Skip to main content
SpringerLink
Log in

Modelling Biomass

  • Chapter
  • First Online:
Forest Bioenergy

Part of the book series: Green Energy and Technology ((GREEN))

  • 84 Accesses

Abstract

Models are abstractions that enable to assess and predict forest stands variables. Two broad methods to estimate biomass were defined. The direct method, the most accurate, has the disadvantage of resulting from destructive sampling. Inversely, the indirect method uses a variety of mathematical methods, with forest inventory, remote sensing, and ancillary data as explanatory variables. The accuracy of the biomass models is dependent on data acquisition precision and accuracy as well as on the model’s uncertainties. Moreover, model accuracy is also dependent on species, individual tree biomass partitioning, stand structure, region, and spatial and temporal scales. This chapter overviews the data sets and mathematical methods used for modelling biomass and their uncertainties. Overall, the performance of the forest biomass functions is linked to its ability to accommodate the variability inherent to forest data and to make biomass assessments, monitoring, and predictions with the best possible precision and accuracy and the smallest bias.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
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

  1. Gonçalves AC (2022) Influence of stand structure on forest biomass sustainability. In: Jhariya MK, Meena RS, Banerjee A, Meena SN (eds) Natural resources conservation and advances for sustainability. Elsevier, Cambridge, United States, pp 327–352

    Chapter  Google Scholar 

  2. Gonçalves AC (2022) Stand structure impacts on forest modelling. Appl Sci 12:6963. https://doi.org/10.3390/app12146963

    Article  Google Scholar 

  3. Burkhart HE, Tomé M (2012) Modeling forest trees and stands. Springer, Netherlands, Dordrecht

    Book  Google Scholar 

  4. Dittmann S, Thiessen E, Hartung E (2017) Applicability of different non-invasive methods for tree mass estimation: a review. For Ecol Manage 398:208–215. https://doi.org/10.1016/j.foreco.2017.05.013

    Article  Google Scholar 

  5. McRoberts RE, Tomppo EO, Næsset E (2010) Advances and emerging issues in national forest inventories. Scand J For Res 25:368–381. https://doi.org/10.1080/02827581.2010.496739

    Article  Google Scholar 

  6. Tomppo E, Olsson H, Ståhl G et al (2008) Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens Environ 112:1982–1999. https://doi.org/10.1016/j.rse.2007.03.032

    Article  Google Scholar 

  7. Vashum KT, Jayakumar S (2012) Methods to estimate above-ground biomass and carbon stock in natural forests-a review. J Ecosyst Ecogr 02.https://doi.org/10.4172/2157-7625.1000116

  8. Xu Z, Du W, Zhou G et al (2022) Aboveground biomass allocation and additive allometric models of fifteen tree species in northeast China based on improved investigation methods. For Ecol Manage 505:119918. https://doi.org/10.1016/j.foreco.2021.119918

    Article  Google Scholar 

  9. Kangas A, Astrup R, Breidenbach J et al (2018) Remote sensing and forest inventories in Nordic countries–roadmap for the future. Scand J For Res 33:397–412. https://doi.org/10.1080/02827581.2017.1416666

    Article  Google Scholar 

  10. Henttonen HM, Kangas A (2015) Optimal plot design in a multipurpose forest inventory. Forest Ecosyst 2:14. https://doi.org/10.1186/s40663-015-0055-2

    Article  Google Scholar 

  11. Pretzsch H (2009) Forest dynamics, growth, and yield. Springer, Berlin

    Book  Google Scholar 

  12. Avery TE, Burkhart HE (1994) Measurements, 4th edn. Macgraw-Hill Inc., New York

    Google Scholar 

  13. Vidal C, Lanz A, Tomppo E et al (2008) Establishing forest inventory reference definitions for forest and growing stock: a study towards common reporting. Silva Fennica 42:247–266. https://doi.org/10.14214/sf.255

  14. Pretzsch H (2014) Canopy space filling and tree crown morphology in mixed-species stands compared with monocultures. For Ecol Manage 327:251–264. https://doi.org/10.1016/j.foreco.2014.04.027

    Article  Google Scholar 

  15. Lu D, Chen Q, Wang G et al (2016) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digital Earth 9:63–105. https://doi.org/10.1080/17538947.2014.990526

    Article  Google Scholar 

  16. White JC, Coops NC, Wulder MA et al (2016) Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens 42:619–641. https://doi.org/10.1080/07038992.2016.1207484

    Article  Google Scholar 

  17. Ke Y, Quackenbush LJ (2011) A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32:4725–4747. https://doi.org/10.1080/01431161.2010.494184

