my thesis defence at copenhagen university
TRANSCRIPT
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Site Index and Height Growth Modelsfor Japanese Larch & miscellaneous
Larch Species in Denmark
By:
Bidya Nath JhaSUFONAMA M.Sc. Student (EMN 08001)
Thesis SupervisorDr. Thomas Nord-Larsen
Senior Research Scientist
Faculty of Life Sciences, University of Copenhagen
July, 2009
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1. Research Context: Problem &Justifications
1.2 Site Index Model for Larch sp. does not exist inDenmark
Andersen, 1950.........Site B Japanese Larch Schober, 1975.............German Yield Table
1.3 Existing yield table Ht. growth relation havemethodological limitation:
Graphical interpretation
Statistical objectivity, flexibility or measure: No
1.1 Larch: An Introduction & Importance.
Adaptation Growth, Environment and Economy Demand and Deficit
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2. Research Objectives
2.1. To develop dynamic site index models forJapanese larch and miscellaneous larch species inDenmark
2.2. To compare the predictive performance of thedeveloped models with conventional height growthmodels for Japanese larch and miscellaneous larchspecies
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Variability of forest sites in theirproductive capacity
Interaction abiotic factors (soil,climate) with biotic factors (biota)
forest growth = f (site factors, time)
Dominant Height as an indicator ofsite productivity
Theoretical
Background
Practical Process for
Model development
Volume growth is the best indicator ofproductivity, but is impacted by
management input.
Establishment of Age Height
Relationship for a given species andsite from periodic growth data
Global and local parameter Estimationfor given function to establish such
relationship
Testing the predictive strength ofgiven relationship (model)
Model application and continuousmonitoring and evaluation
Height growth is least impacted bymanagement inputs
Conceptual Research Frameworkfor site index modelling
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3. Data for Modelling
Summary of data used for site index and height growth
modelling.Species No. of
Experimen
ts
No. of
Plots
Mean
Plot
Area
(ha)
Mean no.
of
Measurem
ent per
plot
Period of
records
(Years)
Jap. larch 19 25 0.1528 9 1918-
2008
Misc
.larch
23 33 0.1505 11 1918-
2008
Data Collection
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4. Methods
4.1. Data Preparation
Regression for the Height (Naslund, 1936; Johannsen,2002)
Dominant Height Calculation (H100)
Definition= 100 thickest trees/hae.g. plot area =0.2 ha; then 0.2*100 = 20thickest trees
Age from records
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4. Methods
4.1. Algebraic Difference Approach-ADA(Bailey & Clutter,1974) 1) Identification of suitable model:
2) Choose and solve for a site parameter:
3) Substitute the solution for the parameter:
Approach and Equations
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4.3. Selected Mathematical Functions:
Model I
Model II
Model III
Model IV
Model V
Model IV.1
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4. Methods
Model Development and
Testing4.4. PROC MODEL SAS 9.3.1.
Indicator Variable Method forSimultaneous estimation of site indexes
and model parameter; PROC MODEL
4.5. Model Evaluation Performance Criteria Residual Diagnostics Linear Regression of Observed vs.Predicted Values Leave-one-out Cross Evaluation
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Criteria Formula Ideal
1. SSE 0
2. MSE 0
3. RMSE 0
4. R-Square 1
5. VR 1
6. MRes 0
7. |MRes| 0
8. RRes 0
9. IRRes 0
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5. Results & Discussion
Estimates of Site Index (S), height in meter atage 50 years
Model
(Equation)
Mean Maximum Minimum Standard
Deviation
Model I 23.5160 27.1972 17.4593 2.5565
Model II 23.8160 26.5424 19.5444 1.9471
Model III 23.9858 26.4363 19.6324 1.6215
Model IV 23.5716 26.7320 18.5599 2.2114
Model IV.1 23.5791 26.7266 18.6240 2.1934
Model V 23.5057 26.9726 17.8843 2.3907
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Model IV.1
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Linear Regression of Observed vs. PredictedValues
Slope & intercept values very close to 1 and zerorespectively Simultaneous F tests reject the hypothesis R-square above 97%
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OLS Assumptions
1. Independence of Residuals
First order autoregressive Model Structure was used; error
term was expanded as
2. Normality of Residuals
Statistical and graphical interpretation of model residuals Kolmogorov-Smirnov, Anderson-Darling and Shapiro-Wilk tests Visual Interpretation
3. Homoscedasticity ( Constant Variances) Statistical and graphical interpretation of model residuals White Tests Visual Interpretation
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Miscellaneous Larch Model:
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Model IV (solid lines) and model IV.1 (dashed
lines) for Jap. larch
6. Comparisons of models
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Japanese Larch (blue-dotted) andMiscellaneous Larch (black-smooth)
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Model IV.1 and Danish yield table age-ht.relation for Jap. larch (Andersen, 1950)
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Model IV.1 and German yield table age-ht.relation for Jap. larch (Schober, 1975)
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Model IV.1 and other models for Jap. larchfrom different countries and contents
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7.Conclusions
Dynamic site index and height growth models are
developed for Jap. larch and misc. larch species inDenmark.They found to be predicting dominant height without any
apparent bias. Cieszewski models performed better than selected
Chapman Richards function. Traditional Japanese larch models were found to be
predicting slightly higher than the developed models.
Comparison of miscellaneous larch models with other
species specific models produce contradicting results. Japanese larch models can be applied in Danish and/or
adjacent countries, for miscellaneous larch models external
validation before wide applications will be a good
recommendation.
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