Trees

, Volume 30, Issue 3, pp 935–947 | Cite as

Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data

  • Adewole Olagoke
  • Christophe Proisy
  • Jean-Baptiste Féret
  • Elodie Blanchard
  • François Fromard
  • Ulf Mehlig
  • Moirah Machado de Menezes
  • Valdenira Ferreira dos Santos
  • Uta Berger
Original Article

Abstract

Key message

We estimated aboveground biomass of large mangrove trees from terrestrial Lidar measurements. This makes the first attempt to extend mangrove biomass equations validity range to trunk diameter reaching 125 cm.

Abstract

Accurately determining biomass of large trees is crucial for reliable biomass analyses in most tropical forests, but most allometric models calibration are deficient in large trees data. This issue is a major concern for high-biomass mangrove forests, especially when their role in the ecosystem carbon storage is considered. As an alternative to the fastidious cutting and weighing measurement approach, we explored a non-destructive terrestrial laser scanning approach to estimate the aboveground biomass of large mangroves (diameters reaching up to 125 cm). Because of buttresses in large trees, we propose a pixel-based analysis of the composite 2D flattened images, obtained from the successive thin segments of stem point-cloud data to estimate wood volume. Branches were considered as successive best-fitted primitive of conical frustums. The product of wood volume and height-decreasing wood density yielded biomass estimates. This approach was tested on 36 A. germinans trees in French Guiana, considering available biomass models from the same region as references. Our biomass estimates reached ca. 90 % accuracy and a correlation of 0.99 with reference biomass values. Based on the results, new tree biomass model, which had R2 of 0.99 and RSE of 87.6 kg of dry matter. This terrestrial LiDAR-based approach allows the estimates of large tree biomass to be tractable, and opens new opportunities to improve biomass estimates of tall mangroves. The method could also be tested and applied to other tree species.

Keywords

Aboveground biomass Coastal blue carbon French Guiana Mangrove Terrestrial LiDAR Tree allometry 

Supplementary material

468_2015_1334_MOESM1_ESM.docx (795 kb)
Supplementary material 1 (DOCX 794 kb)