    Article  Google Scholar 

  18. Eisfelder C, Kuenzer C, Dech S (2012) Derivation of biomass information for semi-arid areas using remote-sensing data. Int J Remote Sens 33:2937–2984. https://doi.org/10.1080/01431161.2011.620034

    Article  Google Scholar 

  19. Ghosh SM, Behera MD (2018) Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Appl Geogr 96:29–40. https://doi.org/10.1016/j.apgeog.2018.05.011

    Article  Google Scholar 

  20. Kumar L, Sinha P, Taylor S, Alqurashi AF (2015) Review of the use of remote sensing for biomass estimation to support renewable energy generation. J Appl Remote Sens 9:097696. https://doi.org/10.1117/1.JRS.9.097696

    Article  Google Scholar 

  21. Gail WB (2007) Remote sensing in the coming decade: the vision and the reality. J Appl Remote Sens 1:012505. https://doi.org/10.1117/12.694379

    Article  Google Scholar 

  22. Boyd DS, Danson FM (2005) Satellite remote sensing of forest resources: three decades of research developmen. Progress Phys Geogr Earth Environ 29:1–26. https://doi.org/10.1191/0309133305pp432

    Article  Google Scholar 

  23. Koskinen J, Leinonen U, Vollrath A et al (2019) Participatory mapping of forest plantations with open foris and google earth engine. ISPRS J Photogramm Remote Sens 148:63–74. https://doi.org/10.1016/j.isprsjprs.2018.12.011

    Article  Google Scholar 

  24. Fassnacht FE, Latifi H, Stereńczak K et al (2016) Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186:64–87. https://doi.org/10.1016/j.rse.2016.08.013

    Article  Google Scholar 

  25. Thomlinson JR, Bolstad PV, Cohen WB (1999) Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sens Environ 70:16–28. https://doi.org/10.1016/S0034-4257(99)00055-3

    Article  Google Scholar 

  26. Li M, Zang S, Zhang B et al (2014) A review of remote sensing image classification techniques: the role of Spatio-contextual information. European Journal of Remote Sensing 47:389–411. https://doi.org/10.5721/EuJRS20144723

    Article  Google Scholar 

  27. Chen Q, Gong P (2004) Automatic variogram parameter extraction for textural classification of the panchromatic IKONOS imagery. IEEE Trans Geosci Remote Sens 42:1106–1115. https://doi.org/10.1109/TGRS.2004.825591

    Article  Google Scholar 

  28. Brovkina O, Novotny J, Cienciala E et al (2017) Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe. Ecol Eng 100:219–230. https://doi.org/10.1016/j.ecoleng.2016.12.004

    Article  Google Scholar 

  29. Chao Z, Liu N, Zhang P et al (2019) Estimation methods developing with remote sensing information for energy crop biomass: a comparative review. Biomass Bioenerg 122:414–425. https://doi.org/10.1016/j.biombioe.2019.02.002

    Article  Google Scholar 

  30. Gonçalves AC, Sousa AMO, Mesquita PG (2017) Estimation and dynamics of above ground biomass with very high resolution satellite images in Pinus pinaster stands. Biomass Bioenerg 106:146–154. https://doi.org/10.1016/j.biombioe.2017.08.026

    Article  Google Scholar 

  31. Gonçalves AC, Sousa AMO, Mesquita P (2019) Functions for aboveground biomass estimation derived from satellite images data in Mediterranean agroforestry systems. Agrofor Syst 93:1485–1500. https://doi.org/10.1007/s10457-018-0252-4

    Article  Google Scholar 

  32. Sousa AMO, Gonçalves AC, da Silva JRM (2017) Above‐ground biomass estimation with high spatial resolution satellite images. In: Tumuluru JS (ed) Biomass volume estimation and valorization for energy. InTech

    Google Scholar 

  33. Sousa AMO, Gonçalves AC, Mesquita P, Marques da Silva JR (2015) Biomass estimation with high resolution satellite images: a case study of Quercus rotundifolia. ISPRS J Photogramm Remote Sens 101:69–79. https://doi.org/10.1016/j.isprsjprs.2014.12.004

    Article  Google Scholar 

  34. Fernández-Manso O, Fernández-Manso A, Quintano C (2014) Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. Int J Appl Earth Obs Geoinf 31:45–56. https://doi.org/10.1016/j.jag.2014.03.005

    Article  Google Scholar 

  35. Tomppo E, Nilsson M, Rosengren M et al (2002) Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass. Remote Sens Environ 82:156–171. https://doi.org/10.1016/S0034-4257(02)00031-7

