My Thesis Defence at Copenhagen University

8/14/2019 My Thesis Defence at Copenhagen University

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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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My Thesis Defence at Copenhagen University