRY; REMOTE SENSING; SURVEYS; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

ACCURACY ASSESSMENT; GLOBAL NAVIGATION SATELLITE SYSTEMS; GROUND MEASUREMENTS; LIDAR (LIGHT DETECTION AND RANGING); POINT CLOUD; ROOT MEAN SQUARE ERRORS; SAMPLING STRATEGIES; VEGETATION RECOVERY;

AIRCRAFT DETECTION;

EID: 85038208516     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9121257     Document Type: Article

References (46)

1. Festa-Bianchet, M.; Ray, J.C.; Boutin, S.; Côté, S.D.; Gunn, A. Conservation of caribou (Rangifer tarandus) in Canada: An uncertain future. Can. J. Zool. 2011, 89, 419-434
2. Environment and Climate Change Canada. Report on the Progress of Recovery Strategy Implementation for the Woodland Caribou (Rangifer tarandus caribou), Boreal population in Canada for the Period 2012-2017. Species at Risk Act Recovery Strategy Series; Environment and Climate Change Canada: Ottawa, ON, Canada, 2017
3. Salmo Consulting Inc.; Athabasca Landscape Team. Athabasca Caribou Landscape Management Options Report; Alberta Caribou Committee Governance Board: Edmonton, AB, Canada, 2009
4. Ritchie, C.; George, P. Boreal Caribou Habitat Restoration; Government of British Columbia: Victoria, BC, Canada, 2012
5. Pyper, M.; Nishi, J.; McNeil, L. Linear Feature Restoration in Caribou Habitat: A Summary of Current Practices and a Roadmap for Future Programs; Fuse Consulting: Calgary, AB, Canada, 2014
6. Latham, A.D.M.; Latham, M.C.; Knopff, K.H.; Hebblewhite, M.; Boutin, S. Wolves, white-tailed deer, and beaver: Implications of seasonal prey switching for woodland caribou declines. Ecography 2013, 36, 1276-1290
7. Latham, A.D.M.; Latham, M.C.; Boyce, M.S.; Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 2011, 21, 2854-2865
8. Van Rensen, C.K.; Nielsen, S.E.; White, B.; Vinge, T.; Lieffers, V.J. Natural regeneration of forest vegetation on legacy seismic lines in boreal habitats in Alberta's oil sands region. Biol. Conserv. 2015, 184, 127-135
9. Dickie, M.; Serrouya, R.; Demars, C.; Cranston, J.; Boutin, S. Evaluating functional recovery of habitat for threatened woodland caribou. Ecosphere 2017, 9
10. Golder Associates Ltd. Caribou Habitat Restoration Pilot Study. Submitted to: ConocoPhillips Canada, Suncor Energy, and the Canadian Association of Petroleum Producers; Golder Associates Ltd.: Hong Kong, China, 2009
11. Alberta Government. Alberta Timber Harvest Planning and Operating Ground Rules Framework for Renewal; Government of Alberta: Edmonton, AB, Canada, 2016
12. Magnussen, S.; Russo, G. Uncertainty in photo-interpreted forest inventory variables and effects on estimates of error in Canada's National Forest Inventory. For. Chron. 2012, 88, 439-447
13. Lefsky, M.A. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett. 2010, 37
14. McRoberts, R.E.; Cohen, W.B.; Næsset, E.; Stehman, S.V.; Tomppo, E.O. Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand. J. For. Res. 2010, 25, 340-367
15. Weber, T.C.; Boss, D.E. Use of LiDAR and supplemental data to estimate forest maturity in Charles County, MD, USA. For. Ecol. Manag. 2009, 258, 2068-2075
16. Persson, H.J.; Perko, R. Assessment of boreal forest height from WorldView-2 satellite stereo images. Remote Sens. Lett. 2016, 7, 1150-1159
17. Pan, Y.; Birdsey, R.A.; Phillips, O.L.; Jackson, R.B. The structure, distribution, and biomass of the world's forests. Annu. Rev. Ecol. Evol. Syst. 2013, 44, 593-622
18. Bouvier, M.; Durrieu, S.; Fournier, R.A.; Renaud, J.-P. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sens. Environ. 2015, 156, 322-334
19. Breidenbach, J.; Nothdurft, A.; Kändler, G. Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. Eur. J. For. Res. 2010, 129, 833-846
20. Estornell, J.; Ruiz, L.A.; Velázquez-Martí, B.; Fernández-Sarría, A. Estimation of shrub biomass by airborne LiDAR data in small forest stands. For. Ecol. Manag. 2011, 262, 1697-1703
21. Vastaranta, M.; Holopainen, M.; Yu, X.; Hyyppä, J.; Hyyppä, H.; Viitala, R. Predicting stand-thinning maturity from airborne laser scanning data. Scand. J. For. Res. 2011, 26, 187-196