    Article  Google Scholar 

  36. Zhang Q, He HS, Liang Y et al (2018) Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests. Can J For Res 48:461–479. https://doi.org/10.1139/cjfr-2017-0346

    Article  Google Scholar 

  37. Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27:1297–1328. https://doi.org/10.1080/01431160500486732

    Article  Google Scholar 

  38. Ko C, Remmel TK (2017) Airborne LiDAR applications in forest landscapes. In: Remmel TK, Perera AH (eds) Mapping forest landscape patterns. Springer, New York, pp 105–185

    Google Scholar 

  39. Radtke P, Walker D, Frank J et al (2017) Improved accuracy of aboveground biomass and carbon estimates for live trees in forests of the eastern United States. Forestry 90:32–46. https://doi.org/10.1093/forestry/cpw047

    Article  Google Scholar 

  40. Skovsgaard JP, Nord-Larsen T (2012) Biomass, basic density and biomass expansion factor functions for European beech (Fagus sylvatica L.) in Denmark. Eur J Forest Res 131:1035–1053. https://doi.org/10.1007/s10342-011-0575-4

    Article  Google Scholar 

  41. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. Forest Sci 49:12–35. https://doi.org/10.1093/forestscience/49.1.12

    Article  Google Scholar 

  42. Alfaro-Sánchez R, Valdés-Correcher E, Espelta JM et al (2020) How do social status and tree architecture influence radial growth, wood density and drought response in spontaneously established oak forests? Ann For Sci 77:49. https://doi.org/10.1007/s13595-020-00949-x

    Article  Google Scholar 

  43. Chave J, Coomes D, Jansen S et al (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

    Article  Google Scholar 

  44. Knapic S, Louzada JL, Leal S, Pereira H (2008) Within-tree and between-tree variation of wood density components in cork oak trees in two sites in Portugal. Forestry 81:465–473. https://doi.org/10.1093/forestry/cpn012

    Article  Google Scholar 

  45. Kuyah S, Muthuri C, Jamnadass R et al (2012) Crown area allometries for estimation of aboveground tree biomass in agricultural landscapes of western Kenya. Agrofor Syst 86:267–277. https://doi.org/10.1007/s10457-012-9529-1

    Article  Google Scholar 

  46. Resquin F, Navarro-Cerrillo RM, Carrasco-Letelier L, Casnati CR (2019) Influence of contrasting stocking densities on the dynamics of above-ground biomass and wood density of Eucalyptus benthamii, Eucalyptus dunnii, and Eucalyptus grandis for bioenergy in Uruguay. For Ecol Manage 438:63–74. https://doi.org/10.1016/j.foreco.2019.02.007

    Article  Google Scholar 

  47. Vannoppen A, Boeckx P, De Mil T et al (2018) Climate driven trends in tree biomass increment show asynchronous dependence on tree-ring width and wood density variation. Dendrochronologia 48:40–51. https://doi.org/10.1016/j.dendro.2018.02.001

    Article  Google Scholar 

  48. Henry M, Besnard A, Asante WA et al (2010) Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For Ecol Manage 260:1375–1388. https://doi.org/10.1016/j.foreco.2010.07.040

    Article  Google Scholar 

  49. Poudel KP, Temesgen H, Radtke PJ, Gray AN (2019) Estimating individual-tree aboveground biomass of tree species in the western U.S.A. Can J For Res 49:701–714. https://doi.org/10.1139/cjfr-2018-0361

    Article  Google Scholar 

  50. Pinheiro JC, Bates DM (2000) Mixed-Effects models in S and S-PLUS. Springer, New York

    Book  Google Scholar 

  51. Robinson A, Hamann JD (2011) Forest analytics with R: an introduction. Springer, New York

    Book  Google Scholar 

  52. Xiang W, Li L, Ouyang S et al (2021) Effects of stand age on tree biomass partitioning and allometric equations in Chinese fir (Cunninghamia lanceolata) plantations. Eur J Forest Res 140:317–332. https://doi.org/10.1007/s10342-020-01333-0

    Article  Google Scholar 

  53. Zhang J, Fiddler GO, Young DH et al (2021) Allometry of tree biomass and carbon partitioning in ponderosa pine plantations grown under diverse conditions. For Ecol Manage 497:119526. https://doi.org/10.1016/j.foreco.2021.119526

    Article  Google Scholar 

  54. Parresol BR (2001) Additivity of nonlinear biomass equations. Can J For Res 31:865–878. https://doi.org/10.1139/x00-202

