Forest aboveground biomass estimates in a tropical rainforest in Madagascar: new insights from the use of wood specific gravity data
Tahiana Ramananantoandro , Herimanitra P. Rafidimanantsoa , Miora F. Ramanakoto
Journal of Forestry Research ›› 2015, Vol. 26 ›› Issue (1) : 47 -55.
Forest aboveground biomass estimates in a tropical rainforest in Madagascar: new insights from the use of wood specific gravity data
To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program (REDD+), accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on carbon stocks in aboveground biomass (AGB). Although wood specific gravity (WSG) is known to be an important variable in AGB estimates, there is currently a lack of data on WSG for Malagasy tree species. This study aimed to determine whether estimates of carbon stocks calculated from literature-based WSG values differed from those based on WSG values measured on wood core samples. Carbon stocks in forest biomass were assessed using two WSG data sets: (i) values measured from 303 wood core samples extracted in the study area, (ii) values derived from international databases. Results suggested that there is difference between the field and literature-based WSG at the 0.05 level. The latter data set was on average 16 % higher than the former. However, carbon stocks calculated from the two data sets did not differ significantly at the 0.05 level. Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter and tree height than to WSG. The choice of dataset should depend on the level of accuracy (Tier II or III) desired by REDD+. As higher levels of accuracy are rewarded by higher prices, species-specific WSG data would be highly desirable.
Biomass estimates / Carbon stocks / Data quality / Madagascar / REDD+ / Wood specific gravity
| [1] |
|
| [2] |
|
| [3] |
Bryan J, Shearman PL, Ash J, Kirkpatrick JB (2010) Estimating rainforest biomass stocks and carbon loss from deforestation and degradation in Papua New Guinea 1972–2002: best estimates, uncertainties and research needs. J Environ Manag 91:995–1001 |
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
Chave J, Condit R, Muller-Landau HC, Thomas SC, Ashton PS, Bunyavejchewin S, Co LL, Dattaraja HS, Davies SJ, Esufali S, Ewango CEN, Feeley KJ, Foster RB, Gunatilleke N, Gunatilleke S, Hall P, Hart TB, Hernandez C, Hubbell S.P, Itoh A, Kiratiprayoon S, LaFrankie JV, Loo de Lao S, Makana J, Noor MNS, Kassim AR, Samper C, Sukumar R, Suresh HS, Tan S, Thompson J, Tongco MDC, Valencia R, Vallejo M, Villa G, Yamakura T, Zimmermann JK, Losos EC (2008) Assessing evidence for a pervasive alteration in tropical tree communities. PLoS Biol 6 (3): e45 http://www.plosbiology.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pbio.0060045&representation=PDF. Accessed April 2013 |
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
MEF-Ministère de l’Environnement et de Forêts (2009) Quatrième rapport national de la convention sur la diversité biologique. MEF/UNEP, Madagascar, pp 1–120 |
| [20] |
Moutinho P, Schwartzman S (2005) Tropical deforestation and climate change. Pará : Instituto de Pesquisa Ambiental da Amazônia Belém, Environmental Defense, Washington DC, p 1–131 |
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed April 2013 |
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
/
| 〈 |
|
〉 |