Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning

Forests

Volumn 6, Issue 11, 2015, Pages 4146-4167

  Leiterer, Reik ^a   Torabzadeh, Hossein ^a   Furrer, Reinhard ^a   Schaepman, Michael E ^a   Morsdorf, Felix ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

Area based approach; Forest inventory; LiDAR; Multi scale; Operational; Point cloud; Remote sensing; Vegetation structure

Indexed keywords

LASER APPLICATIONS; OPTICAL RADAR; REMOTE SENSING; SURFACE ANALYSIS;

AREA-BASED; FOREST INVENTORY; MULTI-SCALE; OPERATIONAL; POINT CLOUD; VEGETATION STRUCTURE;

FORESTRY;

CANOPY ARCHITECTURE; FOREST CANOPY; FOREST INVENTORY; LIDAR; REMOTE SENSING; TEMPERATE FOREST; VEGETATION STRUCTURE;

EID: 84949489149     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f6114146     Document Type: Article

References (80)

1. De Groot, R.S.; Wilson, M.A.; Boumans, R.M.J. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol. Econ. 2002, 41, 393-408.
2. Jonsson, M.; Wardle, D.A. Structural equation modelling reveals plant-community drivers of carbon storage in boreal forest ecosystems. Biol. Lett. 2010, 6, 116-119.
3. Sierra, C.A.; Loescher, H.W.; Harmon, M.E.; Richardson, A.D.; Hollinger, D.Y.; Perakis, S.S. Interannual variation of carbon fluxes from three contrasting evergreen forests: The role of forest dynamics and climate. Ecology 2009, 90, 2711-2723.
4. Bonan, G.B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 2008, 320, 1444-1449.
5. Purves, D.; Pacala, S. Predictive models of forest dynamics. Science 2008, 320, 1452-1453.
6. Nadkarni, N.M.; McIntosh, A.C.S.; Cushing, J.B. A framework to categorize forest structure concepts. For. Ecol. Manag. 2008, 256, 872-882.
7. Disney, M.; Lewis, P.; Saich, P. 3D modeling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sens. Environ. 2006, 100, 114-132.
8. Kayes, L.J.; Tinker, D.B. Forest structure and regeneration following a mountain pine beetle epidemic in southeastern Wyoming. For. Ecol. Manag. 2012, 263, 57-66.
9. Shugart, H.H.; Saatchi, S.; Hall, F.G. Importance of structure and its measurement in quantifying function of forest ecosystems. J. Geophys. Res. 2010, 115, 1-16.
10. Graf, R.; Mathys, L.; Bollmann, K. Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. For. Ecol. Manag. 2009, 257, 160-167.
11. Lindenmayer, D.B.; Franklin, J.F.; Fischer, J. General management principles and a checklist of strategies to guide forest biodiversity conservation. Biol. Conserv. 2006, 131, 433-445.
12. Herrera, J.M.; García, D. Effects of forest fragmentation on seed dispersal and seedling establishment in ornithochorous trees. Conserv. Biol. 2010, 24, 1089-1098.
13. Spies, T.A. Forest structure: A key to the ecosystem. Northwest Sci. 1998, 72, 34-36.
14. McElhinny, C.; Gibbons, P.; Brack, C.; Bauhus, J. Forest and woodland stand structural complexity: Its definition and measurement. For. Ecol. Manag. 2005, 218, 1-24.
15. Dial, R.; Bloodworh, B.; Lee, A.; Boyne, P.; Heys, J. The distribution of free space and its relation to canopy composition at six forest sites. For. Sci. 2004, 50, 312-325.
16. O'Hara, K.; Nagel, L. A functional comparison of productivity in even-aged and multiaged stands: A synthesis of Pinus ponderosa. For. Sci. 2006, 52, 290-303.
17. Everett, R.; Baumgartner, D.; Ohlson, P.; Schellhaas, R. Defining and quantifying canopy strata. Northwest Sci. 2008, 82, 48-64.
18. Parker, G.G.; Brown, M.J. Forest canopy stratification-is it useful? Am. Nat. 2000, 155, 473-484.
19. Franklin, J.F.; Spies. T.A. Composition, function, and structure of old-growth Douglas-fir forests. Wildlife and vegetation of unmanaged Douglas-fir forests. In USDA Forest Service General; Technical Report PNW-GTR-285; Pacific Northwest Research Station: Portland, OR, USA, 1991; pp. 71-81.