    Article  Google Scholar 

  55. Zhao D, Westfall J, Coulston JW et al (2019) Additive biomass equations for slash pine trees: comparing three modeling approaches. Can J For Res 49:27–40. https://doi.org/10.1139/cjfr-2018-0246

    Article  Google Scholar 

  56. Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Sci 45:573–593. https://doi.org/10.1093/forestscience/45.4.573

    Article  Google Scholar 

  57. Carvalho JP, Parresol BR (2003) Additivity in tree biomass components of Pyrenean oak (Quercus pyrenaica Willd.). For Ecol Manage 179:269–276. https://doi.org/10.1016/S0378-1127(02)00549-2

    Article  Google Scholar 

  58. Correia A, Faias S, Tomé M (2008) Ajustamento Simultâneo de Equações de Biomassa de Pinheiro Manso no Sul de Portugal. Silva Lusitana 16:197–205

    Google Scholar 

  59. Levine J, de Valpine P, Battles J (2021) Generalized additive models reveal among-stand variation in live tree biomass equations. Can J For Res 51:546–564. https://doi.org/10.1139/cjfr-2020-0219

    Article  Google Scholar 

  60. Nord-Larsen T, Meilby H, Skovsgaard JP (2017) Simultaneous estimation of biomass models for 13 tree species: effects of compatible additivity requirements. Can J For Res 47:765–776. https://doi.org/10.1139/cjfr-2016-0430

    Article  Google Scholar 

  61. Nord-Larsen T, Nielsen AT (2015) Biomass, stem basic density and expansion factor functions for five exotic conifers grown in Denmark. Scand J For Res 30:135–153. https://doi.org/10.1080/02827581.2014.986519

    Article  Google Scholar 

  62. Skovsgaard JP, Bald C, Nord-Larsen T (2011) Functions for biomass and basic density of stem, crown and root system of Norway spruce ( Picea abies (L.) Karst.) in Denmark. Scand J For Res 26:3–20. https://doi.org/10.1080/02827581.2011.564381

    Article  Google Scholar 

  63. Fang Z, Bailey RL (1998) Height–diameter models for tropical forests on Hainan Island in southern China. For Ecol Manage 110:315–327. https://doi.org/10.1016/S0378-1127(98)00297-7

    Article  Google Scholar 

  64. Yuancai L, Parresol BR (2001) Remarks on height-diameter modeling. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC

    Google Scholar 

  65. Vanclay JK (1994) Modelling forest growth and yield: applications to mixed tropical forests. CAB International, Wallingford, U.K.

    Google Scholar 

  66. Picard N, Rutishauser E, Ploton P et al (2015) Should tree biomass allometry be restricted to power models? For Ecol Manage 353:156–163. https://doi.org/10.1016/j.foreco.2015.05.035

    Article  Google Scholar 

  67. Asrat Z, Eid T, Gobakken T, Negash M (2020) Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia. For Ecol Manage 473:118335. https://doi.org/10.1016/j.foreco.2020.118335

    Article  Google Scholar 

  68. Dutcă I, Mather R, Ioraş F (2018) Tree biomass allometry during the early growth of Norway spruce ( Picea abies ) varies between pure stands and mixtures with European beech (Fagus sylvatica). Can J For Res 48:77–84. https://doi.org/10.1139/cjfr-2017-0177

    Article  Google Scholar 

  69. Elfving B, Ulvcrona KA, Egnell G (2017) Biomass equations for lodgepole pine in northern Sweden. Can J For Res 47:89–96. https://doi.org/10.1139/cjfr-2016-0131

    Article  Google Scholar 

  70. Dutcă I, Mather R, Blujdea VNB et al (2018) Site-effects on biomass allometric models for early growth plantations of Norway spruce (Picea abies (L.) Karst.). Biomass Bioenerg 116:8–17. https://doi.org/10.1016/j.biombioe.2018.05.013

    Article  Google Scholar 

  71. Forrester DI (2021) Does individual-tree biomass growth increase continuously with tree size? For Ecol Manage 481:118717. https://doi.org/10.1016/j.foreco.2020.118717

    Article  Google Scholar 

  72. Sillett SC, Van Pelt R, Carroll AL et al (2020) Aboveground biomass dynamics and growth efficiency of Sequoia sempervirens forests. For Ecol Manage 458:117740. https://doi.org/10.1016/j.foreco.2019.117740

    Article  Google Scholar 

  73. Mankou GS, Ligot G, Loubota Panzou GJ et al (2021) Tropical tree allometry and crown allocation, and their relationship with species traits in central Africa. Forest Ecol Manag 493:119262. https://doi.org/10.1016/j.foreco.2021.119262

