Lecture 3: Vegetation Sampling - Alaska Geobotany Center

Oct 17 schedule
• 1 hour to go over PCQ lab to see if we can
arrive at the right numbers for density,
frequency, basal area for both transects.
• Short discussion of notebooks.
• Lecture on vegeta@on sampling methods.

Lecture 3: Vegeta4on Sampling
• Subjec4ve vs. objec4ve sampling
• Centralized replicate, random, and systema4c
sampling approaches
• Review of relevé approach
• Cover
• Frequency
• Density
• Tree height
• Biomass and various related indices (LAI, NDVI)

Subjec4ve vs. objec4ve sampling
Subjec've sampling
– Sample sites are consciously chosen as representa4ve of predetermined
vegeta4on classes.
– Most flexible sampling scheme
– Allows for experience and decision making ability of the inves4gator
– Best used in areas where there are clear boundaries between plant
communi4es
– Good approach for vegeta4on classifica4on
Objec've sampling
– Sample sites are chosen according to chance (i.e. random sampling)
– Essen4al if probability sta4s4cs are to be used to back up the
conclusions
– Best used in areas where boundaries between communi4es are
indis4nct or where the objec4ve is to determine the causes of varia4on
within a single plant community
– Good approach for ordina4on methods

Objective sampling approaches
Random
Systema4c
Non stra4fied
Stra4fied

Objec4ve sampling approaches:
Random, and systema4c
Random: Sample sites are chosen according some randomizing method
(e.g., dice, random numbers). Any point is a possible sample point.
– Non-­‐stra4fied random: plots are chosen completely randomly.
– Stra4fied random: The research areas is first divided into classes
based on some criteria, for example, landscape units (floodplains,
hills, mountains) or mapped vegeta@on units. Sample sites are
then randomly chosen in the various classes. This ensures that the
most common units are not over-­‐sampled and the uncommon
units under sampled. The samples are dispersed throughout the
en@re survey area.
Systema4c: Plots are located according to a regular system such as a grid
or regular intervals along a line.
– Sra4fied systema4c approach is similar to the stra@fied random
approach except the sample sites are chosen according to a
systema@c method (grids or linear transects) within each stra@fied
class.

Subjec4ve sampling approach:
Centralized Replicate
Centralized replicate
– Sample sites are centrally located within representa4ve
homogeneous areas of predetermined vegeta4on types.
– Sampling is replicated in many similar areas to obtain a large
sample size of similar vegeta4on.
– This method is used in the relevé approach.

Plot sampling: The relevé method
• French term meaning summary, statement, list or catalog. OZen used in
terms of surveys.
• The quickest way to obtain detailed plant-­‐community informa4on.
• Main objec4ve is get a complete list of plant species with cover es4mates
from a representa4ve plant community.
• Standardized approach developed in Europe.
• Applica4on has been tradi4onally been for vegeta4on classifica4on in the
European tradi4on, but the method can be applied to vegeta4on surveys
anywhere and ordina4on methods can be applied to the data.
• Does not necessarily involve sampling other components of the site such as
soils and site factors, although these are oZen collected if environmental
gradient analysis is part of the research.
• Subjec4ve sampling (centralized replicate).
• Qualita4ve in the sense that species cover is es4mated instead of measured.
• Quan4ta4ve in the sense that it gives a complete list of species for the plot.
• Much more on the method later.

En44ta4on and sample site requirements of
relevés:
Requirements of a sample site for a relevé
• Should be recognizable as unit that is repeated in other areas of the landscape, i.e. it is
a repea@ng assemblage of species.
• HOMOGENEITY of the vegeta@on canopy, soil, and other site factors.
• Large enough to contain all the species in the community, but small enough to sample
efficiently and not contain other vegeta@on types (minimum area).
The first step: en'ta'on
• The process of subdividing the vegeta4on into recognizable en44es or preliminary
vegeta4on types
• The first groupings should be at the “habitat type level”. (e.g. stream areas, talus slopes,
discrete soil types) recognizable units on aerial photos, obvious physiognomic
differences.
• Start with the largest easiest to recognize units.
• Reconnaissance essen@al (cannot be overemphasized) The beXer your ini@al
knowledge of an area, the beXer will be the subsequent sampling.
• Important to avoid sampling ecotones or breaks between dis@nct communi@es.
• Knowledge of exis@ng literature and experience in other areas help.
• More subtle floris@c differences will become apparent later.
• Itera@ve process that may take several aXempts to perfect descrip@on of the
communi@es.

