Skip to main content

Localisation of Biomass Potentials

  • Chapter
  • First Online:
Bioenergy from Wood

Part of the book series: Managing Forest Ecosystems ((MAFE,volume 26))

Abstract

The aim of this chapter is to provide an overview of methods of estimating woody biomass from inventory information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ackerman S, Ackerman PA, Seifert T (2013) Effects of irregular stand structure on tree growth, crown extension and branchiness of plantation grown Pinus patula. South For (in review)

    Google Scholar 

  • Adler-Golden SM, Matthew MW, Bernstein LS, Levine RY, Berk A, Richtsmeier SC, Acharya PK, Anderson GP, Felde G, Gardner J, Hoke M, Jeong LS, Pukall B, Mello J, Ratkowski A, Burke HH (1999) Atmospheric correction for short-wave spectral imagery based on MODTRAN4. SPIE Proc Imaging Spectrom 3753:61–69

    Google Scholar 

  • Andersen HE, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using lidar data. Remote Sens Environ 94(4):441–449

    Article  Google Scholar 

  • Attiwil PM, Ovington JD (1968) Determination of forest biomass. For Sci 14:13–15

    Google Scholar 

  • Baltsavias EP (1999) Airborne laser scanning: basic relations and formulas. ISPRS J Photogramm Remote Sens 54(2–3):199–214

    Article  Google Scholar 

  • Balzter H, Talmon E, Wagner W, Gaveau D, Plummer S, Yu JJ, Quegan S, Davidson M, Le Toan T, Gluck M, Shvidenko A, Nilsson S, Tansey K, Luckman A, Schmullius C (2002) Accuracy assessment of a large-scale forest cover map of Central Siberia from synthetic aperture radar. Can J Remote Sens 28(6):719–737

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Bundeswaldinventur (2008) The National Forest Inventory. Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz. Berlin. Online: http://www.bundeswaldinventur.de/enid/6a58daffd51c0d46d88bfb4ca088fd05,0/75.html

  • Butson C, King DJ (1999) Semivariance analysis of forest structure and remote sensing data to determine an optimal sample plot size. In: Proceedings of the 4th international airborne remote sensing conference, Ottawa, 21–24 June 1999. Environmental research institute of Michigan, Ann Arbor, vol II, pp 155–162

    Google Scholar 

  • Cartus O, Santoro M, Schmullius C, Li Z (2011) Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields. Remote Sens Environ 115:931–943

    Article  Google Scholar 

  • Carreiras JMB, Melo JM, Vasconcelos MJ (2013) Estimating the above-ground biomass in Miombo Savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sens 5(4):1524–1548

    Article  Google Scholar 

  • Chambers JQ, Asner GP, Morton DC, Anderson LO, Saatchi SS, Espırito-Santo FDB, Palace M, Souza C Jr (2007) Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends Ecol Evol 22(8):389–440

    Article  Google Scholar 

  • Cho MA, Debba P, Mathieu R, Naidoo L, van Aardt JAN, Asner GP (2010) Improving discrimination of savanna tree species through a multiple endmember spectral angle mapper approach: canopy-level analysis. IEEE Trans Geosci Remote Sens 48(11):4133–4142

    Google Scholar 

  • Clark ML, Clark DB, Roberts DA (2004) Small-footprint lidar estimation of sub canopy elevation and tree height in a tropical rainforest landscape. Remote Sens Environ 91(1):68–89

    Article  Google Scholar 

  • Clark ML, Roberts DA, Clark DB (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens Environ 96:375–398

    Article  Google Scholar 

  • Cohen WB, Maiersperger TK, Gower ST, Turner DP (2003) An improved strategy for regression of biophysical variables and Landsat ETM + data. Remote Sens Environ 84(4):561–571

    Article  Google Scholar 

  • Cook RD (1977) Detection of influential observation in linear regression. Technometrics 84:561–581

    Google Scholar 

  • Coops NC, Wulder MA, Culvenor DS, St-onge B (2004) Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data. Can J Rem Sens 30(6):855–866

    Article  Google Scholar 

  • Crookston NL, Finley AO (2008) YaImpute: an R package for kNN imputation. J Stat Softw 23(10):1–16

    Google Scholar 

  • Cunia T (1990) Forest inventory: on the structure of error of estimates. In: LaBau, VJ, Cunia T (eds) State-of-the-art methodology of forest inventory: a symposium proceedings. General technical report PNW-GTR-263. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland. 635 pp

    Google Scholar 

  • Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278

    Article  Google Scholar 

  • Curran PJ, Atkinson PM (1998) Geostatistics and remote sensing. Progr Phys Geogr 22(1):61–78

    Google Scholar 

  • Duncanson LI, Niemann KO, Wulder MA (2010) Integration of GLAS and Landsat TM data for aboveground biomass estimation. Can J Remote Sens 36(2):129–141

    Article  Google Scholar 

  • Fung T, Ma FY, Sui WL (1999) Hyperspectral data analysis for subtropical tree species identification. In: Proceedings of ASPRS annual conference, 17–21 May 1999, Portland, 10 pp.