  74. Annighöfer P, Ameztegui A, Ammer C et al (2016) Species-specific and generic biomass equations for seedlings and saplings of European tree species. Eur J Forest Res 135:313–329. https://doi.org/10.1007/s10342-016-0937-z

    Article  Google Scholar 

  75. Greenberg JA, Dobrowski SZ, Ustin SL (2005) Shadow allometry: estimating tree structural parameters using hyperspatial image analysis. Remote Sens Environ 97:15–25. https://doi.org/10.1016/j.rse.2005.02.015

    Article  Google Scholar 

  76. Brown S, Gillespie ARJ, Lugo AE (1989) Biomass estimation methods for tropical forests with aplications to forest inventory data. Forest Science 35:881–902. https://doi.org/10.1093/forestscience/35.4.881

    Article  Google Scholar 

  77. Fehrmann L, Kleinn C (2006) General considerations about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. For Ecol Manage 236:412–421. https://doi.org/10.1016/j.foreco.2006.09.026

    Article  Google Scholar 

  78. Jagodziński AM, Dyderski MK, Gęsikiewicz K et al (2018) How do tree stand parameters affect young Scots pine biomass?–Allometric equations and biomass conversion and expansion factors. For Ecol Manage 409:74–83. https://doi.org/10.1016/j.foreco.2017.11.001

    Article  Google Scholar 

  79. Keith H, Barrett D, Keenan R (2000) Review of allometric relationships for estimating woody biomass for New South Wales, the Australian Capital Territory, Victoria, Tasmania and South Australia. Australian Greenhouse Office, Canberra

    Google Scholar 

  80. Li H, Zhao P (2013) Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale. For Ecol Manage 289:153–163. https://doi.org/10.1016/j.foreco.2012.10.002

    Article  Google Scholar 

  81. Návar J (2009) Biomass component equations for Latin American species and groups of species. Ann For Sci 66:208–208. https://doi.org/10.1051/forest/2009001

    Article  Google Scholar 

  82. Djomo AN, Ibrahima A, Saborowski J, Gravenhorst G (2010) Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For Ecol Manage 260:1873–1885. https://doi.org/10.1016/j.foreco.2010.08.034

  83. Ter-Mikaelian MT, Korzukhin MD (1997) Biomass equations for sixty-five North American tree species. For Ecol Manage 97:1–24. https://doi.org/10.1016/S0378-1127(97)00019-4

    Article  Google Scholar 

  84. Zianis D, Suomen Metsätieteellinen Seura, Metsäntutkimuslaitos (2005) Biomass and stem volume equations for tree species in Europe. Finnish Society of Forest Science, Finnish Forest Research Institute, Helsinki, Finland

    Google Scholar 

  85. Paul KI, Roxburgh SH, England JR et al (2013) Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. For Ecol Manage 310:483–494. https://doi.org/10.1016/j.foreco.2013.08.054

    Article  Google Scholar 

  86. Oliveira TS, Tomé M (2017) Improving biomass estimation for Eucalyptus globulus Labill at stand level in Portugal. Biomass Bioenerg 96:103–111. https://doi.org/10.1016/j.biombioe.2016.11.010

    Article  Google Scholar 

  87. Paul KI, Roxburgh SH, Ritson P et al (2013) Testing allometric equations for prediction of above-ground biomass of mallee eucalypts in southern Australia. For Ecol Manage 310:1005–1015. https://doi.org/10.1016/j.foreco.2013.09.040

    Article  Google Scholar 

  88. Zabek LM, Prescott CE (2006) Biomass equations and carbon content of aboveground leafless biomass of hybrid poplar in Coastal British Columbia. For Ecol Manage 223:291–302. https://doi.org/10.1016/j.foreco.2005.11.009

    Article  Google Scholar 

  89. Henry M, Picard N, Trotta C et al (2011) Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fennica 45:477–569. https://doi.org/10.14214/sf.38

  90. Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer. FAO, Rome

    Google Scholar 

  91. Chave J, Andalo C, Brown S et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99. https://doi.org/10.1007/s00442-005-0100-x

    Article  Google Scholar 

  92. Cole TG, Ewel JJ (2006) Allometric equations for four valuable tropical tree species. For Ecol Manage 229:351–360. https://doi.org/10.1016/j.foreco.2006.04.017

    Article  Google Scholar 

  93. Hairiah K, Sitompul S (2001) Methods for sampling carbon stocks above and below ground. International Centre for Research in Agroforestry, Bogor, Indonesia

    Google Scholar 

  94. Terakunpisut J, Gajaseni N, Ruankawe N (2007) Carbon sequestration potential in aboveground biomass of Thong Pha Phum national forest, Thailand. Appl Ecol Environ Res 5:93–102

