Journal of Forestry Research

, Volume 25, Issue 4, pp 761–771 | Cite as

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

  • Sisira Ediriweera
  • Sumith Pathirana
  • Tim Danaher
  • Doland Nichols
Original Paper


We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass (AGB) by fusion of LiDAR and Landsat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carried out separately for LiDAR and Landsat derived variables individually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation (3% for eucalypt forest and 2% combined sites) of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, and fusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical rainforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.


Fusion above-ground biomass LiDAR multispectral data subtropical plant communities 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Armston JD, Denham RJ, Danaher TJ, Scarth PF, Moffiet TN. 2009. Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. Journal of Applied Remote Sensing, 3: 1–28.CrossRefGoogle Scholar
  2. Birth GS, McVey G. 1968. Measuring the colour of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60: 640–643.CrossRefGoogle Scholar
  3. Bortolot ZJ, Wynne RH. 2005. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. Isprs Journal of Photogrammetry and Remote Sensing, 59: 342–360.CrossRefGoogle Scholar
  4. Brown L, Chen JM, Leblanc SG, Cihlar J. 2000. A shortwave infrared modification to the simple ratio for LAI retrival in borel forests: and image and model analysis. Remote Sensing of Environment, 71: 16–25.CrossRefGoogle Scholar
  5. Bureau of Meterology. 2010. Bureau of Meteorology (ABN 92 637 533 532), Australia. Available at: [accessed on 2 August 2012].Google Scholar
  6. Chambers JQ, dos Santos J, Ribeiro RJ, Higuchi N. 2001. Tree damage, allometric relationships, and above-ground net primary production in central Amazon forest. Forest Ecology and Management, 152: 73–84.CrossRefGoogle Scholar
  7. Chave JH, Andalo C, Brown S, Cairns MA, Chabbers JQ, Eamus D, Folster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson BW, Ogawa H, Puig H, Riera B, Yamakura T. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145: 87–99.PubMedCrossRefGoogle Scholar
  8. Chen JM. 1996. Evaluation for vegetation indices and modified simple ration for boreal application Canadian Journal of Remote Sensing, 22: 229–242.CrossRefGoogle Scholar
  9. Drake JB, Dubayah RO, Knox RG, Clark DB, Blair JB. 2002. Sensitivity of large-footprint LiDAR to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment, 81: 378–392.CrossRefGoogle Scholar
  10. Drake JB, Knox RG, Dubayah RO, Clark DB, Condit R, Blair JB, Hofton M. 2003. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecology and Biogeography, 12: 147–159.CrossRefGoogle Scholar
  11. Ediriweera S, Pathirana S, Danaher T, Nichols D. 2014 (accepted). LiDAR remote sensing of structural properties of subtropical rainforest and eucalypt forest in complex terrain in North-eastern Australia. Journal of Tropical Forest Science.Google Scholar
  12. Erdody TL, Moskal LM. 2010. Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114: 725–737.CrossRefGoogle Scholar
  13. Fassnacht KS, Gower ST. 1997. Interrelationships between the edaphic and stand characteristics, leaf area index and above ground next primary productivity of upland forest ecosystems in north central Wisconsin. Canadian Journal of Forestry, 27: 1058–1067.CrossRefGoogle Scholar
  14. Flood N, Danaher T, Gill T, Gillingham S. 2013. An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing, 5: 83–109.CrossRefGoogle Scholar
  15. Florence RG. 1996 Ecology and Silviculture of Eucalypt Forests. CSIRO Australia.Google Scholar
  16. Foody GM, Boyd DS, Cutler MEJ. 2003. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85: 463–474.CrossRefGoogle Scholar
  17. Foody GM, Cutler ME, McMorrow J, Pelz D, Tangki H, Boyd DS, Douglas I. 2001. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography, 10: 379–387.CrossRefGoogle Scholar
  18. Franklin J. 1986. Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing, 7: 1287–1301.CrossRefGoogle Scholar
  19. Heurich M, Thoma F. 2008. Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry, 81: 645–661.CrossRefGoogle Scholar
  20. Hudak AT, Crookston NL, Evans JS, Falkowski MJ, Smith AMS, Gessler PE, Morgan P. 2006. Regression modelling and mapping of coniferous forest basal area and tree density from discrete-return LiDAR and multispectral satellite data. Canadian Journal of Remote Sensing, 32: 126–138.CrossRefGoogle Scholar
  21. Hudak AT, Lefsky MA, Cohen WB. 2001: Integration of lidar and Landsat ETM data. In: Hofton, M.A. (ed.), The international archives of the Photogrammetry, Remote Sensing and Spatial Information Science. Volume XXXIV, Part 3/W4, Commission III, Annapolis MD, 22–24 October, pp.95–104Google Scholar
  22. Jakubauskas ME, Price KP. 1997. Empirical relationaship between structural and spectral factors of Yellowstone Lodgepole Pine Forests. Photogrammetric Engineering & Remote Sensing, 63: 1375–1381.Google Scholar
  23. Jensen JLR, Humes SK, Vierling LA, Hudak AT. 2008. Discrete return LiDAR based prediction of leaf area index in two conifer forests. Remote Sensing of Environment, 112: 3947–3957.CrossRefGoogle Scholar
  24. Krasnow K, Schoennagel T, Veblen TT. 2009. Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder country, Colorado, USA. Forest Ecology and Management, 257: 1603–1612.CrossRefGoogle Scholar
  25. Latifi H, Nothdurft A, Koch B. 2010. Non-parametric prediction and mapping of standing timber volum and biomass in a temperature forest. Application of multiple optical/LiDAR-derived predictors. Forestry, 83: 395–407.Google Scholar
  26. Li Y, Andersen HE, McGaughey R. 2008. A comparison of statistical methods for estimating forest biomass from light detection and ranging data. Western Journal of Applied Forestry, 23: 223–231.Google Scholar
  27. Lovell JL, Jupp DLB, Culvenor DS, Coops NC. 2003. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Canadian Journal of Remote Sensing, 29: 607–622.CrossRefGoogle Scholar
  28. Lu D. 2006. The potential and challenge of remote sensing based biomass estimation. International Journal of Remote Sensing, 27: 1297–1328.CrossRefGoogle Scholar
  29. Lu D, Batistella M. 2005. Exploring TM image texture and its relationships with biomass estimation in Rondonia, Brazilian Amazon. Acta Amazonica, 35: 261–268.CrossRefGoogle Scholar
  30. Lu D, Chen Q, Wang G, Moran E, Batistella M, Zhang M, Vaglio LG, Saah D. 2012. Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. International Journal of Forestry Research, 2012: 16.CrossRefGoogle Scholar
  31. Lymburner L, Beggs PJ, Jacobson CR. 2000. Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66: 183–191.Google Scholar
  32. Markham BL, Helder DL. 2012. Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Remote Sensing of Environment, 122 30–40.CrossRefGoogle Scholar
  33. McCombs JW, Roberts SD, Evans DL. 2003. Influence of fusing LiDAR and multispectral imagery on remotely sensed estimates of stand density and mean tree height in managed Loblolly pine plantation. Forest Science, 49: 457–466.Google Scholar
  34. Moffiet T, Armston JD, Mengersen K. 2010. Motivation, development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover. Isprs Journal of Photogrammetry and Remote Sensing, 65: 26–41.CrossRefGoogle Scholar
  35. Mutlu M, Popescu SC, Stripling C, Spencer T. 2008. Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sensing of Environment, 112: 274–285.CrossRefGoogle Scholar
  36. Myneni RB, Williams DL. 1994. On the Relationship between FAPAR and NDVI. Remote Sensing of Environment, 49: 200–211.CrossRefGoogle Scholar
  37. Nemani R, Pierce L, Running S, Band L. 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed Leaf Area Index estimates. International Journal of Remote Sensing, 14: 2519–2534.CrossRefGoogle Scholar
  38. Patenaude G, Milne R, Dawson TP. 2005. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environmental Science and Policy, 8: 161–178.CrossRefGoogle Scholar
  39. Popescu SC. 2007. Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 31: 646–655.CrossRefGoogle Scholar
