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Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M . Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate, Univ. of Florida

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Page 1: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Forest SamplingSimulation

Loukas G. Arvanitis

University of Florida

Robin M. Reich

Colorado State University

Valentina S. Boycheva

Post-doctoral Associate, Univ. of Florida

Page 2: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

“It is the mark of an educated mind to rest satisfied with the degree of precision, which the nature of the subject admits, and not to seek exactness where only an approximation is possible.”

Aristotle (384-322 B.C.)

Page 3: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Main Objective

Assist students of all ages in:

• Designing

• Implementing

• Interpreting

COST-EFFECTIVE forest inventories

(Maximize information per unit cost)

Page 4: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Prerequisites

a) An introductory course in statistics.

b) Basic knowledge of computer simulation

c) Critical thinking

d) Perseverance

Page 5: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Basic Components of FOSS

• Spatial distribution of trees• Cost and precision constraints• Sampling units• Allocation of samples (sampling designs).

Page 6: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

FOSS

• Spatial pattern of trees

Random Aggregated Uniform (plantation type)

• Distribution of tree DBH Normal Weibull

Page 7: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Random Pattern

Page 8: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Aggregated Pattern

Page 9: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Uniform Pattern

Page 10: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Tree Gradients

• East - West

• North - South

• Combination of E-W and N-S

Page 11: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Sequence of Tasks

• Assess spatial pattern of the population• Decide on cost and precision constraints• Determine number, size, and shape of the

elementary sampling unit• Select sampling method• Decide on single or repeated sampling• Implement selected sampling method• Evaluate results

Page 12: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 13: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 14: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 15: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 16: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 17: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Elementary Sampling Units

– Square

– Rectangle

– Circle

– Strip

– Line

– Point

Page 18: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 19: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 20: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Available Sampling Designs

• Simple random• Systematic • Stratified (Proportional, Neyman, Optimal)• 3-P• Vertical Point• Vertical Line• Horizontal Line • Multi-phase• Multi-stage• List Sampling• Double sampling with regression

Page 21: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Vertical Point Sampling

Page 22: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Vertical Line Sampling

Page 23: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Horizontal Line Sampling

(Hush, Beers, & Kershaw, Jr. 2003)

Page 24: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Density Estimation

• Quadrat sampling is used in forestry, range, wildlife, and ecology to sample frequency, density, abundance, and presence.

• Distance sampling was developed primarily to study the spatial relationships that exist in biological populations.

Page 25: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Forest Variables of Interest in FOSS

• Mean/total basal area

• Mean/total volume

• Mean DBH

• Total tree height

Page 26: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 27: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

1

Page 28: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 29: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,
Page 30: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Selection of Sampling Units

• Most efficient: one that samples proportional to the variance of the stand parameter of interest.

– Density: fixed area plots– Basal Area and Volume: horizontal point sample

• Unbiased estimates of forest parameters can be obtained from any plot type and size.

• The precision and cost may vary significantly.

Page 31: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Selection of Sampling Units

• For a given sampling intensity, the smaller the sampling unit, the greater will be the precision because there will be more samples.

• However, large (n) will increase the cost of sampling.

• In general, the cost of sampling will be greater for a large number of sample units than for fewer samples of larger size.

Page 32: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Selection of Sampling Units

Cos

t $yS

Sample Size (n)

Page 33: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Selection of Sampling Units

• If sampling units are too small, the probability that they may be representative of the population decreases.

• Ultimately, the size of the sampling unit should be large enough to include a representative number of trees but small enough that the time required for measurements is not excessive.

Page 34: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Concluding Remarks

• FOSS: interactive program to enhance students’ comprehension of basic concepts on sampling forests in a cost effective manner.

• Main idea: assist students in advancing from dependent memorization to independent thinking and problem solving ability.

Page 35: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Concluding Remarks

• Allows students to explore a wide variety of alternative solutions through computer simulation, linking theory and practice.

• Gradients of different densities: East-West, N-S, and E-W /N-S, valuable for foresters,

ecologies, range specialists, and wildlife professionals.

Page 36: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Concluding Remarks

• Aims at student’s awareness of fundamental concepts related to sampling efficiency and the thinking process of maximizing the amount of information per unit effort or cost.

Page 37: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Concluding Remarks

• Assists students in becoming actively involved in the decision-making process (rewards and penalties) attributed to their actions.

• Although FOSS does not process field data, it is a powerful scientific tool for understanding, implementing, and properly interpreting forest sampling.

Page 38: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Concluding Remarks

FOSS works in the following operating systems:

• MS Windows NT

• MS Windows 2000

• MS Windows XP

Page 39: Forest Sampling Simulation Loukas G. Arvanitis University of Florida Robin M. Reich Colorado State University Valentina S. Boycheva Post-doctoral Associate,

Words of Wisdom

Jonathan Swift, an Irish-born satirist of the 17th century said that:

“A man should never be ashamed to own he had

been in the wrong, which is but saying, in other words, that he is wiser today than he was yesterday”.

THANK YOU FOR YOUR ATTENTION