References

  1. Åkerblom M, Raumonen P, Kaasalainen M, Casella E (2015) Analysis of geometric primitives in quantitative structure models of tree stems. Remote Sens 7:4581–4603CrossRefGoogle Scholar
  2. Anthony EJ, Gardel A, Gratiot N, Proisy C, Allison MA, Dolique F, Fromard F (2010) The Amazon-influenced muddy coast of South America: a review of mud-bank-shoreline interactions. Earth Sci Rev 103:99–121. doi:10.1016/j.earscirev.2010.09.008 CrossRefGoogle Scholar
  3. Baltzer F, Allison M, Fromard F (2004) Material exchange between the continental shelf and mangrove-fringed coasts with special reference to the Amazon-Guianas coast. Mar Geol 208:115–126. doi:10.1016/j.margeo.2004.04.024 CrossRefGoogle Scholar
  4. Bastin JF et al (2015a) Seeing Central African forests through their largest trees. Scientific Reports 5:13156. doi:10.1038/srep13156 Google Scholar
  5. Bastin JF et al (2015b) Wood specific gravity variations and biomass of central african tree species: the simple choice of the outer wood. PLoS One 10:e0142146. doi:10.1371/journal.pone.0142146 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bayer D, Seifert S, Pretzsch H (2013) Structural crown properties of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in mixed versus pure stands revealed by terrestrial laser scanning. Trees 27:1035–1047. doi:10.1007/s00468-013-0854-4 CrossRefGoogle Scholar
  7. Béland M, Baldocchi DD, Widlowski J-L, Fournier RA, Verstraete MM (2014) On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR. Agric For Meteorol 184:82–97. doi:10.1016/j.agrformet.2013.09.005 CrossRefGoogle Scholar
  8. Brancheriau L, Lasaygues P, Debieu E, Lefebvre JP (2008) Ultrasonic tomography of green wood using a non-parametric imaging algorithm with reflected waves. Ann For Sci 65:712. doi:10.1051/forest:200851 CrossRefGoogle Scholar
  9. Calders K et al (2015) Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol Evol 6:198–208. doi:10.1111/2041-210x.12301 CrossRefGoogle Scholar
  10. Chave J et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99. doi:10.1007/s00442-0050100-x CrossRefPubMedGoogle Scholar
  11. Chave J et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol 20:3177–3190. doi:10.1111/gcb.12629 CrossRefPubMedGoogle Scholar
  12. Coops N, Hilker T, Wulder M, St-Onge B, Newnham G, Siggins A, Trofymow JA (2007) Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 21:295–310. doi:10.1007/s00468-006-0119-6 CrossRefGoogle Scholar
  13. Dassot M, Colin A, Santenoise P, Fournier M, Constant T (2012) Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput Elect Agric 89:86–93. doi:10.1016/j.compag.2012.08.005 CrossRefGoogle Scholar
  14. Feliciano EA, Wdowinski S, Potts MD (2014) Assessing mangrove above-ground biomass and structure using terrestrial laser scanning: a case study in the Everglades National Park. Wetlands 34:955–968. doi:10.1007/s13157-014-0558-6 CrossRefGoogle Scholar
  15. Fromard F, Puig H, Mougin E, Marty G, Betoulle JL, Cadamuro L (1998) Structure, above-ground biomass and dynamics of mangrove ecosystems: new data from French Guiana. Oecologia 115:39–53. doi:10.1007/s004420050489 CrossRefGoogle Scholar
  16. Fromard F, Vega C, Proisy C (2004) Half a century of dynamic coastal change affecting mangrove shorelines of French Guiana. A case study based on remote sensing data analyses and field surveys. Mar Geol 208:265–280. doi:10.1016/j.margeo.2004.04.018 CrossRefGoogle Scholar
  17. Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:1–13. doi:10.1088/1748-9326/2/4/045023 Google Scholar
  18. Hackenberg J, Wassenberg M, Spiecker H, Sun D (2015) Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6:1274–1300CrossRefGoogle Scholar
  19. Hauglin M, Astrup R, Gobakken T, Næsset E (2013) Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features. Scand J For Res 28:456–469. doi:10.1080/02827581.2013.777772 CrossRefGoogle Scholar
  20. Hilker T, van Leeuwen M, Coops N, Wulder M, Newnham G, Jupp DB, Culvenor D (2010) Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees 24:819–832. doi:10.1007/s00468-010-0452-7 CrossRefGoogle Scholar
  21. Huang P, Pretzsch H (2010) Using terrestrial laser scanner for estimating leaf areas of individual trees in a conifer forest. Trees 24:609–619. doi:10.1007/s00468-010-0431-z CrossRefGoogle Scholar
  22. Imbert D, Rollet B (1989) Phytomasse aerienne et production primaire dans la mangrove du Grand Cul-de-Sac Marin (Guadeloupe, Antilles francaises). Bulletin d’Ecologie 20:27–39Google Scholar