    Google Scholar 

  • Gaveau DLA, Hill RA (2003) Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can J Rem Sens 29(5):650–657

    Article  Google Scholar 

  • Gong P, Pu R, Yu B (1997) Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sens Environ 62:189–200

    Article  Google Scholar 

  • Greenberg JA, Dobrowski SZ, Ustin SL (2005) Shadow allometry: estimating tree structural parameters using hyperspatial image analysis. Remote Sens Environ 97:15–25

    Article  Google Scholar 

  • Haala N, Brenner C (1999) Extraction of buildings and trees in urban environments. ISPRS J Photogramm Remote Sens 54(2–3):130–137

    Article  Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River, 842 p

    Google Scholar 

  • Hiemstra PH, Pebesma EJ, Twenhofel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network. Comput Geosci 35(8):1711–1721

    Article  CAS  Google Scholar 

  • Hodgson ME, Jensen JR, Schmidt L, Schill S, Davis B (2003) An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS level 1 and level 2 DEMs. Remote Sens Environ 84:295–308

    Article  Google Scholar 

  • Hollinger DY (2008) Defining a landscape-scale monitoring tier for the North American Carbon Program. In: Hoover CM (ed) Field measurements for forest carbon monitoring. Springer, New York, pp 3–16

    Chapter  Google Scholar 

  • Holmgren J (2004) Prediction of tree height, basal area, and stem volume in forest stands using airborne laser scanning. Scand J For Res 19(6):543–553

    Article  Google Scholar 

  • Holmgren J, Nilsson M, Olsson H (2003) Estimation of tree height and stem volume using airborne laser scanning. For Sci 49(3):419–428

    Google Scholar 

  • Hopkinson C, Chasmer L, Lim K, Treitz P, Creed I (2006) Towards a universal canopy height indicator. Can J Rem Sens 32(2):1–14

    Google Scholar 

  • Hudak AT, Crookston NL, Evans JS, Falkowski MJ, Smith AMS, Gessler PE, Morgan P (2006) Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Can J Remote Sens 32(2):126–138

    Article  Google Scholar 

  • Hudak AT, Crookston NL, Evans JS, Hall DE, Falkowski MJ (2008) Nearest Neighbor imputation of species-level, plot-scale forest structure attribute from lidar data. Remote Sens Environ 112(5):2232–2245

    Article  Google Scholar 

  • Hudak AT, Lefsky MA, Cohen WB, Berterretche M (2002) Integration of lidar and Landsat ETM + data for estimating and mapping forest canopy height. Remote Sens Environ 82(2–3):397–416

    Article  Google Scholar 

  • Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S, Zhu Y-H (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manage 128(1–2):109–120

    Article  Google Scholar 

  • Jordan MI, Bishop CM (1996) Neural networks. ACM Comput Surv 28(1):73–75

    Article  Google Scholar 

  • Kangas A, Maltamo M (eds) (2006) Forest inventory – methodology and applications, Managing forest ecosystems series. Springer, Dordrecht

    Google Scholar 

  • Kasischke ES, Christensen NL Jr, Bourgeau-Chavez LL (1995) Correlating radar backscatter with components of biomass in loblolly pine forests. IEEE Trans Geosci Remote Sens 33(3):643–659

    Article  Google Scholar 

  • Kätsch C, Vogt H (1999) Remote sensing from space—present and future applications in forestry, nature conservation and landscape management. South Afr For J 185(1):14–26

    Google Scholar 

  • Kokaly RF (2001) Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens Environ 75:153–161

    Article  Google Scholar 

  • Kokaly RF, Clark RN (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ 67:267–287

    Article  Google Scholar 

  • Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002a) Lidar remote sensing for ecosystem studies. Bioscience 52(1):19–30

    Article  Google Scholar 

  • Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002b) Lidar remote sensing of above-ground biomass in three biomes. Global Ecol Biogeogr 11:393–399