Desirable quali4es of relevés:
• Homogeneous.
• Representa4ve of
many areas in the
landscape
• Large enough to
contain the great
majority of
species in the
community
Fred Daniéls collec@ng relevé data at Isachsen, Canada.
Photo D.A. Walker

Relevé sites:
Homogeneity of vegeta4on

How would you sample
this landscape?

Plot size
Minimal Area Method

Plot size
Tables based on previous inves4gators experience

The advantage of permanent plots
• For long-­‐term studies. Examine changes due to successional processes, climate
change, or disturbance.
• Complete species list can be made by revisi4ng at different 4mes of year.
• Permits collec4on of other types of informa4on that were not collected during
the first visit, for example, spectral informa4on for remote sensing studies,
observa4ons regarding winter snow depth or other more detailed microclimate
informa4on, biomass informa4on, or physiological informa4on for individual
plant species.
Marking the plots:
In some of our studies, we pound 3/4" rebar in the center of the plot and slip a 60"x1" PVC pipe
over the rebar. The pipe is marked with 10-­‐cm stripes that can be used for monitoring snow
depth or water depth in aqua4c vegeta4on. The markers are also highly visible for loca4ng
the plots, par4cularly in winter, and provide a scale for photographing the vegeta4on.

Example of a European relevé protocol, header data
• Relevé No.,
• Date,
• Loca4on,
• Site descrip4on,
• Habitat descrip4on
• Brief soil descrip4on
• Area of plot
!

Example of a European relevé protocol: species data
Species data:
• Note separate list for
each layer in canopy.
• Cover-­‐abundance score (le_
column, le_ of decimal), Br.-­‐Bl.
cover abundance scores. See
next slides.
• Sociability (le_ column, right of
decimal. See next slides.
• Phenological state (middle
column: See next slides.
• Vigor (right column: See next
slides.
Notes:
1. Except for cover-­‐abundance, much of
this informa=on is difficult to quan=fy and
has only minimal value for classifica=on or
ordina=on methods. We will record cover-­abundance
for species in each layer, but
ignore the other variables.
2. We make voucher collec=ons of all
species in the plot for the record, and to
insure that mistakes in names are not
made.

Strata (canopy layers)
• Separate lists are made for each layer in the plant
canopy. A single species can appear in more than
one layer.
• Tree layer (can be broken into tall trees (T1) and lower strata trees
(T2)
• Shrub layer (low and tall shrubs or break into S1 and S2)
• Herb layer (includes dwarf shrubs, < 40 cm, or break into H1
(dwarf shrubs) and H2 (herbs)
• Moss layer (includes mosses, lichens, liverworts)

Sociability (rarely recorded)
• Sociability is a measure of the degree of clustering
(contagion) of individuals of a plant species.
– .1 Growing solitary
– .2 Growing in small groups, or in small tussocks
– .3 Growing in small patches, cushions or large tussocks
– .4 Growing in extensive patches, in carpets or broken mats
– .5 Growing in great crowds or extensive mats covering the whole
plot

Vigor (rarely recorded)
• Vigor is a measure of the vitality of the species in the plot.
– Closed solid circle: Well developed, regularly comple@ng life cycle.
– Open circle with center dot: With vegeta@ve produc@on but not
comple@ng life cycle .
– Open circle: Feeble with low vegeta@ve propaga@on, not
comple@ng life cycle.

Phenology (rarely recorded)
• Phenology provides informa@on regarding the
reproduc@ve status of the plant (v. vegeta@ve, fl.
flowering, fr. frui@ng, s. senescing.).