20. Foody, G.M. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens. Environ. 2010, 114, 2271-2285.
21. Haara, A.; Leskinen, P. The assessment of the uncertainty of updated stand-level inventory data. Silva Fenn. 2009, 43, 87-112.
22. Jones, T.G.; Coops, N.C.; Sharma, T. Assessing the utility of LiDAR to differentiate among vegetation structural classes. Remote Sens. Lett. 2012, 3, 231-238.
23. Hall, F.G.; Bergen, K.; Blair, J.B.; Dubayah, R.; Houghton, R.; Hurtt, G.; Kellndorfer, J.; Lefsky, M.; Ranson, J.; Saatchi, S.; et al. Characterizing 3D vegetation structure from space: Mission requirements. Remote Sens. Environ. 2011, 115, 2753-2775.
24. Roberts, J.; Tesfamichael, S.; Gebreslasie, M.; van Aardt, J.; Ahmed, F. Forest structural assessment using remote sensing technologies: An overview of the current state of the art. South. Hemisphere For. J. 2007, 69, 183-203.
25. Wulder, M.A.; Coops, N.C.; Hudak, A.T.; Morsdorf, F.; Nelson, R.; Newnham, G.; Vastaranta, M. Status and prospects for LiDAR remote sensing of forested ecosystems. Can. J. Remote Sens. 2013, 39, S1-S5.
26. Leiterer, R.; Mücke, W.; Morsdorf, F.; Hollaus, M.; Pfeifer, N.; Schaepman, M.E. Operational forest structure monitoring using airborne laser scanning (Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring). Photogramm. Fernerkund. Geoinf. 2013, 3, 173-184.
27. Van Leeuwen, M.; Nieuwenhuis, M. Retrieval of forest structural parameters using LiDAR remote sensing. Eur. J. For. Res. 2010, 129, 749-770.
28. Dubayah, R.O.; Drake, J.B. Lidar remote sensing for forestry. J. For. 2000, 98, 44-52.
29. Tang, H.; Dubayah, R.; Brolly, M.; Ganguly, S.; Zhang, G. Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat). Remote Sens. Environ. 2014, 154, 8-18.
30. Whitehurst, A.S.; Swatantran, A.; Blair, J.B.; Hofton, M.A.; Dubayah, R. Characterization of canopy layering in forested ecosystems using full waveform lidar. Remote Sens. 2013, 5, 2014-2036.
31. Lefsky, M.A.; Cohen, W.B.; Acker, S.A.; Parker, G.G.; Spies, T.A.; Harding, D. Lidar remote sensing of the canopy structure and biophysical properties of douglas-fir western hemlock forests. Remote Sens. Environ. 1999, 70, 339-361.
32. Ferraz, A.; Bretar, F.; Jacquemoud, S.; Gonçalves, G.; Pereira, L.; Tomé, M.; Soares, P. 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sens. Environ. 2012, 121, 210-223.
33. Morsdorf, F.; Mårell, A.; Koetz, B.; Cassagne, N.; Pimont, F.; Rigolot, E.; Allgöwer, B. Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sens. Environ. 2010, 114, 1403-1415.
34. Leiterer, R.; Furrer, R.; Schaepman, M.E.; Morsdorf, F. Forest canopy-structure characterization: A data-driven approach. Forest Ecol. Manag. 2015, 358, 48-61.
35. Wang, Y.; Weinacker, H.; Koch, B. A Lidar point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest. Sensors 2008, 8, 3938-3951.
36. Maltamo, M.; Packalén, P.; Yu, X.; Eerikäinen, K.; Hyyppä, J.; Pitkänen, J. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. For. Ecol. Manag. 2005, 216, 41-50.
37. Breidenbach, J.; Næsset, E.; Lien, V.; Gobakken, T.; Solberg, S. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens. Environ. 2010, 114, 911-924.
38. Frazer, G.W.; Wulder, M.A.; Niemann, K.O. Simulation and quantification of the fine-scale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights. For. Ecol. Manag. 2005, 214, 65-90.
39. Lee, A.C.; Lucas, R.M.; Brack, C. Quantifiying Vertical Forest Stand Structure Using Small Footprint LiDAR to Assess Potential Stand Dynamics; Institute for Forest Growth and Institute for Remote Sensing & Landscape Information Systems: Freiburg, Germany, 2004; pp. 213-217.