    Article  Google Scholar 

  • Magnussen S, Boudewyn P (1998) Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Can J Forest Res 28(7):1016–1031

    Article  Google Scholar 

  • Mahalanobis PC (1936) On the generalised distance in statistics. Proc Indian Natl Sci Acad Phys Sci 12:49–55

    Google Scholar 

  • Maltamo M, Tokola T, Lehikoinen M (2003) Estimating stand characteristics by combining single tree pattern recognition of digital video imagery and a theoretical diameter distribution model. For Sci 49(1):98–109

    Google Scholar 

  • Maltamo M, Mustonen K, Hyyppä J, Pitkänen J, Yu X (2004) The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve. Can J Forest Res 34(9):1791–1801

    Article  Google Scholar 

  • Mandallaz D (2008) Sampling techniques for forest inventories, Applied environmental statistics series 4. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Martin ME, Newman SD, Aber JD, Congalton RG (1998) Determining forest species composition using high spectral resolution remote sensing data. Remote Sens Environ 65:249–254

    Article  Google Scholar 

  • Matthew MW, Adler-Golden SM, Berk A, Richtsmeier SC, Levine RY, Bernstein LS, Acharya PK, Anderson GP, Felde HW, Hoke MP, Ratkowski A, Burke HH, Kaiser RD, Millerd DP (2000) Status of atmospheric correction using a MODTRAN4-based algorithm. In: Shen SS (ed) SPIE proceeding, algorithms for multispectral, hyperspectral, and ultraspectral imagery VI. Proceedings of SPIE, vol 4049, pp 199–207

    Google Scholar 

  • Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L, Hendrix CJ (2000) Predicting forest stand characteristics with airborne scanning lidar. Photogramm Eng Remote Sens 66(11):1367–1371

    Google Scholar 

  • Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99

    Article  Google Scholar 

  • Næsset E, Gobakken T (2005) Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sens Environ 96(3–4):453–465

    Article  Google Scholar 

  • Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56(1):1–7

    Article  Google Scholar 

  • Pagnutti M, Ryan RE, Kelly M, Holekamp K, Zanoni V, Thome K, Schiller S (2003) Radiometric characterization of IKONOS multispectral imagery. Remote Sens Environ 88(1–2):53–68

    Article  Google Scholar 

  • Popescu SC, Wynne RH, Nelson RF (2002) Estimating plot level tree heights with lidar: local filtering with a canopy-height based variable window size. Comput Electron Agric 37(1–3):71–95

    Article  Google Scholar 

  • 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, U.S.A. For Sci 50(4):551–565

    Google Scholar 

  • Reese H, Nilsson M, Sandström P, Olsson H (2002) Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput Electron Agric 37(1–3):37–55

    Article  Google Scholar 

  • Riaño D, Meier E, Allgöwer B, Chuvieco E, Ustin SL (2003) Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sens Environ 86:177–186

    Article  Google Scholar 

  • Richards T, Gallego J, Achard F (2000) Sampling for forest cover change assessment at the pan-tropical scale. Int J Rem Sens 21(6):1473–1490

    Article  Google Scholar 

  • Roberts JW, van Aardt JAN, Ahmed FB (2011) Image fusion for enhanced forest structure assessment. Int J Remote Sens 32(1):243–266

    Article  Google Scholar 

  • Saatchi SS et al (2007) Spatial distribution of live aboveground biomass in the Amazon Basin. Global Change Biol 13:816–837

    Article  Google Scholar 

  • Santoro M, Askne J, Smith G, Fransson JES (2002) Stem volume retrieval in boreal forests from ERS-1/2 interferometry. Remote Sens Environ 81:19–35

    Article  Google Scholar 

  • Schulze RE (1997) South African atlas of agrohydrology and Climatology. Report TT82/96. Water Research Commission, Pretoria

    Google Scholar 

  • Seidel D, Albert K, Fehrmann L, Ammer C (2012) The potential of terrestrial laser scanning for the estimation of understory biomass in coppice-with-standard systems Biomass Bioenergy 47:20–25. http://www.sciencedirect.com/science/journal/09619534

  • Seielstad CA, Queen LP (2003) Using airborne laser altimetry to determine fuel models for estimating fire behavior. J For 101(4):10–15

    Google Scholar 

  • Smith WB, Miles PD, Vissage JS, Pugh SA (2004) Forest resources of the United States, 2002. General technical report NC-241. USDA Forest Service, Washington, DC