    Article  Google Scholar 

  95. Feldpausch TR, Lloyd J, Lewis SL et al (2012) Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9:3381–3403. https://doi.org/10.5194/bg-9-3381-2012

    Article  Google Scholar 

  96. Vieilledent G, Vaudry R, Andriamanohisoa SF et al (2012) A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecol Appl 22:572–583. https://doi.org/10.1890/11-0039.1

    Article  Google Scholar 

  97. Mattsson E, Ostwald M, Wallin G, Nissanka SP (2016) Heterogeneity and assessment uncertainties in forest characteristics and biomass carbon stocks: important considerations for climate mitigation policies. Land Use Policy 59:84–94. https://doi.org/10.1016/j.landusepol.2016.08.026

    Article  Google Scholar 

  98. Chave J, Réjou-Méchain M, Búrquez A et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190. https://doi.org/10.1111/gcb.12629

    Article  Google Scholar 

  99. Holdridge LR, Tosi, Jr JA (1967) Life zone ecology. Tropical Science Center, San Jose, Costa Rica

    Google Scholar 

  100. Henry M, Bombelli A, Trotta C, et al (2013) GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment. iForest Biogeosci Forest 6:326–330. https://doi.org/10.3832/ifor0901-006

  101. Feldpausch TR, Banin L, Phillips OL et al (2011) Height-diameter allometry of tropical forest trees. Biogeosciences 8:1081–1106. https://doi.org/10.5194/bg-8-1081-2011

    Article  Google Scholar 

  102. Falster DS, Duursma RA, Ishihara MI et al (2015) BAAD: a biomass and allometry database for woody plants: ecological archives E096–128. Ecology 96:1445–1445. https://doi.org/10.1890/14-1889.1

    Article  Google Scholar 

  103. Jagodziński AM, Dyderski MK, Gęsikiewicz K, Horodecki P (2019) Effects of stand features on aboveground biomass and biomass conversion and expansion factors based on a Pinus sylvestris L. chronosequence in Western Poland. Eur J Forest Res 138:673–683. https://doi.org/10.1007/s10342-019-01197-z

    Article  Google Scholar 

  104. Neumann M, Moreno A, Mues V et al (2016) Comparison of carbon estimation methods for European forests. For Ecol Manage 361:397–420. https://doi.org/10.1016/j.foreco.2015.11.016

    Article  Google Scholar 

  105. Teobaldelli M, Somogyi Z, Migliavacca M, Usoltsev VA (2009) Generalized functions of biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index. For Ecol Manage 257:1004–1013. https://doi.org/10.1016/j.foreco.2008.11.002

    Article  Google Scholar 

  106. Somogyi Z, Cienciala E, Mäkipää R et al (2007) Indirect methods of large-scale forest biomass estimation. Eur J Forest Res 126:197–207. https://doi.org/10.1007/s10342-006-0125-7

    Article  Google Scholar 

  107. Kangas A, Maltamo M (2006) Forest inventory: methodology and applications. Springer, Dordrecht

    Book  Google Scholar 

  108. Chen L, Wang Y, Ren C et al (2019) Optimal combination of predictors and algorithms for forest above-ground biomass mapping from sentinel and SRTM data. Remote Sensing 11:414. https://doi.org/10.3390/rs11040414

    Article  Google Scholar 

  109. Chen L, Ren C, Zhang B et al (2018) Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests 9:582. https://doi.org/10.3390/f9100582

    Article  Google Scholar 

  110. Propastin P (2012) Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. Int J Appl Earth Obs Geoinf 18:82–90. https://doi.org/10.1016/j.jag.2011.12.013

    Article  Google Scholar 

  111. Qi YJ, Zhang YC, Wang K et al (2020) Application of spatial regression models for forest biomass estimation in Guizhou province, southwest China. Appl Ecol Environ Res 18:7215–7232. https://doi.org/10.15666/aeer/1805_72157232

  112. Chirici G, Giannetti F, McRoberts RE et al (2020) Wall-to-wall spatial prediction of growing stock volume based on Italian national forest inventory plots and remotely sensed data. Int J Appl Earth Obs Geoinf 84:101959. https://doi.org/10.1016/j.jag.2019.101959

    Article  Google Scholar 

  113. Chirici G, Mura M, McInerney D et al (2016) A meta-analysis and review of the literature on the k-nearest neighbors technique for forestry applications that use remotely sensed data. Remote Sens Environ 176:282–294. https://doi.org/10.1016/j.rse.2016.02.001