Site-­‐factor data form

Soils
• The soil-­‐vegeta@on rela@onships are a key to understanding
vegeta@on paXerns.
• If you have a background in soils, then it is desirable to obtain
as complete soil informa@on from each sample site as
possible. Or work in conjuc@on with a soil scien@st.
• The effort should include digging a soil pit, making a quick
descrip@on of the soil (see relevé soil form), and collec@ng soil
from each soil horizon for later analysis.
• Use a can of known volume to carefully collect the soil. At a
minimum, a grab sample (large handful) of soil should be
obtained from the top mineral horizon (generally 10 cm
depth). This can later be analyzed for pH, percent soil
moisture, soil texture, soil color, percent organic maXer, soil
nutrients (N, P, K) and other physical and chemical
characteris@cs.
• In our class sampling, we use a simplified soil descrip=on that
is part of the site factor data sheet, and we will collect soil
samples from the roo=ng zone (about 10 cm) or the top of the
first mineral horizon in organic-­‐rich (peaty) soils.

Soil descrip4on form (for those with experience
in soils descrip4on)

Photographs
• Photographs can be extremely valuable for later reference.
• We generally take at least three photographs:
(1) the loca4on of the plot within the general landscape, preferably
with a stake marked with the plot number in the center of the plot;
(2) one or more ver4cal close-­‐ups of the vegeta4on; and
(3) a photo of the soil profile that includes a label giving the plot
number, a ruled tape for scale, and markers (e.g. nails) that
delineate the boundaries between the soil horizons.

Cover
The area of ground covered by the ver4cal projec4on of the
aerial parts of plants of one or more species.
• An easily obtained index of plant biomass.
• The variable most oZen recorded.

Es4mates vs. measures of cover
• Es4mates of cover can be obtained by using cover-­‐abundance
scores. Most useful for classifica@on.
• Measures of cover can be made using point sampling
methods, line transect method, or photos and planimeter or
other direct measure of cover. O_en necessary for change
analysis and more quan@ta@ve studies.

Es4ma4ng percentage cover
• Generally a ball-­‐park es4mate is good enough
(e.g., 20% for species B above. This falls in B-­‐B
cover-­‐abundance category 3, which is the same
as the actual cover (33%) determined by
measurement.)
• Cover is very difficult to
accurately es4mate. The
use of cover-­‐abundance
scores greatly reduces
the varia4on in scores
from observer to
observer.
• For the relevé method,
actual scores are not as
important as the
presence or absence of
species.
• Cover is a secondary
considera4on in the
Braun-­‐Blanquet table
analysis method.

Cover-­‐abundance classes

Measuring Cover:
Line intercept method
• Generally used for tree and shrub cover or for measuring cover of
clearly defined vegeta4on types
• A line is laid out along the ground and the line segments for each
species or vegeta4on type is recorded.
• Percentage cover for each species is the total length of line
segments for each species divided by the total length of the
transect.

Measuring Cover:
Point intercept methods
• Many methods
– Point quadrats
– Point frames
• Ver4cal point frames
• Inclined point frames
– Moosehorn crown cover es4mator
– Laser points
– Op4cal point sampling devices

Advantages and disadvantages of point-­intercept
methods
• Advantages
– objec@ve
– confidence in values (accurate & precise)
– repeatable
• Disadvantages
– @me consuming
– emphasizes most common plants
“best suited to applica=ons such as mine permit baselines and tes=ng for
revegeta=on success, where maximum confidence in absolute cover and
repeatability is paramount and informa=on on the full range of individual
species' cover values is not.” (Buckner web site)

Ver4cal point frame
Martha Raynolds

Point methods
Sloping point frame to
32.5% makes it least
affected by foliage
angle. Will increase
values, but may provide
beXer comparison
between stands in some
vegeta@on types.
% cover
Effect of point diameter: Ideal is infinitely small
Percent cover measured by 100 pins
100
80
60
grass FESRUB
40
forb RANACR
moss HYLSPL
20
0
0 1 2 3 4 5 6
Diameter of pin (mm)
Shimwell 1972
• First hit is always recorded
• Successive hits on same species recorded if interested in leaf area
• Hits of other layers o_en recorded
• Understory hit (usually moss layer) o_en recorded
• Height can be recorded
• Various liXer and bare categories can be defined

Point quadrat

Point quadrat grid

Point quadrat grid
Walker, D.A., Walker, M.D., Gould, W.A., Mercado, J., Auerbach, N.A., Maier, H.A., Neufeld, G.P. 2010. Maps for monitoring changes to
vegetation structure and composition: The Toolik and Imnavait Creek grid plots. Viten. 1:121-123.

Point quadrat grid
Walker, D.A., Walker, M.D., Gould, W.A., Mercado, J., Auerbach, N.A., Maier, H.A., Neufeld, G.P. 2010. Maps for
monitoring changes to vegetation structure and composition: The Toolik and Imnavait Creek grid plots. Viten. 1:121-123.

Point quadrat grid
Walker, D.A., Walker, M.D., Gould, W.A., Mercado, J., Auerbach, N.A., Maier, H.A., Neufeld, G.P. 2010. Maps for
monitoring changes to vegetation structure and composition: The Toolik and Imnavait Creek grid plots. Viten. 1:121-123.

Time-­‐series results from Imnavait Creek
point-­‐quadrat data
Data from W.G. Gould and J. Mercado.

Funky methods of determining tree-­‐canopy cover
Densitometer with single cross hairs
Moosehorn crown-cover estimator
Spherical densiometer

Buckner sampler: Op4cal Sigh4ng Device (OSD)

Basal area
• A measure of dominance. Generally used for trees.
• The cross-­‐sec4onal area of tree stems at breast height per
unit of ground area (e.g., m 2 /ha)
• Methods of determining basal area
– Measure tree diameters at breast height with a biltmore s4ck or
diameter tape. Area = π(dbh/2) 2 . This approach is used in count-­‐plot
and distance methods (e.g., point-­‐centered quarter method).
– Bijerlich s4ck, or angle gauge
• We will discuss basal area more thoroughly when we discuss
methods of measuring density and dominance of forest
species (point-­‐centered quarter method and count-­‐plot
method).

Frequency
• Expressed as a percentage of plots (quadrats) of
equal size in which at least one individual of the
species occurs in a group of plots.
• It is a measure of the degree of uniformity with
which individuals of a species are distributed in a
vegeta4on type.

Density
• The number of plants per unit area. Expressed as number/
square meter, stems/acre, etc.
• Most oZen used for trees or large plants.
• An an easy concept to grasp, but very difficult to perform in
some types of vegeta4on because of:
(1) the difficulty of defining an individual (e.g. caespitose growth forms,
plants with underground rhizomes, plants in peaty landscapes oZen
have complicated stems just beneath the surface of the moss layer)
(2) quadrat size affects density size because of problem of coun4ng
large individuals near the boundary of the quadrat
(3) it is very 4me-­‐consuming in graminoid dominated systems and low-­growing
vegeta4on, or moss or lichen communi4es.

Tree height
• The number of plants per unit area. Expressed as number/
square meter, stems/acre, etc.
• Most oZen used for trees or large plants.
• An an easy concept to grasp, but very difficult to perform in
some types of vegeta4on because of:
(1) the difficulty of defining an individual (e.g. caespitose growth forms,
plants with underground rhizomes, plants in peaty landscapes oZen
have complicated stems just beneath the surface of the moss layer)
(2) quadrat size affects density size because of problem of coun4ng
large individuals near the boundary of the quadrat
(3) it is very 4me-­‐consuming in graminoid dominated systems and low-­growing
vegeta4on, or moss or lichen communi4es.

Tree height
Use your trigonometry
for something useful!
There are many
methods. We will use
one:
45 o 90 o
45 o
1 2
• If the tree is ver4cal, and you stand
where the angle to the top of the tree is 45
degrees, the distance from you to the tree
is the same as its height
• To get the angle to be 45 degrees
• measure the length of your arm,
from shoulder to fist
• hold the meter s4ck ver4cally, so
that distance extends above your fist
• if you put your eye close to your
shoulder and sight at the top of the
meter s4ck, that is 45 degrees
• walk to a point where you can sight
on the top of the tree and are level
with the bojom
• measure the distance to the bojom
of the tree
1

Tree height

Biomass (phytomass)
• Total standing crop (g m -­‐2 )
• Clip harvest
– usually sorted into categories such as live vs. dead, plant
func@onal types, etc.
– dried and weighed
– (can be frozen before these steps)

Biomass clip harvest
1 2
3
4
5
6
7
8

Biomass sor4ng according plant func4onal
types

Plant func4onal type sor4ng categories
• Deciduous shrubs
– Woody stems
– Foliar
• Live
• Dead
• Evergreen shrubs
– Woody stems
– Foliar
• Live
• Dead
• Forbs
• Live
• Dead
• Graminoids
• Live
• Dead
• Lichens
• Live
• Dead
• Mosses
• Live
• Litter
• Dead

Methods of measuring biomass or
proper4es that are surrogates of biomass
• Aboveground biomass
– Clip harvest
– Dimension analysis
• Belowground biomass
– Soil cores
– In situ root washing
– Root ingrowth bags
– Mini-­‐rhizotrons (root length, root produc@on, death)
• Leaf area index (LAI)
– Point frame
– Li-­‐COR instruments
• Remote sensing vegeta4on indices
– Normalized difference vegeta@on index (NDVI)

Clip Harvest
• Normally, several small representa@ve areas (e.g. 0.1 m 2
for graminoid communi@es) are clipped.
• Normally it is desirable to provide more detail from clip
harvests than total mass per unit area.
• Sor@ng of the harvest can be by any of several criteria, or
combina@on thereof, for example:
1. Species
2. Growth forms
3. Live vs. dead
4. Foliar vs. woody (useful for determining green biomass)
5. Plant parts (flowers, stems, leaves, seeds, etc.).
• Once sorted, the components are oven dried (105° C) and
weighed, and biomass expressed as mass/unit area

Alaska Tundra Biomass Sor4ng
Forbs Graminoids Dwarf Shrubs Horsetails Mosses Lichens
Live Dead Live Dead Live Dead
Evergreen Deciduous
Woody Foliar Woody Foliar
Live Dead
Live Dead

Biomass sampling in forests
dbh

Belowground biomass
• Below ground harvest
– Much more difficult to obtain.
– In herbaceous vegeta@on several cores are take by taken by pressing a coring tube
(e.g., 10-­‐cm diameter) into the soil and extrac@ng soil containing roots.
– The cores are washed to remove the soil.
– Live from dead roots can some@mes be determined by color of the roots, or
applica@on of tetrazolium salts.
– Woody root systems may require excava@on and exposure of the root system in
situ.
• Root ingrowth bags
– Numerous small nylon mesh bags of known diameter and volume are filled with
soil and inserted into the ground.
– These are retrieved at intervals, and the ingrown roots are removed and weighed.
• Minirhizotrons
– Photos taken at regular intervals along a tube inserted into the ground. PaXerns of
fine roots are photographed at several @mes during the summer, and root growth
is recorded on video.
– Growth is obtained by taking periodic photos.

Minirhizotron
apparatus
Photo: Roger Ruess

Photo of roots through the minirhizotron apparatus

Net primary produc4vity (NPP)
NPP = (W t+1 -­‐ W t ) + D + H
(W t+1 -­‐ W t ) = difference in biomass between two
harvests
D = biomass lost to decomposi@on
H = is the biomass lost to herbivory

Leaf Area Index (LAI)
• An index of the ra4o of the area of leaves and green vegeta4on in
the plant canopy per unit area of ground surface
• LAI is a nondestruc4ve, rela4vely fast method of obtaining an
es4mate of biomass.
• The only way to get true leaf area is to strip all the leaves off the
plants and measure their area.
• All other methods provide an “index” of this value (e.g. inclined
point frame, LICOR-­‐2000).
• Regression methods are used to relate biomass to leaf area.

Inclined point frame
32.5˚ @lt was
determined as the
best compromise for
determining the area
of leaves in mixed
canopies of
erectophyllic and
planophyllic leaves.

Electronic inclined point frame for
determining LAI

Li-COR® LAI-2200 Plant Canopy Analyzer
• Very rapid method of
obtaining field LAI.
• Provides only total LAI, and
cannot dis4nguish
components of individual
species nor of woody vs.
foliar frac4on.
• Not useful for very low
growing vegeta4on (moss
and lichen mats) because the
height of the sensor is above
most moss and lichens (about
2.5 cm).
Courtesy of Li-­‐COR Biosciences

• Reference
reading is taken
above the plant
canopy.
• Below the canopy
measurement is
then taken and
the instrument
records the
difference in the
two readings of
light as the LAI.