  23. Kankare V et al (2013) Individual tree biomass estimation using terrestrial laser scanning. ISPRS J Photogramm Remote Sens 75:64–75. doi:10.1016/j.isprsjprs.2012.10.003 CrossRefGoogle Scholar
  24. Komiyama A, Poungparn S, Kato S (2005) Common allometric equations for estimating the tree weight of mangroves. J Trop Ecol 21:471–477. doi:10.1017/s0266467405002476 CrossRefGoogle Scholar
  25. Komiyama A, Ong JE, Poungparn S (2008) Allometry, biomass, and productivity of mangrove forests: a review. Aquat Bot 89:128–137. doi:10.1016/j.aquabot.2007.12.006 CrossRefGoogle Scholar
  26. Lindenmayer DB, Laurance WF, Franklin JF (2012) Global decline in large old trees. Science 338:1305–1306. doi:10.1126/science.1231070 CrossRefPubMedGoogle Scholar
  27. Lutz JA, Larson AJ, Freund JA, Swanson ME, Bible KJ (2013) The Importance of large-diameter trees to forest structural heterogeneity. PLoS ONE 8:e82784. doi:10.1371/journal.pone.0082784 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Nogueira EM, Nelson BW, Fearnside PM (2006) Volume and biomass of trees in central Amazonia: influence of irregularly shaped and hollow trunks. For Ecol Manag 227:14–21. doi:10.1016/j.foreco.2006.02.004 CrossRefGoogle Scholar
  29. Peters R, Vovides AG, Luna S, Grüters U, Berger U (2014) Changes in allometric relations of mangrove trees due to resource availability—a new mechanistic modelling approach. Ecol Model 283:53–61. doi:10.1016/j.ecolmodel.2014.04.001 CrossRefGoogle Scholar
  30. Pistorius T (2012) From RED to REDD+: the evolution of a forest-based mitigation approach for developing countries. Curr Opin Environ Sustain 4:638–645. doi:10.1016/j.cosust.2012.07.002 CrossRefGoogle Scholar
  31. Proisy C, Couteron P, Fromard F (2007) Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sens Environ 109:379–392. doi:10.1016/j.rse.2007.01.009 CrossRefGoogle Scholar
  32. Pueschel P, Newnham G, Rock G, Udelhoven T, Werner W, Hill J (2013) The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans. ISPRS J Photogramm Remote Sens 77:44–56. doi:10.1016/j.isprsjprs.2012.12.001 CrossRefGoogle Scholar
  33. Raumonen P et al (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520. doi:10.3390/rs5020491 CrossRefGoogle Scholar
  34. Rinn F, Schweingruber F-H, Schaer E (1996) RESISTOGRAPH and X-ray density charts of wood comparative evaluation of drill resistance profiles and X-ray density charts of different wood species. Holzforschung 50:303–311CrossRefGoogle Scholar
  35. Slik JWF et al (2013) Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Glob Ecol Biogeogr 22:1261–1271. doi:10.1111/geb.12092 CrossRefGoogle Scholar
  36. Sprugel D (1983) Correcting for bias in log-transformed allometric equations. Ecology 64:209–210. doi:10.1111/geb.12092 CrossRefGoogle Scholar
  37. Strahler AH et al (2008) Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®). Can J Remote Sens 34:S426–S440. doi:10.5589/m08-046 CrossRefGoogle Scholar
  38. Vanclay JK, Skovsgaard JP (1997) Evaluating forest growth models. Ecol Model 98:1–12. doi:10.1016/S0304-3800(96)01932-1 CrossRefGoogle Scholar
  39. Vogt J, Lin Y, Pranchai A, Frohberg P, Mehlig U, Berger U (2014) The importance of conspecific facilitation during recruitment and regeneration: a case study in degraded mangroves. Basic Appl Ecol 15:651–660. doi:10.1016/j.baae.2014.09.005 CrossRefGoogle Scholar
  40. Yu X, Liang X, Hyyppa J, Kankare V, Vastaranta M, Holopainen M (2013) Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds. Remote Sens Lett 4:344–353. doi:10.1080/2150704x.2012.734931 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Adewole Olagoke
    • 1
    • 2
    • 3
  • Christophe Proisy
    • 2
  • Jean-Baptiste Féret
    • 4
  • Elodie Blanchard
    • 2
  • François Fromard
    • 5
    • 6
  • Ulf Mehlig
    • 7
  • Moirah Machado de Menezes
    • 7
  • Valdenira Ferreira dos Santos
    • 8
  • Uta Berger
    • 1
  1. 1.Institute of Forest Growth and Computer SciencesTechnische UniversitätDresdenGermany
  2. 2.IRD, UMR-AMAPMontpellierFrance
  3. 3.Institut des sciences et industries du vivant et de l’environnement (AgroParisTech), Campus d’Agropolis InternationalMontpellierFrance
  4. 4.IRSTEA, UMR-TETISMontpellier Cedex 5France
  5. 5.INP, UPS, EcoLabUniversité de ToulouseToulouseFrance
  6. 6.CNRS, EcoLabToulouseFrance
  7. 7.Instituto de Estudos CosteirosUniversidade Federal do Pará, Campus de BragançaBragançaBrazil
  8. 8.Instituto de Pesquisas Científicas e Tecnológicas do Estado do Amapá (IEPA)MacapáBrazil

Personalised recommendations