Error propagation and scaling for tropical forest biomass estimates.

By Jérôme Chave, Richard S. Condit, Salomón Aguilar, Andrés Hernández, Suzanne Lao, Rolando Pẽrez. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2004

922 citations recorded by the source corpus.

Subfields: Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6662f84f954133b44f926f93b741fae29ef70f2b

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Citations in the corpus

Monitoring and estimating tropical forest carbon stocks: making REDD a reality (2007, background): “An extensive review of allometric equations concluded that the pan-tropic models were ‘the best available’ way to estimate forest biomass and recommended them over local allometric models that may be based on less than 100 destructively sampled trees (Chave et al 2004).”
Improved allometric models to estimate the aboveground biomass of tropical trees (2014, positive): “This plot-based uncertainty is larger than expected from a simple averaging because a few large trees contribute disproportionately to stand-level AGB (Chave et al., 2004).”
Variation in aboveground tree live biomass in a central Amazonian Forest: Effects of soil and topography (2006, positive): “, 2003), which generally lack replicates and are rarely randomly located in the landscape (Brown and Lugo, 1992; Clark and Clark, 2000; Keller et al., 2001; Clark, 2002; Chave et al., 2004).”
Role of Brazilian Amazon protected areas in climate change mitigation (2010, positive): “2 to include errors in biomass estimates (26).”
Reducing the error in biomass estimates strongly depends on model selection (2015, positive): “Because biomass exhibits positive spatial autocorrelation (Keller et al. 2001; Chave et al. 2003; Wagner et al. 2010), these K units are not true independent replicates.”
Allometric equations for aboveground biomass estimation of Olea europaea L. subsp. cuspidata in Mana Angetu Forest (2018, neutral): “However, the use of generalized equations can lead to a bias in estimating biomass for a particular species (Clark et al. 2001; Cairns et al. 2003; Chave et al. 2004; Litton, Sandquist, and Cordell 2006; Pilli, Anfodillo, and Carrer 2006) although recent approaches incorporating data on wood…”
Hybrid estimators for mean aboveground carbon per unit area (2016, positive): “The ratio of this value and the mean predicted wood density (Table 1) corresponds well with the overall ratio of the standard deviation to the mean of 0.10 assumed by Chave et al. (2004).”
Tree allometry and improved estimation of carbon stocks and balance in tropical forests (2005, methodology): “The general method for estimating AGB from tropical forest stands and for assessing error in these protocols is described in Chave et al. (2004), and Fig.”
Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data (2015, positive): “Substantial bias can be introduced if inappropriate allometric equations are used for volumes and AGB estimates (Chave et al., 2004).”
Above-ground biomass and productivity in a rain forest of eastern South America (2008, positive): “We also tested for a potential bias related to this assumption, namely, that large trees do not have a lower wood specific gravity than small ones, as it is the case in the BCI forest (Chave et al. 2004).”
Seeing the forest beyond the trees (2015, positive): “…for several reasons: (1) diversity in the structure, wood density and dynamics of tropical forests leads to complex and variable allometry (Chave et al., 2004); (2) natural and anthropogenic disturbances at various spatial and temporal scales add to forest heterogeneity (Espírito-Santo et…”