Source: Li-COR manual
Correla4on between actual leaf area
and LAI measured with the LAI-­‐2000

Normalized Difference Vegeta4on
Index (NDVI)
• One of several spectral vegeta4on indices that use
reflectance in variance bands of the total spectrum to
determine and index of vegeta4ve abundance.
• The NDVI is based on the principle that red, R, wavelengths
(near 0.6µm) are absorbed by chloroplasts and mesophyll,
while near infrared, NIR, wavelengths (0.7-­‐0.9µm) are
reflected.

Energy inputs to the top of the Earth’s atmosphere and surface
Top of Atmosphere
Percent reaching Earth Surface
From Lillisand and Kiefer 1987

Reflectance spectra for typical components
of the Earth’s surface
From Lillisand and Kiefer 1987

Different vegeta4on types have different
reflectance spectra depending mainly on chlorophyll content

Absorption spectra
for different plant
pigments and
photosynthetic
response
• The pigments in plant
leaves, chlorophyll,
strongly absorbs visible
light (from 0.4 to 0.7 µm).
for use in photosynthesis.

Some reflectance spectra
The cell structure of the
leaves and the plant canopy,
strongly scajer near-­‐infrared
light (from 0.7 to 1.1 µm).
(http://internet1-ci.cst.cnes.fr:8100/cdrom/ceos1/titlep.htm)
Normalized Difference Vegeta4on Index, NDVI = (NIR – R) / (NIR + R)

The difference in the
reflectance in the NIR and
red regions is the numerator
of the NDVI.
The sum of these
reflectances is the numerator
which normalizes the value
and corrects for shadows and
different slope aspects.
(http://internet1-ci.cst.cnes.fr:8100/cdrom/ceos1/titlep.htm)
Normalized Difference Vegetation Index, NDVI = (NIR – R) / (NIR + R)

Satellites measure reflectance in discreet bands or channels
From Lillisand and Kiefer 1987

Thema@c Mapper (TM) Sensor bands on Landsat 4 and 5 satellites

Normalized Difference Vegeta4on Index: an
index of greenness
NDVI = (NIR-­‐R)/(NIR+R)
NIR = spectral reflectance in the near-­‐infrared band
(0.725 -­‐ 1.1µm), where light scaXering from the canopy
dominates,
R = reflectance in the red, chlorophyll-­‐absorbing por@on
of the spectrum (0.58 to 0.68µm).

Hand-held spectroradiometer for ground-level
measurement of spectral reflectance
• There are several
brands of hand-­‐held
radiometers that
mimic the sensors in
satellites.
• Some have four
sensors that can be
set to sense the same
band widths as
common satellite
sensors.
• Most of the newer
ones have large
numbers of channels
(ASD instruments
have 256 channels).
Courtesy of Analy@cal Spectral Devised, Inc., Boulder, CO

Biomass and LAI vs.
total summer
warmth index
1200
1000
800
600
Above-ground biomass vs. Total summer
warmth
Biomass
y = 85.625e 0.0703x
R 2 = 0.9131
Oumalik
Ivotuk
MAT
MNT
400
200
Barrow
Atqasuk
Oumalik
Ivotuk
0
0 10 20 30 40
Total Summer Warmth
( o C)
Leaf Area Index vs. Total Summer Warmth
Leaf Area Index (LAI)
3
2.5
2
y = 0.4763e 0.0463x
R 2 = 0.933
Ivotuk
MAT
MNT
1.5
Oumalik
1
0.5
Barrow
Atqasuk
Ivotuk
Oumalik
0
0 10 20 30 40
Total Summer Warmth
( o C)

NDVI vs. total aboveground biomass
NDVI vs. Total Biomass
0.6
0.55
0.5
y = 0.0005x + 0.1471
R 2 = 0.9827
Oumalik
Ivotuk
0.45
0.4
Barrow
0.35
Atqasuk
0.3
200 300 400 500 600 700 800
TotalBiomass (g/m 2 )

AVHRR false-­‐Color infrared mosaic
Redder areas
have more
biomass

MaxNDVI map of the circumpolar Arc4c
MaxNDVI
0.62

Eurasia Arctic Transect
Plot-level biomass trends
along two North-South
Arctic Transect
North America Arctic Transect
83

Arc4c phytomass based on NDVI data from
clip harvests