    Article  Google Scholar 

  114. Chirici G, Barbati A, Corona P et al (2008) Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems. Remote Sens Environ 112:2686–2700. https://doi.org/10.1016/j.rse.2008.01.002

    Article  Google Scholar 

  115. McRoberts RE (2009) Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sens Environ 113:489–499. https://doi.org/10.1016/j.rse.2008.06.015

    Article  Google Scholar 

  116. McRoberts RE, Gobakken T, Næsset E (2012) Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications. Remote Sens Environ 125:157–166. https://doi.org/10.1016/j.rse.2012.07.002

    Article  Google Scholar 

  117. Tomppo EO, Gagliano C, Natale FD et al (2009) Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sensing Environ 18

    Google Scholar 

  118. Foody GM, Cutler ME, McMorrow J et al (2001) Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Glob Ecol Biogeogr 10:379–387. https://doi.org/10.1046/j.1466-822X.2001.00248.x

    Article  Google Scholar 

  119. Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29:617–663. https://doi.org/10.1080/01431160701352154

    Article  Google Scholar 

  120. Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

    Article  Google Scholar 

  121. Saatchi SS, Harris NL, Brown S et al (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci 108:9899–9904

    Article  Google Scholar 

  122. Blackard J, Finco M, Helmer E et al (2008) Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens Environ 112:1658–1677. https://doi.org/10.1016/j.rse.2007.08.021

    Article  Google Scholar 

  123. Carreiras JMB, Vasconcelos MJ, Lucas RM (2012) Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sens Environ 121:426–442. https://doi.org/10.1016/j.rse.2012.02.012

    Article  Google Scholar 

  124. Ford SE, Keeton WS (2017) Enhanced carbon storage through management for old-growth characteristics in northern hardwood-conifer forests. Ecosphere 8:e01721. https://doi.org/10.1002/ecs2.1721

    Article  Google Scholar 

  125. Lin D, Anderson-Teixeira KJ, Lai J et al (2016) Traits of dominant tree species predict local scale variation in forest aboveground and topsoil carbon stocks. Plant Soil 409:435–446. https://doi.org/10.1007/s11104-016-2976-0

    Article  Google Scholar 

  126. Powell SL, Cohen WB, Healey SP et al (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114:1053–1068. https://doi.org/10.1016/j.rse.2009.12.018

    Article  Google Scholar 

  127. Saatchi SS, Houghton RA, Dos Santos Alvalá RC et al (2007) Distribution of aboveground live biomass in the Amazon basin: AGLB IN THE AMAZON BASIN. Glob Change Biol 13:816–837. https://doi.org/10.1111/j.1365-2486.2007.01323.x

    Article  Google Scholar 

  128. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  129. Chi H, Sun G, Huang J et al (2017) Estimation of forest aboveground biomass in changbai mountain region using ICESat/GLAS and Landsat/TM Data. Remote Sens 9:707. https://doi.org/10.3390/rs9070707

    Article  Google Scholar 

  130. Waser L, Ginzler C, Rehush N (2017) Wall-to-wall tree type mapping from countrywide airborne remote sensing surveys. Remote Sens 9:766. https://doi.org/10.3390/rs9080766

    Article  Google Scholar 

  131. Axelsson C, Skidmore AK, Schlerf M et al (2013) Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression. Int J Remote Sens 34:1724–1743. https://doi.org/10.1080/01431161.2012.725958

    Article  Google Scholar 

  132. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  133. Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

    Article  Google Scholar 

  134. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88

    Article  MathSciNet  Google Scholar 

  135. Giannetti F, Barbati A, Mancini LD et al (2018) European forest types: toward an automated classification. Ann For Sci 75:6. https://doi.org/10.1007/s13595-017-0674-6

    Article  Google Scholar 

  136. Waser L, Fischer C, Wang Z, Ginzler C (2015) Wall-to-wall forest mapping based on digital surface models from image-based point clouds and a NFI forest definition. Forests 6:4510–4528. https://doi.org/10.3390/f6124386

    Article  Google Scholar 

  137. Irulappa-Pillai-Vijayakumar DB, Renaud J-P, Morneau F et al (2019) Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators. Remote Sensing 11:991. https://doi.org/10.3390/rs11080991

    Article  Google Scholar 

  138. Nilsson M, Nordkvist K, Jonzén J et al (2017) A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory. Remote Sens Environ 194:447–454. https://doi.org/10.1016/j.rse.2016.10.022

    Article  Google Scholar 

  139. Tomppo E, Halme M (2004) Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach. Remote Sens Environ 92:1–20. https://doi.org/10.1016/j.rse.2004.04.003

