Appendix D — Additional Reading

Allometric Uncertainty

  • Aholoukpè, H. N. S., Dubos, B., Deleporte, P., Flori, A., Amadji, L. G., Chotte, J.-L., & Blavet, D. (2018). Allometric equations for estimating oil palm stem biomass in the ecological context of benin, west africa. Trees, 32(6), 1669–1680.
  • Andersen, H.-E., Reutebuch, S. E., & McGaughey, R. J. (2006). A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Canadian Journal of Remote Sensing, 32(5), 355–366. https://doi.org/10.5589/m06-030
  • Baskerville, G. (1972). Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research, 2(1), 49–53.
  • Duncanson, L., Disney, M., Armston, J., Nickeson, J., Minor, D., & Camacho, F. (2021). Aboveground woody biomass product validation good practices protocol. https://doi.org/10.5067/DOC/CEOSWGCV/LPV/AGB.001
  • Dutcă, I., Stăncioiu, P. T., Abrudan, I. V., & Ioraș, F. (2018). Using clustered data to develop biomass allometric models: The consequences of ignoring the clustered data structure. PloS One, 13(8), e0200123.
  • Martin, A. (2022). Accuracy and precision in urban forestry tools for estimating total tree height. Arboric. Urban For, 48(6), 319–332.
  • Martı́nez-Sánchez, J. L., Martı́nez-Garza, C., Cámara, L., & Castillo, O. (2020). Species-specific or generic allometric equations: Which option is better when estimating the biomass of mexican tropical humid forests? Carbon Management, 11(3), 241–249.
  • McRoberts, R. E., & Westfall, J. A. (2016). Propagating uncertainty through individual tree volume model predictions to large-area volume estimates. Annals of Forest Science, 73(3), 625–633. https://doi.org/10.1007/s13595-015-0473-x
  • Nickless, A., Scholes, R. J., & Archibald, S. (2011). A method for calculating the variance and confidence intervals for tree biomass estimates obtained from allometric equations. South African Journal of Science, 107(5), 1–10.
  • Ojoatre, S., Zhang, C., Hussin, Y. A., Kloosterman, H. E., & Ismail, M. H. (2019). Assessing the uncertainty of tree height and aboveground biomass from terrestrial laser scanner and hypsometer using airborne LiDAR data in tropical rainforests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 4149–4159.
  • Parresol, B. R. (1993). Modeling multiplicative error variance: An example predicting tree diameter from stump dimensions in baldcypress. Forest Science, 39(4), 670–679.
  • Picard, N., Bosela, F. B., & Rossi, V. (2015). Reducing the error in biomass estimates strongly depends on model selection. Annals of Forest Science, 72(6), 811–823. https://doi.org/10.1007/s13595-014-0434-9
  • Picard N., Saint-André L., Henry M. 2012. Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Food and Agricultural Organization of the United Nations, Rome, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier, 215 pp.
  • Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., & al., et. (2020). Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications, 11(1), 4540.
  • Roxburgh, S., Paul, K., Clifford, D., England, J., & Raison, R. (2015). Guidelines for constructing allometric models for the prediction of woody biomass: How many individuals to harvest? Ecosphere (Washington, D.C), 6(3), 1–27.
  • Shang, Y., Xia, Y., Ran, X., Zheng, X., Ding, H., & Fang, Y. (2025). Allometric equations for aboveground biomass estimation in natural forest trees: Generalized or species-specific? Diversity, 17(7), 493.
  • Vorster, A. G., Evangelista, P. H., Stovall, A. E., & Ex, S. (2020). Variability and uncertainty in forest biomass estimates from the tree to landscape scale: The role of allometric equations. Carbon Balance and Management, 15(1), 8.
  • Wayson, C. A., Johnson, K. D., Cole, J. A., Olguín, M. I., Carrillo, O. I., & Birdsey, R. A. (2015). Estimating uncertainty of allometric biomass equations with incomplete fit error information using a pseudo-data approach: methods. Annals of Forest Science, 72(6), 825–834.
  • White, G. W., Yamamoto, J. K., Elsyad, D. H., Schmitt, J. F., Korsgaard, N. H., Hu, J. K., Gaines III, G. C., Frescino, T. S., & McConville, K. S. (2025). Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data. Canadian Journal of Forest Research, 55, 1–19.
  • Yanai, R. D., Battles, J. J., Richardson, A. D., Blodgett, C. A., Wood, D. M., & Rastetter, E. B. (2010). Estimating uncertainty in ecosystem budget calculations. Ecosystems (New York, N.Y.), 13(2), 239–248. https://doi.org/10.1007/s10021-010-9315-8
  • Yokelson, R.J., et al. (2013). Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires. Atmospheric Chemistry and Physics, 13, 89-116.
  • Zapata-Cuartas, M., Sierra, C. A., & Alleman, L. (2012). Probability distribution of allometric coefficients and bayesian estimation of aboveground tree biomass. Forest Ecology and Management, 277, 173–179.

Emission Factor Uncertainty

  • Andreae, M.O. (2019). Emission of trace gases and aerosols from biomass burning – an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. doi:10.5194/acp-19-8523-2019
  • Brown, J.K. (1974). Handbook for inventorying downed woody material. USDA Forest Service General Technical Report INT-16.
  • IPCC. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Agriculture, Forestry and Other Land Use, Vol. 4). Intergovernmental Panel on Climate Change. https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html
  • IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use. Intergovernmental Panel on Climate Change.
  • Köhler, P., & Huth, A. (2010). Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences (Online), 7(8), 2531–2543.
  • Pelletier, J., Martin, D., & Potvin, C. (2013). REDD+ emissions estimation and reporting: Dealing with uncertainty. Environmental Research Letters, 8(3), 034009.
  • Pelletier, J., Busch, J., & Potvin, C. (2015). Addressing uncertainty upstream or downstream of accounting for emissions reductions from deforestation and forest degradation. Climatic Change, 130(4), 635-648
  • Pelletier, N., Thiagarajan, A., Durnin-Vermette, F., Liang, B. C., Choo, D., Cerkowniak, D., … & VandenBygaart, A. J. (2025). Approximate Bayesian inference for calibrating the IPCC tier-2 steady-state soil organic carbon model for Canadian croplands using long-term experimental data. Environmental Modelling & Software, 190, 106481
  • Seiler, W., & Crutzen, P.J. (1980). Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change, 2(3), 207-247.
  • van Leeuwen, T.T., & van der Werf, G.R. (2011). Spatial and temporal variability in the ratio of trace gases emitted from biomass burning. Atmospheric Chemistry and Physics, 11, 3611-3629.

Activity Data Uncertainty

  • Butler, B. J., Sass, E. M., Gamarra, J. G., Campbell, J. L., Wayson, C., Olguín, M., Carrillo, O., & Yanai, R. D. (2024). Uncertainty in REDD+ carbon accounting: A survey of experts involved in REDD+ reporting. Carbon Balance and Management, 19(1), 22.
  • Chen, Q., Laurin, G. V., & Valentini, R. (2015). Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels. Remote Sensing of Environment, 160, 134–143. https://doi.org/10.1016/j.rse.2015.01.009
  • GOFC-GOLD (2016). Integration of remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and Guidance from the Global Forest Observations Initiative. Edition 2.0. Rome: Food and Agriculture Organization.
  • GOFC-GOLD (2016). A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals associated with deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation. GOFC-GOLD Report version COP22-1. Alberta, Canada: GOFC-GOLD Land Cover Project Office.
  • Köhler, P., & Huth, A. (2010). Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences, 7(8), 2531–2543.
  • Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., & Wulder, M.A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.
  • Pontius Jr., R.G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429.
  • Sheng, J., Zhou, W., & De Sherbinin, A. (2018). Uncertainty in estimates, incentives, and emission reductions in REDD+ projects. International Journal of Environmental Research and Public Health, 15(7), 1544.
  • Stehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. International Journal of Remote Sensing, 35(13), 4923-4939.

Monte Carlo Methods

  • Holdaway, R. J., McNeill, S. J., Mason, N. W. H., & Carswell, F. E. (2014). Propagating uncertainty in plot-based estimates of forest carbon stock and carbon stock change. Ecosystems (New York, N.Y.), 17(4), 627–640. https://doi.org/10.1007/s10021-014-9749-5
  • Keller, M., Palace, M., & Hurtt, G. (2001). Biomass estimation in the tapajos national forest, brazil. Forest Ecology and Management, 154(3), 371–382.
  • Molto, Q., Rossi, V., & Blanc, L. (2013). Error propagation in biomass estimation in tropical forests. Methods in Ecology and Evolution, 4(2), 175–183. https://doi.org/10.1111/j.2041-210x.2012.00266.x
  • Yanai, R. D., Battles, J. J., Richardson, A. D., Blodgett, C. A., Wood, D. M., & Rastetter, E. B. (2010). Estimating uncertainty in ecosystem budget calculations. Ecosystems, 13(2), 239–248. https://doi.org/10.1007/s10021-010-9315-8

Biostatistical Theory

  • Buchanan, M. (2000). Ubiquity: Why Catastrophes Happen. Three Rivers Press.
  • Mandelbrot, B. B., & Hudson, R. L. (2004). The Misbehavior of Markets: A Fractal View of Financial Turbulence. Basic Books.
  • Strogatz, S. H. (2003). Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Life. Hyperion.
  • Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.

IPCC Guidelines

The table lists the equations, tables and decision trees in the IPCC guidelines that the chapters draw on, with a page link for each.

Resource Description Source
IPCC 2006, Vol. 4
Ch.2 Generic Methodologies Eq.2.9 Calculation of biomass retention & growth post-conversion Link
Ch.3 Representation of Lands S.3.2 Six land-use categories recommended for estimating GHG emissions from LULC Link
IPCC 2019, Vol. 4
Ch.2 Generic Methodologies Eq 2.25 Annual SOC stock change in mineral soils Link
Tbl.2.3 Default reference condition of SOC stocks to soil & climate Link
Ch.3 Representation of Lands Tb.3.1 List of IPCC categories: land, climate, soil, mgt, activity Link
Pg.3.1 Tier 1 sampling approaches decision tree Link
Tb.3.6X Approach 1-3 to IPCC land-use classification & sampling Link
Tb.3.4 Approach 2 land change matrix to avoid double-counting Link
Pg.3A5 Climate zone delineation & updated datasets Link
Tb.3A.1 Global land-cover datasets listed by IPCC in 2017 Link
Ch.4 Forest Land Tb.4.4 R:S below to above-ground biomass ratio by climate & region Link
Ch.5 Cropland Tb.5.5 Relative stock change factors for mgt. activity in croplands Link
Tb.5.8 Default AGB carbon stocks retained on cropland in year 1 Link
Tb.5.10 Soil stock change factors for conversion to cropland Link
Ch.6 Grasslands Tbl 6.4 Default biomass stocks on converted grasslands Link
Ch.9 Other Land Ch.9: Near-zero SOC retention assigned to mining in “Other Lands” Link
IPCC 2013 Wetland Supplement Tb.1.1 Look-up table for wetlands by vegetation and soil type Link
IPCC 2023 AR6 Updated GWPs Tb.7.15 Updated GWPs for N₂O and fossil-specific CH₄ Link