40. 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.
41. Ishii, H.T.; Tanabe, S.-I.; Hiura, T. Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. For. Sci. 2004, 50, 342-355.
42. Treitz, P.; Lim, K.; Woods, M.; Pitt, D.; Nesbitt, D.; Etheridge, D. LiDAR sampling density for forest resource inventories in Ontario, Canada. Remote Sens. 2012, 4, 830-848.
43. Zimble, D.A.; Evans, D.L.; Carlson, G.C.; Parker, R.C.; Grado, S.C.; Gerard, P.D. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sens. Environ. 2003, 87, 171-182.
44. Wulder, M.A.; White, J.C.; Nelson, R.F.; Næsset, E.; Ørka, H.O.; Coops, N.C.; Hilker, T.; Bater, C.W.; Gobakken, T. LiDAR sampling for large-area forest characterization: A review. Remote Sens. Environ. 2012, 121, 196-209.
45. White, J.C.; Wulder, M.A.; Varhola, A.; Vastaranta, M.; Coops, N.C.; Cook, B.D.; Pitt, D.; Woods, M. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. In Information Report FI-X-010; Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre: Victoria, BC, Canada, 2013.
46. Næsset, E. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 2004, 19, 164-179.
47. Schneider, F.D.; Leiterer, R.; Morsdorf, F.; Gastellu-Etchegorry, J.P.; Lauret, N.; Pfeifer, N.; Schaepman, M.E. Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sens. Environ. 2014, 152, 235-250.
48. Eugster, W.; Zeyer, K.; Zeeman, M.; Michna, P.; Zingg, A.; Buchmann, N.; Emmenegger, L. Nitrous oxide net exchange in a beech dominated mixed forest in Switzerland measured with a quantum cascade laser spectrometer. Biogeosci. Discuss. 2007, 4, 1167-1200.
49. AGIS (Aargauisches Geografisches Informationssystem). Forest Stand Monitoring (Bestandeskartierung). Available online: http://www.geoportal.ag.ch (accessed on 3 April 2015).
50. Wagner, W.; Hollaus, M.; Briese, C.; Ducic, V. 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. Int. J. Remote Sens. 2008, 29, 1433-1452.
51. RIEGL. Products. Airborne Scanning. Datasheets 2015. Available online: http://www.riegl.com (accessed on 20 October 2015).
52. Wagner, W.; Ullrich, A.; Ducic, V.; Melzer, T.; Studnicka, N. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J. Photogramm. Remote Sens. 2006, 60, 100-112.
53. Leiterer, R.; Morsdorf, F.; Torabzadeh, H.; Schaepman, M.E.; Muecke, W.; Pfeifer, N.; Hollaus, M. A voxel-based approach for canopy structure characterization using full-waveform airborne laser scanning. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012; pp. 3399-3402.
54. Muss, J.D.; Mladenoff, D.J.; Townsend, P.A. A pseudo-waveform technique to assess forest structure using discrete lidar data. Remote Sens. Environ. 2011, 115, 824-835.
55. Morsdorf, F.; Nichol, C.; Malthus, T.; Woodhouse, I.H. Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sens. Environ. 2009, 113, 2152-2163.
56. Farid, A.; Goodrich, D.C.; Bryant, R.; Sorooshian, S. Using airborne lidar to predict Leaf Area Index in cottonwood trees and refine riparian water-use estimates. J. Arid Environ. 2008, 72, 1-15.
57. Lovell, J.L.; Jupp, D.L.B.; Culvenor, D.S.; Coops, N.C. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can. J. Remote Sens. 2003, 29, 607-622.
58. Joerg, P.C.; Morsdorf, F.; Zemp, M. Uncertainty assessment of multi-temporal airborne laser scanning data: A case study on an Alpine glacier. Remote Sens. Environ. 2012, 127, 118-129.
59. Kükenbrink, D.; Leiterer, R.; Schneider, F.D.; Schaepman, M.E.; Morsdorf, F. Voxel based occlusion mapping and plant area index estimation from airborne laser scanning data. In Proceedings of the SilviLaser 2015, La Grande Motte, France, 28-30 September 2015.