    Article  Google Scholar 

  140. Chave J, Condit R, Aguilar S et al (2004) Error propagation and scaling for tropical forest biomass estimates. Phil Trans R Soc Lond B 359:409–420. https://doi.org/10.1098/rstb.2003.1425

    Article  Google Scholar 

  141. Cohen R, Kaino J, Okello JA et al (2013) Propagating uncertainty to estimates of above-ground biomass for Kenyan mangroves: a scaling procedure from tree to landscape level. For Ecol Manage 310:968–982. https://doi.org/10.1016/j.foreco.2013.09.047

    Article  Google Scholar 

  142. Lu D, Chen Q, Wang G et al (2012) Aboveground forest biomass estimation with landsat and LiDAR data and uncertainty analysis of the estimates. Int J Forest Res 2012:1–16. https://doi.org/10.1155/2012/436537

    Article  Google Scholar 

  143. McRoberts RE, Westfall JA (2014) Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Sci 60:34–42. https://doi.org/10.5849/forsci.12-141

    Article  Google Scholar 

  144. Shao Z, Zhang L (2016) Estimating forest aboveground biomass by combining optical and SAR data: a case study in Genhe, Inner Mongolia, China. Sensors 16:834. https://doi.org/10.3390/s16060834

    Article  Google Scholar 

  145. Wang G, Oyana T, Zhang M et al (2009) Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images. For Ecol Manage 258:1275–1283

    Article  Google Scholar 

  146. Clutter JL, Fortson JC, Pienaar LV et al (1983) Timber managment: a quantitative approach. John Wiley & Sons, New York

    Google Scholar 

  147. Myers RH (1986) Classical and modern regression with applications. Duxbury Press, Boston

    Google Scholar 

  148. Ståhl G, Holm S, Gregoire TG et al (2011) Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway. Can J For Res 41:96–107. https://doi.org/10.1139/X10-161

    Article  Google Scholar 

  149. Ståhl G, Heikkinen J, Petersson H et al (2014) Sample-based estimation of greenhouse gas emissions from forests—a new approach to account for both sampling and model errors. Forest Sci 60:3–13. https://doi.org/10.5849/forsci.13-005

    Article  Google Scholar 

  150. Fu Y, Lei Y, Zeng W et al (2017) Uncertainty assessment in aboveground biomass estimation at the regional scale using a new method considering both sampling error and model error. Can J For Res 47:1095–1103. https://doi.org/10.1139/cjfr-2016-0436

    Article  Google Scholar 

  151. McRoberts RE, Næsset E, Gobakken T (2013) Inference for lidar-assisted estimation of forest growing stock volume. Remote Sens Environ 128:268–275. https://doi.org/10.1016/j.rse.2012.10.007

    Article  Google Scholar 

  152. McRoberts RE, Chen Q, Domke GM et al (2016) Hybrid estimators for mean aboveground carbon per unit area. Ecol Manage 378:44–56. https://doi.org/10.1016/j.foreco.2016.07.007

    Article  Google Scholar 

  153. Thiel M, Basiliko N, Caspersen J et al (2015) Operational biomass recovery of small trees: equations for six central Ontario tree species. Can J For Res 45:372–377. https://doi.org/10.1139/cjfr-2014-0429

    Article  Google Scholar 

Download references

Funding

This work is funded by National Funds through FCT - Foundation for Science and Technology under the Project UIDB/05183/2020.

Author information

Authors and Affiliations

  1. MED—Mediterranean Institute for Agriculture, Environment and Development & CHANGE—Global Change and Sustainability Institute, Instituto de Investigação e Formação Avançada, Departamento de Engenharia Rural, Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-544, Évora, Portugal

    Ana Cristina Gonçalves

Authors
  1. Ana Cristina Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Cristina Gonçalves .

Editor information

Editors and Affiliations

  1. MED—Mediterranean Institute for Agriculture, Environment and Development and CHANGE—Global Change and Sustainability Institute, Instituto de Investigação e Formação Avançada, Departamento de Engenharia Rural, Escola de Ciências e Tecnologia, University of Évora, Évora, Portugal

    Ana Cristina Gonçalves

  2. IDMEC, Escola de Ciências e Tecnologia, Departamento de Engenharia Mecatrónica, Universidade de Évora, Évora, Portugal

    Isabel Malico

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gonçalves, A.C. (2024). Modelling Biomass. In: Gonçalves, A.C., Malico, I. (eds) Forest Bioenergy. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-48224-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48224-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48223-6

  • Online ISBN: 978-3-031-48224-3

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation