1. STT 2063 Forest Science and Management
5.0 Introduction to Vegetation Sampling
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2. 5.1 Sampling
Sampling is the process of selecting units (e.g., people,
plants) from a population of interest so that by studying
the sample we may fairly generalize our results back to
the population from which they were chosen.
Statistical population
Population
Biological population
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3. Statistical population
- The entire set of data of interest e.g. tree height, tree
diameter, no. of tree
Biological population
- Aggregation of individual organism of a single
species.
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4. Statistical sample
Sample
Physical sample
Statistical sample
- A portion of a larger set of data i.e. (the stat. Pop.)
- It’s part or subset of the population that is actually
measured.
Physical sample
- A portion, or subset of a collection of one or more
materials objects.
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5. Examples of statistical and physical sample
Example 1
Collect one–litre sample from pond  PS
 Collected a portion of the entire volume of pond
water.
Measurement of PH, Temp. or phosphate  SS
 Measurement made only from the collected portion
i.e. the one-litre sample.
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6. Example 2
Sample a vegetation of a forest  PS
 Small portion of all vegetation from a forest.
Volume of standing trees, basal area  SS
 Result only from a small portion of the entire forest.
- The sample is the group of (people, plant etc.) who you
select to be in your study.
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7. 5.2 Purpose of sampling?
• To draw conclusions about populations from samples.
• Enables us to determine a population`s characteristics
by directly observing only a portion (or sample) of the
population.
5.3 The importance of sampling
• Impossible to observe/measure all
• Time
• Cost - cheaper to observe a part rather than the whole
• Efficiency in resources (labour, logistics, infrastructure)
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8. 5.4 Types of Sampling
(i) Non-statistical sampling
(ii) Statistical sampling
Non-statistical sampling
Refers to collection of data/information by subjective
way/method.
Characteristics:
• Rely on skill, experience and thoroughness of
researchers.
• No constant or consistent sampling design.
• Reliability can not be determine
• Very low cost.
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9. Statistical sampling
Refers to data that are collected through scientific way.
It involves selection of samples.
Characteristics:
• Accepted scientifically.
• Selection of sample that represent the population.
• Reliability can be measured e.g. 95% or 99%
Advantages:
• Quick, because sample are used.
• Scope of study are wide
• High reliability.
• Data recorded are very systematic.
• Collection of data can be repeated at the same
reliability.
• Similar method can be employ at different site for
valid comparison. 9

10. 5.5 Vegetation sampling
Vegetation populations are relatively easy to sample
because:
 Vegetation doesn't move, hence as much time as
is necessary can be taken to make measurements
or observations.
 Vegetation is usually easily visible.
 Vegetation doesn't change much over short
periods of time.
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11. Few characteristics about vegetation do cause some
concern when sampling, namely:
 There may be difficulties in defining exactly what
constitutes a vegetation individual. For example, many
plants consist of multiple stems emerging from a single
root system interconnected by runners. Are these
multiple individuals or one individual?
 Vegetation often grows in patches or groups. Is the
patch or each plant in the patch the individual?
 Vegetation in one area can overlap, that is, exists in a
number of scales (e.g. grass, shrubs, under canopy
trees, canopy trees).
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12. 5.6 Vegetation Sampling Approaches
Most vegetation and forest sampling derives from one
of the following approaches.
• Use the individuals. If we are interested in
average tree height, we simply locate some trees in
the area of interest (usually chosen at random) and
measure the height of all selected trees.
• Use predefined areas. This is the basic idea behind
quadrate and strip quadrate sampling units. Often it
is easier to locate a randomly chosen study area than
to locate/select a random individual.
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13. • Use predefined lines. Lines may be used to identify
which individuals are to be observed/measured (line
intersect selection) or can themselves constitute the
measurement (line intercept sampling). Line can also
be used in conjunction with areas to locate study
individuals (point quarter centre or strip counts).
Line sampling methods are sometimes also referred to
as plotless sampling.
• Use distances between individuals. For
determining certain characteristics relating to the
distributions of individuals in space, individual-to-
individual distances or point-to-individual distances
may be used.
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14. 5.7 Experimental Design
 Method how samples are:
• Chosen to represent the population of study.
• Determination of no. or % area to be sampled.
• Determination of size, shape and method of data
collection.
 It is the research plan either in the field or laboratory
experiments.
 Done prior to data collection.
 It is a procedure for specific sampling and data
analyses that are determine using statistical basis.
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15. 5.8 Classification of sampling design
Design
Selective Objective
Systematic Random Cluster
Unstratified Stratified Simple Multistage
Simple Stratified Two > Two
Random Random stage stage
Sampling Sampling
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16. 5.8.1 Selective/Authoritative sampling
 Sample are selected in subjective manner.
 Depend very much on the skill, experience and
objective of individual researcher.
 Employed certain strategy during measurement in
the field.
Method:
•Samples are selected based on certain characteristics.
•Sampling area must contain the individuals or species
that are to be study.
•Data collection are easy; avoid areas that are difficult
such as steep slope, depression etc.
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17. 5.8.2 Objective sampling
 Involve random selection and probability sampling does.
 We know the odds or probability that we have
represented the population well. We are able to estimate
confidence intervals for the statistic.
 In Selective samples, we may or may not represent the
population well, and it will often be hard for us to know how
well we've done so.
 In general, researchers prefer random sampling methods
over selective ones, and consider them to be more accurate
and rigorous.
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18. 5.8.3 Random sampling
• Selection of sample are random i.e. every parts of the
population have the same chance to be selected as
sample.
• Avoid bias in choosing samples
- Samples randomly selected usually by random
numbers.
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19. 19

20. Method
- Area of study are divided into quadrates that have similar
size.
- Every quadrate are assigned with a number.
- Selection of samples must be based on random
numbers
- Selection of samples are done either with replacement
or without replacement.
- Mostly without replacement
- No. of samples are usually determined prior to random
selection.
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21. Simple random sampling &
Stratified random sampling
SIMPLE RANDOM SAMPLE STRATIFIED RANDOM SAMPLE
- all plot have the same - Population are divided into
probability to be chosen homogeneous group & samples are
or selected homo group.
- This will help to ensure that all
types are represented in the
overall sample.
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22. Multistage Random Sampling
• Sampling are done in stages. Usually involve
subsample in the main sample.
Main Sample
Sub sample
Double stage random sampling Random sampling > two stages
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23. 5.8.4 Systematic sampling
- Sampling with a system.
- From the sampling frame, a starting point is chosen at
random, and thereafter at regular intervals.
- Assumption: a population of interest in distributed
randomly over space.
- Samples are selected systematically where distribution.
- Samples are arranged I such a way that every part of
the population have the same number of samples.
- Easy to locate.
- Distribution of samples are uniform.
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24. For Forest Inventory systematic sampling is usually
chosen because:
(i) easily planned, (ii) faster in execution and mostly
cheaper; (iii) it gives better estimates of the mean; (iv)
it gives thus better precision compared to random
sampling
General characteristics:
• Formation Transect line.
• Study area is divided into quadrate or plot.
• Distance between samples are the same.
• Distribution of samples in a pattern.
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25. Unstratified Systematic Sampling
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26. 26

27. Diagrammatic plan for a 20 percent systematic strip cruise.
Sample strips 1 chain wide are spaced at regular intervals of
5 chains.
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28. 5.8.5 Cluster Sampling
In cluster sampling the units sampled are chosen in
clusters, close to each other.
Examples are rattan in the same strip, or successive
sample units along a transect line.
The population is divided into clusters, and some of
these are then chosen at random.
Within each cluster units are then chosen by simple
random sampling or some other method.
Ideally the clusters chosen should be dissimilar so that
the sample is as representative of the population as
possible.
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29. 29

30. Advantages and disadvantages of sampling method
Random Systematic Cluster Stratified
Advantages ideal for spreads the saving of saving of
statistical sample more travelling time, travelling
purposes evenly over and time, and
the population consequent consequen
easier to reduction in t reduction
conduct than a cost in cost
simple random
sample
spreads the
sample more
evenly over
the population
easier to
conduct than a
simple random
sample
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31. Advantages and disadvantages of sampling method
Random Systematic Cluster Stratified
Disadvantages hard to the system units close to units close
achieve in may interact each other to each
practice with some may be very other may
requires an hidden pattern similar and so be very
accurate list of in the less likely to similar and
the whole population, represent the so less
population e.g. every third whole likely to
expensive to house along population represent
conduct as the street larger the whole
those sampled might always sampling error population
may be be the middle than simple larger
scattered over one of a random sampling
a wide area terrace of sampling error than
three simple
random
sampling
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32. END OF LECTURE
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