Three-Dimensional Crown Mass Distribution via Copulas Dr. John A. Kershaw, Jr. Professor of Forest...
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Transcript of Three-Dimensional Crown Mass Distribution via Copulas Dr. John A. Kershaw, Jr. Professor of Forest...
Three-Dimensional Crown MassDistribution via Copulas
Dr. John A. Kershaw, Jr.Professor of Forest Mensuration/BiometricsFaculty of Forestry and Env. MgmtUniversity of New Brunswick
Copula
• [kop-yuh-luh]• something that connects or links together
Cupola
Genest, C. and MacKay, J. (1987). The Joy of Copulas: The Bivariate
Distributions with Uniform Marginals. American Statistician, 40, 280-283.
Gaussian Copula
• H(x,y) is a joint distribution• F(x) is the marginal distribution of x• G(y) is the marginal distribution of y• H(x,y) = Cx,y,p[Φ-1(x),Φ-1(y)]• Φ is the cumulative (Inverse) Normal distribution• p is the correlation between x and y• So dependence is specified in the same manner as
with a multivariate Normal, but, like all copulas, F() and G() can be any marginal distribution
HT-DBH Simulation Example
Western Hemlock Crown Data
• 42 western hemlock trees dissected standing• EVERY branch measured for height on stem,
azimuth, total length, green length, maximum branch width, and branch basal diameter
• 10% sample, stratified by height, dissected in 15 cm concentric bands and mass determined for current foliage, older foliage, current wood, and older wood
Of course I had a little bit of help from a Sidekick…
…and my “Fall Guy”
Crown Reconstruction
• Dissected branches used to build prediction system for all branches
• Total branch mass by component (current and older foliage, current and older wood – Kershaw and Maguire 1995 CJFR)
• Horizontal distribution by component (Kershaw and Maguire 1996 CJFR)
• Refitted to take advantage of nonlinear mixed effects models and SUR
Crown Reconstruction
Two Copula Approaches
• “Fitted” based on reconstructed branches• “Predicted” based on tree-level moment-
based parameter prediction
Crown Copula Requirements
• Vertical Marginal Distribution• Horizontal Marginal Distribution• Radial Marginal Distribution• Correlation Matrix• Separate Copula for each Component– Current and Older Foliage Mass– Current and Older Wood Mass
Vertical Distribution
Horizontal Distribution
Radial Distribution
Simulation via Normal Copula
• Generate m standard normal random variates of length n– rnorm()
• Correlate using partial correlation matrix and Choleski’s decomposition– Chol(X) :: X = A’A
• Strip off Normal marginals using Inverse Normal distribution– pnorm()
• Apply desired margin using the quantile for the distribution qDIST()
• The “rdpq”s in R makes this trivial (given a few custom tools)
Predicted Copula
• Estimated Kernel Density Distribution– Overall vertical distribution estimated using Reverse Weibull– Density “peaks” estimated using Wiley’s (1977) Site Index
and Height Growth models– Weibull Density distributed via Normal Distribution between
Density “peaks”• Horizontal Distribution recovered from tree-level mean
and CV predictions• Radial Distribution estimated using Voronoi polygon• Correlations sampled from copula distribution of
observed correlations
Predicted “Composite” Vertical Distribution
Voronoi Derived Radial Distribution
Predicted Copula
Goodness-of-fit Criterion
• Needed a Criterion that:– Could be expanded to 3 or more dimensions– Didn’t require binning– Applied to multivariate distributions with mixed
margins• Two-Sample n-Nearest Neighbor Approach
(Narsky 2008)
Two Sample n-Nearest Neighbors• Two Distributions
– Observed– Predicted
• Interested in how the two distributions conform to one another• Randomly select a point from the observed distribution• Determine distances to all other Observed and all Predicted points• Select the n nearest neighbors• Classify n neighbors as belonging to the Observed (i=1) or Predicted
(i=0) Distribution• I = Sum(i)/n• If the two distributions are the same I ≈ 0.50• I = 1 shows no conformity
Foliage Distributions
Plot Reconstruction
Framework for Analyzing LiDAR
• Copula decomposition of LiDAR– Extract tree locations– Develop a classification of LiDAR points into
foliage and wood– Extract the relative 3D distribution via a copula
• Use allometric equations to predict totals• Put them together to get mass distributions