  40. Popescu SC, Wynne RH, Nelson RF. 2003. Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29: 564–577.CrossRefGoogle Scholar
  41. Popescu SC, Wynne RH, Scrivani JA. 2004. Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot-Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA. Forest Science, 50: 551–565.Google Scholar
  42. Reich PB, Walters MB, Ellsworth DS. 1997. From tropic to tundra: Global convergence in plant functioning. In: Proceeding of the National Academy of Sciences, pp. 13730–13734, USA.Google Scholar
  43. Reich PB, Walters MB, Ellsworth DS, Vose JM, Volin JC, Gresham C, Bowman WD. 1988. Relationships of leaf dark respiration and leaf area index to specific leaf area and leaf life -span: A test across biomes and functional group Oecologia, 114: 471–482.CrossRefGoogle Scholar
  44. Riggins JJ, Tullis JA, Stephen FM. 2009. Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics. Gioscience & Remote Sensing, 46: 232–248.CrossRefGoogle Scholar
  45. Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Third Earth Resources Technology Satellite-1 Symposium. NASA SP-351, Greenbelt, Md., Woshington DC. pp. 301–317.Google Scholar
  46. Roy PS, Ravan SA. 1996. Biomass estimation using satellite remote sensing data-an investigation on possible approaches for natural forest. Journal of Biosciencce, 21: 535–561.CrossRefGoogle Scholar
  47. Smith RGB, Nichols JD, Vanclay JK. 2005. Dynamics of tree diversity in undisturbed and logged subtropcal rainforest in Australia. Biodiversity and Conservation, 14: 2447–2463.CrossRefGoogle Scholar
  48. Spanner MA, Pierce LL, Peterson DL, Running SW. 1990. Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance. International Journal of Remote Sensing, 11: 95–111.CrossRefGoogle Scholar
  49. Specht A, West PW. 2003. Estimation of biomass and sequestered carbon on farm forest plantations in northern New South Wales Australia. Biomass Bioenergy, 25: 363–379.CrossRefGoogle Scholar
  50. Specht RL, Specht A. 1999 Australian plant communities: dynamics of structure, growth and biodiversity.Oxford University Press.Google Scholar
  51. Tonolli S, Dalponte M, Neteler M, Rodeghiero M, Vescovo L, Gianelle D. 2011. Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps. Remote Sensing of Environment, 115: 2486–2498.CrossRefGoogle Scholar
  52. Treitz PM, Howarth PJ. 1999. Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography, 23: 359–390.CrossRefGoogle Scholar
  53. Vermote FE, Tanre D, Deuze JL, Herman M, Morcrette JJ. 1997. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: Overview. Ieee Transactions on Geoscience and Remote Sensing, 35: 675–686.CrossRefGoogle Scholar
  54. Vogelmann JE. 1990. Comparison between two vegetation indices for measuring different types of forest damage in the northeastern United States. International Journal of Remote Sensing, 11: 2281–2297.CrossRefGoogle Scholar
  55. Weller D, Denham R, Witte C, Mackie C, Smith D. 2003. Assessment and monitoring of foliage projected cover and canopy height across native vegetation in Queensland, Australia, using laser profiler data. Canadian Journal of Remote Sensing, 29: 578–591.CrossRefGoogle Scholar
  56. Westman WE, Rogers RW. 1977. Biomass and structure of a subtropical eucalypt forest, North Stradbroke Island. Australian Journal of Botany, 25: 171–19.CrossRefGoogle Scholar
  57. Zheng D, Rademacher J, Chen J, Crow T, Bresee M, Le Moine J, Ryu SR. 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93: 402–411.CrossRefGoogle Scholar
  58. Zianis D, Mencuccini M. 2004. On simplifying allometric analyses of forest biomass. Forest Ecology and Management, 187: 311–332.CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sisira Ediriweera
    • 1
    • 3
  • Sumith Pathirana
    • 1
  • Tim Danaher
    • 2
  • Doland Nichols
    • 1
  1. 1.School of Environment, Science and EngineeringSouthern Cross UniversityLismoreAustralia
  2. 2.Office of Environment and HeritageAlstonvilleAustralia
  3. 3.Faculty of Science and TechnologyUva Wellassa UniversityBadullaSri Lanka

Personalised recommendations