22. Whitehead, K.; Hugenholtz, C.H.; Myshak, S.; Brown, O.; LeClair, A.; Tamminga, A.; Barchyn, T.E.; Moorman, B.; Eaton, B. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications 1. J. Unmanned Veh. Syst. 2014, 2, 86-102
23. 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
24. Hardin, P.J.; Jensen, R.R. Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities. GISci. Remote Sens. 2011, 48, 99-111
25. Dandois, J.P.; Ellis, E.C. Remote sensing of vegetation structure using computer vision. Remote Sens. 2010, 2, 1157-1176
26. Järnstedt, J.; Pekkarinen, A.; Tuominen, S.; Ginzler, C.; Holopainen, M.; Viitala, R. Forest variable estimation using a high-resolution digital surface model. ISPRS J. Photogramm. Remote Sens. 2012, 74, 78-84
27. 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
28. White, J.C.; Wulder, M.A.; Vastaranta, M.; Coops, N.C.; Pitt, D.; Woods, M. The utility of image-based point clouds for forest inventory: A comparison with airborne laser scanning. Forests 2013, 4, 518-536
29. 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
30. Whitehead, K.; Hugenholtz, C.H.; Myshak, S.; Brown, O.; LeClair, A.; Tamminga, A.; Barchyn, T.E.; Moorman, B.; Eaton, B. 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, 86-102
31. Vastaranta, M.; Wulder, M.A.; White, J.C.; Pekkarinen, A.; Tuominen, S.; Ginzler, C.; Kankare, V.; Holopainen, M.; Hyyppä, J.; Hyyppä, H. Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update. Can. J. Remote Sens. 2013, 39, 382-395
32. Natural Regions Committee. Natural Regions and Subregions of Alberta; Downing, D.J.; Pettapiece, W.W. (Compiler) Publ. No. T/852; Government of Alberta: Edmonton, AB, Canada, 2006
33. Donahue, B.; Wentzel, J.; Berg, R. Guidelines for RTK/RTN GNSS Surveying in Canada; Version 1.1; Natural Resources Canada: Ottawa, ON, Canada, 2013
34. Hird, J.N.; Nielsen, S.; McDermid, G.; Tan, T. BERA Project: Baseline Field Data Collection Protocols, Version 2016-07-15; Boreal Ecosystem Recovery and Assessment (BERA): Calgary, AB, Canada, 2016
35. Agisoft. Agisoft Photoscan User Manual. Available online: http://www.agisoft.com/pdf/photoscan-pro_ 1_2_en.pdf (accessed on 2 December 2017)
36. Schenk, T. Section 6.2: Digital phogrammetric workstations. In Introduction to Photogrammetry; Ohio State University, Department of Civil and Environmental Enginering and Geodetic Science: Columbus, OH, USA, 2005; pp. 79-95
37. 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
38. Triggs, B.; McLauchlan, P.F.; Hartley, R.I.; Fitzgibbon, A.W. Bundle Adjustment-A Modern Synthesis. In Vision Algorithms: Theory and Practice, Proceedings of the International Workshop on Vision Algorithms, Corfu, Greece, 21-22 Septem 2000; Lecture Notes in Computer Science; Triggs, B., Zisserman, A., Szeliski, R., Eds.; Springer: Berlin/Heidelberg, Germany, 2000; Volume 1883
39. Isenburg, M. LAStools-Efficient Tools for LiDAR Processing. 2013. Available online: http://lastools.org (accessed on 2 December 2017)
40. AltaLIS. LiDAR15 DEM. Available online: http://www.altalis.com/products/terrain/lidar15_dem.html (accessed on 2 December 2017)
41. Hird, J.N.; Montaghi, A.; McDermid, G.J.; Kariyeva, J.; Moorman, B.J.; Nielsen, S.E.; McIntosh, A.C.S. Use of unmanned aerial vehicles for monitoring recovery of forest vegetation on petroleum well sites. Remote Sens. 2017, 9
42. White, J.C.; Stepper, C.; Tompalski, P.; Coops, N.C.;Wulder, M.A. Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests 2015, 6, 3704-3732
43. Maltamo, M.; Bollandsås, O.; Næsset, E.; Gobakken, T.; Packalén, P.; Bollandsås, O.M.; Næsset, E.; Gobakken, T.; Packalén, P. Different plot selection strategies for field training data in ALS-assisted forest inventory. Forestry 2011, 84, 23-31
44. Jensen, J.L.R.; Mathews, A.J. Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sens. 2016, 8
45. 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
46. 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