Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LIDAR

Remote Sensing

Volumn 5, Issue 5, 2013, Pages 2308-2326

  Mora, Brice ^a   Wulder, Michael A ^a   White, Joanne C ^a   Hobart, Geordie ^a  

^a PACIFIC FORESTRY CENTRE   (Canada)

Author keywords

Biomass; Boreal; Crown; Forest; Height; Landsat; LiDAR; Modeling; Panchromatic; Sample; Volume

Indexed keywords

BOREAL; CROWN; FOREST; HEIGHT; LANDSAT; PANCHROMATIC; VOLUME;

BIOMASS; ESTIMATION; FORESTRY; MODELS; OPTICAL RADAR; REGRESSION ANALYSIS; SAMPLING;

SATELLITE IMAGERY;

BIOMASS; CANADA; IMAGE ANALYSIS; INVENTORIES; REGRESSION ANALYSIS; SAMPLING; SATELLITES;

EID: 84880351760     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs5052308     Document Type: Article

References (54)

1. Tomppo, E.O.; Gschwantner, T.; Lawrence, M.; McRoberts, R.E. National Forest Inventories: Pathways for Common Reporting; Springer: New York, NY, USA, 2010.
2. Wulder, M.A.; Kurz, W.; Gillis, M. National level forest monitoring and modelling in Canada. Progr. Plan. 2004, 61, 365-381.
3. Millennium Ecosystem Assessment. Living Beyond Our Means: Natural Assets and Human Well-Being (Statement from the Board); United Nations Environment Programme (UNEP); 2005. Available online: http://www.unep.org/maweb/en/BoardStatement.aspx (accessed on 13 March 2013).
4. Gillis, M.D.; Omule, A.Y.; Brierly, T. Monitoring Canada's forests: The National Forest Inventory. Forest Chron. 2005, 81, 214-221.
5. Natural Resources Canada. Canada's National Forest Inventory: Photo Plot Guidelines; Version 1.1; Natural Resources Canada: Victoria, BC, Canada, 2004.
6. Power, K.; Gillis, M.D. Canada's Forest Inventory 2001; Natural Resources Canada Canadian Forest Service Pacific Forestry Centre: Victoria, BC, Canada, 2006.
7. Wulder, M.A.; Campbell, C.; White, J.C.; Flannigan, M.; Campbell, I.D. National circumstances in the international circumboreal community. Forest Chron. 2007, 83, 539-556.
8. Wulder, M.A.; White, J.C.; Cranny, M.; Hall, R.J.; Luther, J.E.; Beaudoin, A.; Goodenough, D.G.; Dechka, J.A. Monitoring Canada's forests. Part 1: Completion of the EOSD land cover project. Can. J. Remote Sens. 2008, 34, 549-562.
9. Falkowski, M.J.; Wulder, M.A.; White, J.C.; Gillis, M.D. Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery. Progr. Phys. Geogr. 2009, 33, 403-423.
10. Wulder, M.A.; White, J.C.; Bater, C.W.; Coops, N.C.; Hopkinson, C.; Chen, G. Lidar plots-A new large-area data collection option: Context, concepts, and case study. Can. J. Remote Sens. 2012, 38, 600-618.
11. Hall, R.J.; Skakun, R.S.; Arsenault, E.J.; Case, B.S. Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. For. Ecol. Manage 2006, 225, 378-390.
12. Tomppo, E.O.; Tuomainen, T. Finland. In National Forest Inventories: Pathways for Common Reporting; Tomppo, E.O., Gschwantner, T., Lawrence, M., McRoberts, R.E., Eds.; Springer: New York, NY, USA, 2010; pp. 185-206.
13. Axelsson, A.L.; Ståhl, G.; Söderberg, U.; Petersson, H.; Fridman, J.; Lundström, A. Sweden. In National Forest Inventories: Pathways for Common Reporting; Tomppo, E.O., Gschwantner, T., Lawrence, M., McRoberts, R.E., Eds.; Springer: New York, NY, USA, 2010; pp. 541-553.
14. Næsset, E.; Gobakken, T.; Holmgren, J.; Hyyppä, H.; Hyyppä, J.; Maltamo, M.; Nilsson. M.; Olsson, H.; Persson, Å.; Söderman, U. Laser scanning of forest resources: The Nordic experience. Scand. J. For. Res. 2004, 19, 482-499.
15. Wulder, M.A.; Bater, C.W.; Coops, N.C.; Hilker, T.; White, J.C. The role of LiDAR in sustainable forest management. Forest Chron. 2008, 84, 807-826.
16. Wulder, M.A.; Seemann, D. Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Can. J. Remote Sens. 2003, 29, 536-543.
17. Chen, G.; Hay, G.J.; St-Onge, B.A. GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada. Int. J. Appl. Earth Obs. Geoinf. 2012, 15, 28-37.
18. Mora, B.; Wulder, M.A.; White, J.C. Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment. Remote Sens. Environ. 2010, 114, 2474-2484.
19. 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.
20. Frazer, G.; 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. Manage 2005, 214, 65-90.
21. Ecological Stratification Working Group (ESWG). A National Ecological Framework for Canada; Government of Canada: Ottawa/Hull, QC, Canada, 1995.
22. Canadian Climate Normals or Averages 1971-2000. Available online: www.climate.weatheroffice.gc.ca/climate_normals (accessed on 13 March 2013). 23. Krause, K. Radiance Conversion of QuickBird Data-Technical Note; DigitalGlobe Inc.: Longmont, CO, USA, 2003.
23. Krause, K. Radiance Conversion of QuickBird Data-Technical Note; DigitalGlobe Inc.: Longmont, CO, USA, 2003.
24. Krause, K.S. WorldView-1 pre and Post-Launch Radiometric Calibration and Early On-Orbit Characterization; DigitalGlobe Inc.: Longmont, CO, USA, 2008.