    Google Scholar 

  • Stauffer HB (1982) A sample size table for forest sampling. For Sci 28(4):777–784

    Google Scholar 

  • Suarez JC, Ontiveros C, Smith S, Snape S (2005) Use of lidar and aerial photography in the estimation of individual tree heights in forestry. Comput Geosci 31(2):253–262

    Article  Google Scholar 

  • Sun G, Ranson KJ, Kharuk VI, Kovacs K (2003) Validation of surface height from shuttle radar topography mission using shuttle laser altimeter. Remote Sens Environ 88(4):401–411

    Article  Google Scholar 

  • Treitz P (2001) Variogram analysis of high spatial resolution remote sensing data: an examination of boreal forest ecosystems. Int J Rem Sens 22(18):3895–3900

    Article  Google Scholar 

  • Treuhaft RN, Law BE, Asner GP (2004) Forest attributes from radar interferometric structure and its fusion with optical remote sensing. BioScience 54(6):561–571

    Article  Google Scholar 

  • van Aardt JAN, Norris-Rogers M (2008) Spectral-age interactions in managed, even-aged eucalyptus plantations: application of discriminant analysis and classification and regression trees approaches to hyperspectral data. Int J Remote Sens 29(6):1841–1845

    Article  Google Scholar 

  • van Aardt JAN, Wynne RH (2001) Spectral separability among six southern tree species. Photogramm Eng Remote Sens 67(12):1367–1375

    Google Scholar 

  • van Aardt JAN, Wynne RH (2007) Examining pine spectral separability using hyperspectral data from an airborne sensor: an extension of field-based results. Int J Remote Sens 28(1–2):431–436

    Article  Google Scholar 

  • van Aardt JAN, Wynne RH, Oderwald RG (2006) Forest volume and biomass estimation using small-footprint lidar-distributional parameters on a per-segment basis. For Sci 52(6):636–649

    Google Scholar 

  • van Aardt JAN, Wynne RH, Scrivani JA (2008) Lidar-based mapping of forest volume and biomass by taxonomic group using structurally homogenous segments. Photogramm Eng Remote Sens 74(8):1033–1044

    Article  Google Scholar 

  • van Laar A, Akça A (2007) Forest mensuration, Managing forest ecosystems series 13. Springer, Dordrecht

    Book  Google Scholar 

  • van Laar A, Theron JM (2004) Equations for predicting the biomass of Acacia Cyclops and Acacia saligna in the western and eastern Cape regions of South Africa: part 1: tree-level models. S Afr For J 201:25–34

    Google Scholar 

  • van Laar A, van Lill WS (1978) A biomass study in Pinus radiata. S Afr For J 107(1):71–76

    Google Scholar 

  • Waggoner PE (2009) Forest inventories discrepancies and uncertainties. Discussion paper, Washington, DC

    Google Scholar 

  • Wehr A, Lohr U (1999) Airborne laser scanning – an introduction and overview. ISPRS J Photogramm Remote Sens 54(2–3):68–82

    Article  Google Scholar 

  • Wessman CA, Aber JD, Peterson DL (1989) An evaluation of imaging spectrometry for estimating forest canopy chemistry. Remote Sens Environ 10(8):1293–1316

    Google Scholar 

  • Woodcock CE, Strahler AH, Jupp DLB (1988a) The use of variograms in remote sensing: I. Scene models and simulated images. Remote Sens Environ 25(3):323–348

    Article  Google Scholar 

  • Woodcock CE, Strahler AH, Jupp DLB (1988b) The use of variograms in remote sensing: II. Real digital images. Remote Sens Environ 25(3):349–379

    Article  Google Scholar 

  • Wu Y, Strahler AH (1994) Remote estimation of crown size, stand density and biomass on the Oregon transect. Ecol Appl 4(2):299–312

    Article  Google Scholar 

  • Wulder MA, Seemann D (2003) Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Can J Remote Sens 29(5):536–543

    Article  Google Scholar 

  • Yoder BJ, Pettigrew-Cosby RE (1995) Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens Environ 53:199–211

    Article  Google Scholar 

  • Zimble DA, Evan DL, Carlson DG, Parker RC, Grado SC, Gerrard PD (2003) Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sens Environ 87(2–3):171–182

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anton Kunneke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Kunneke, A., van Aardt, J., Roberts, W., Seifert, T. (2014). Localisation of Biomass Potentials. In: Seifert, T. (eds) Bioenergy from Wood. Managing Forest Ecosystems, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7448-3_2

Download citation

Publish with us

Policies and ethics