Evaluation of sampling strategies to estimate crown biomass

Poudel, Krishna P.; Temesgen, Hailemariam; Gray, Andrew N.

doi:10.1186/s40663-014-0025-0

Cited by 36 publications

(16 citation statements)

References 33 publications

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“…These statistical considerations led the author to conclude that simple allometric models based on a single predictor (typically, dbh) should be preferred to models with several non-independent predictors (such as dbh and height). On the other hand, a growing body of literature highlighted the importance of height-diameter ratios as critical determinants of biomass, both for biomass prediction and for designing sampling strategies of biomass (Temesgen et al, 2011Poudel et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Should tree biomass allometry be restricted to power models?

Picard

Rutishauser²,

Ploton

et al. 2015

Forest Ecology and Management

117

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Section: Introductionmentioning

confidence: 99%

Should tree biomass allometry be restricted to power models?

Picard

Rutishauser²,

Ploton

et al. 2015

Forest Ecology and Management

117

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“…The sampling methods were compared using relative bias ‫)ܤܴ(‬ and relative root mean square error ‫)ܧܵܯܴܴ(‬ (e.g., Temesgen 2003and Poudel et al 2015. For each site, sampling method, number of neighbors for the indexes definition and average number of trees in the fixed and variable radius plots, relative bias was computed as:…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Comparison of sampling methods for estimation of nearest-neighbor index values

2017

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Neighborhood-based indices such as mingling index and diameter differentiation are a set of diversity measures that are based on the relationship between a reference tree and a certain number of nearest neighbors (i.e., trees to which it has the lowest horizontal distance). Using stem-mapped data from eight headwater sites, we compared the relative bias and relative root mean square error (relative to the true mean of each site) of several different methods of choosing reference trees for calculation of diameter differentiation ([Formula: see text]) and species mingling ([Formula: see text]) index. Indices were defined using two, three, and four neighbors and methods for selection of the reference tree were random selection of a tree in a fixed-radius plot (FI), random selection of a tree in a variable-radius plot (VA), azimuth selection method (AZ), and nearest tree selection (NT). In general, the relative bias was lower than ±2.5% for [Formula: see text] and lower than ±10% for [Formula: see text] regardless of the method. The FI method consistently had the lowest relative bias and relative root mean squared error. The NT and AZ methods were second in terms of relative root mean squared error for [Formula: see text] and [Formula: see text], respectively. Simplicity of these two methods might outweigh their slightly worse performance.

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“…The biomass equations used for this study predict the proportion of AGB for the bole, bark, branch and foliage component (Poudel and Temesgen and Poudel et al 2015). These proportions can then be multiplied by an estimate of total tree AGB to obtain the AGB of each tree component.…”

Section: Modeling Biomassmentioning

confidence: 99%

“…Both the component equations and the total tree biomass equation were fit in separate systems of equations using the seemingly unrelated regression method (SUR) in SAS statistical software (SAS Institute Inc., v9.4). The four CRM component equations and the total tree equation used in our study are of the form Temesgen 2016 andPoudel et al 2015):…”

Section: Modeling Biomassmentioning

confidence: 99%

Examination of uncertainty in per unit area estimates of aboveground biomass using terrestrial LiDAR and ground data

2016

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Abstract:In estimating aboveground forest biomass (AGB), three sources of 15 error that interact and propagate include: (1) measurement error, the quality of the 16 tree-level measurement data used as inputs for the individual-tree equations; (2) 17 model error, the uncertainty about the equations of the individual trees; and (3) 18 sampling error, the uncertainty due to having obtained a probabilistic or 19 purposive sample, rather than a census, of the trees on a given area of forest land. 20Monte Carlo simulations were used to examine measurement, model and 21 sampling error, and to compare total uncertainty between models, and between a 22 phase-based terrestrial laser scanner (TLS) and traditional forest inventory 23 instruments. Input variables for the equations were diameter at breast height, total 24 tree height (defined the height from the uphill side of the tree to the tree top) and 25 height to crown base; these were extracted from the terrestrial LiDAR data. 26Relative contributions for measurement, model and sampling error were 5%, 70% 27 and 25%, respectively when using TLS, and 11%, 66% and 23%, respectively 28 when using the traditional inventory measurements as inputs into the models. We 29 conclude that the use of TLS can reduce measurement errors of AGB compared 30 to traditional measurement approaches. 31Keywords: Model error; sampling error; measurement error; Pacific Northwest 32 33

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Evaluation of sampling strategies to estimate crown biomass

Cited by 36 publications

References 33 publications

Should tree biomass allometry be restricted to power models?

Should tree biomass allometry be restricted to power models?

Comparison of sampling methods for estimation of nearest-neighbor index values

Examination of uncertainty in per unit area estimates of aboveground biomass using terrestrial LiDAR and ground data

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