25. Wulder, M.A.; Loubier, E.; Richardson, D. Landsat-7 ETM+ orthoimage coverage of Canada. Can. J. Remote Sens. 2002, 28, 667-671.
26. Hopkinson, C.; Wulder, M.A.; Coops; N.C.; Milne, T.; Fox, A.; Bater, C.W. Airborne Lidar Sampling of the Canadian Boreal Forest: Planning, Execution, and Initial Processing. In Proceedings of SilviLaser, Hobart, TS, Australia, 16-19 October 2011; pp. 393-401.
27. Axelsson, P. DEM generation from laser scanner data using adaptive TIN models. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2000, 33, 110-117.
28. McGaughey, R.J. FUSION/LDV: Software for Lidar Data Analysis and Visualization (No. Version 2.90); Version 2; US Department of Agriculture: Seattle, WA, USA, 2010.
29. Nilsson, M. Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sens. Environ. 1996, 56, 1-7.
30. Næsset, E. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 2004, 19, 164-179.
31. Marshall, P.M.; Lemay, V. Forest Inventory. In Forestry Handbook for British Columbia; The Forestry Undergraduate Society, University of British Columbia: Vancouver, BC, Canada, 2005; pp. 577-604.
32. Lambert, M.-C.; Chhun-Huor, U.; Raulier, F. Canadian national tree aboveground biomass equations. Can. J. For. Res. 2005, 35, 1996-2018.
33. Ung, C.-H.; Bernier, P.; Guo, X.-J. Canada national biomass equations: New parameter estimates that include British Columbia data. Can. J. For. Res. 2008, 38, 1123-1132.
34. Li, Y.; Andersen, H.-E.; McGaughey, R.J. A comparison of statistical methods for estimating forest biomass from light detection and ranging data. Western J. Appl. For. 2008, 23, 223-231.
35. Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle. In Second International Symposium on Information Theory; Academiai Kiado: Budapest, Hungary, 1973; pp. 267-281.
36. Posada, D.; Buckley, T.R. Model selection and model averaging in phylogenetics: Advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. Syst. Biol. 2004, 53, 793-808.
37. Baatz, M.; Schäpe, A. Multiresolution Segmentation-An Optimization Approach for High Quality Multiscale Image Segmentation. In Proceedings of Angewandte Geographische Informationsverarbeitung XII, Beiträge zum AGIT-Symposium Salzburgz, Karlsruhe, Germany, 5-7 July2000; pp. 12-23.
38. Definiens. eCognition 4 User Guide; Definiens: Munich, Germany, 2004.
39. Wulder, M.A.; White, J.C.; Hay, G.J.; Castilla, G. Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery. Forest Chron. 2008, 84, 221-230.
40. Wulder, M.A.; Nelson, T. EOSD Land Cover Classification Legend Report; Version 2; Canadian Forest Service Natural Resources Canada: Victoria, BC, Canada, 2003.
41. Næsset, E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. 1997, 52, 49-56.
42. Parker, G.G.; Lowman, M.; Nadkarni, N. Structure and Microclimate of Forest Canopies. In Forest Canopies: A Review of Research on a Biological Frontier; Lowman, M.D., Nadkarni, N.M., Eds.; Academic Press: San Diego, CA, USA, 1995; pp. 73-106.
43. Asner, G.P.; Scurlock, J.M.O.; Hicke, J.A. Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies. Glob. Ecol. Biogeogr. 2003, 12, 191-205.
44. R Development Core Team. R: A Language and Environment for Statistical Computing, Reference Index Version 2.2.1; R Foundations for Statistical Computing: Vienna, Austria, 2005. Available online: http://www.r-project.org/ (accessed on 13 March 2013).
45. Crookston, N.L.; Finley, A.O. YaImpute: An R Package for kNN Imputation. J. Stat. Softw. 2008, 23, 1-16.
46. Natural Resources Canada. The State of Canada's Forests: Annual Report 2012; Natural Resources Canada: Ottawa, ON, Canada, 2012.
47. Paine, D.P.; Kiser, J.D. Aerial Photography and Image Interpretation; John Wiley and Sons: Hoboken, NJ, USA, 2003.
48. Popescu, S.C.; Wynne, R.H.; Nelson, R.F. Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens. 2003, 29, 564-577.
49. Peuhkurnen, J.; Maltamo, M.; Vesa, L.; Packalén, P. Estimation of forest stand characteristics using spectral histograms derived from an IKONOS satellite image. Photogramm. Eng. Remote Sensing 2008, 74, 1335-1341.
50. Maselli, F.; Chiesi, M.; Montaghi, A.; Pranzini, E. Use of ETM+ images to extend stem volume estimates obtained from LiDAR data. ISPRS J. Photogramm. 2011, 66, 662-671.
51. Næsset, E.; Gobakken, T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens. Environ. 2008, 112, 3079-3090.
52. Zhang, L. Cross-validation of non-linear growth functions for modelling tree height-diameter relationships. Ann. Bot. 1997, 79, 251-257.
53. Bechtold, W.A. Largest-crown-width prediction models for 53 species in the western United States. Western J. Appl. For. 2004, 19, 245-251.
54. Wulder, M.A.; White, J.C.; Hay, G.J.; Castilla, G. Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery. Forest Chron. 2008, 84, 221-230.