60. Ruxton, G.D. The unequal variance t-test is an underused alternative to Student's t-test and the Mann-Whitney U test. Behav. Ecol. 2006, 17, 688-690.
61. Kendall, M.G.; Gibbons, J.D. Rank Correlation Methods, 5th ed.; Oxford University Press: London, UK, 1990.
62. Liu, C.; Frazier, P.; Kumar, L. Comparative assessment of the measures of thematic classification accuracy. Remote Sens. Environ. 2007, 107, 606-616.
63. Rosenfield, G.H.; Fitzpatrick-Lins, K. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Remote Sens. 1986, 52, 223-227.
64. Villikka, M.; Packalén, P.; Maltamo, M. The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees. Silva Fenn. 2012, 46, 99-110.
65. Ørka, H.O.; Næsset, E.; Bollandsås, O.M. Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sens. Environ. 2010, 114, 1445-1461.
66. Næsset, E. Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sens. Environ. 2005, 98, 356-370.
67. Kim, S.; McGaughey, R.J.; Andersen, H.-E.; Gerard Schreuder, G. Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data. Remote Sens. Environ. 2009, 113, 1575-1586.
68. Gobakken, T.; Næsset, E. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can. J. For. Res. 2008, 38, 1095-1109.
69. Lim, K.; Hopkinson, C.; Treitz, P. Examining the effects of sampling point densities on laser canopy height and density metrics. For. Chron. 2008, 84, 876-885.
70. Wilkes, P.; Jones, S.D.; Suarez, L.; Haywood, A.; Woodgate, W.; Soto-Berelov, M.; Mellor, A.; Skidmore, A.K. Understanding the effects of ALS pulse density for metric retrieval across diverse forest types. Photogramm. Eng. Remote Sens. 2015, 81, 625-635.
71. Hawbaker, T.J.; Gobakken, T.; Lesak, A.; Trømborg, E.; Contrucci, K.; Radeloff, V. Light detection and ranging-based measures of mixed hardwood forest structure. For. Sci. 2010, 56, 313-326.
72. Hayashi, R.; Weiskittel, A.; Sader, S. Assessing the feasibility of low-density LiDAR for stand inventory attribute predictions in complex and managed forests of Northern Maine, USA. Forests 2014, 5, 363-383.
73. Van Oosterom, P.; Ravada, S.; Horhammer, M.; Rubi, O.M.; Ivanova, M.; Kodde, M.; Tijssen, T. Point cloud data management. In proceedings of the IQmulus Workshop on Processing Large Geospatial Data 2014, Cardiff, Wales, UK, 8 July 2014.
74. Armston, J.; Disney, M.; Lewis, P.; Scarth, P.; Phinn, S.; Lucas, R.; Bunting, P.; Goodwin, N. Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sens. Environ. 2013, 134, 24-38.
75. Torabzadeh, H.; Leiterer, R.; Hueni, A.; Schaepman, M.E.; Morsdorf, F. Tree species classification in dense temperate mixed forests using a combination of imaging spectroscopy and airborne laser scanning. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Québec, QC, Canada, 13-18 July 2014; pp. 1253-1256.
76. Lindberg, E.; Eysn, L.; Hollaus, M.; Holmgren, J.; Pfeifer, N. Delineation of tree crowns and tree species classification from full-waveform airborne laser scanning data using 3-D ellipsoidal clustering. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2014, 7, 3174-3181.
77. Scanlan, I.; McElhinny, C.; Turner, P. A methodology for modelling canopy structure: An exploratory analysis in the tall wet eucalypt forests of Southern Tasmania. Forests 2010, 1, 4-24.
78. Torabzadeh, H.; Leiterer, R.; Tuia, D.; Schaepman, M.E.; Morsdorf, F. 3D iterative tree crown delineation in a multi-layered forest. Remote Sens. Environ. 2015, submitted.
79. Wing, B.M.; Ritchie, M.W.; Boston, K.; Cohen, W.B.; Gitelman, A.; Olsen, M.J. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sens. Environ. 2012, 124, 730-741.
80. Gao, T.; Hedblom, M.; Emilsson, T.; Nielsen, A.B. The role of forest stand structure as biodiversity indicator. For. Ecol. Manag. 2014, 330, 82-93.