Alex Peters
University of Exeter
Dissertation on the effects of moisture and climate change on Sphagnum
growth rates in Exmoor Mires
2011
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Table of contents
SECTION
HEADING
TITLE PAGE
CONTENTS
LIST OF FIGURES
ACKNOLEDGEMENTS
ABSTRACT
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INTRODUCTION
1.1 Overarching aim
1.2. Radiative forcing and climate change
1.3. The missing carbon sink, peatlands and climatic importance
1.4. UK climate projections
1.5. Null Hypothesises
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LITERATURE REVIEW
2.1. What is peat?
2.1.1. Peat and Peatland classification
2.1.2. Sphagnum mosses
2.1.3. Peatland Hydrology
2.1.4. Carbon cycle of peatlands
2.1.5. Sphagnum, hydrology and carbon cycling
2.1.6. Peatland resources within the UK
2.2. Peatland Monitoring
2.2.1. Carbon fluxes
2.2.2. Near surface water monitoring
2.3. What is remote sensing
2.3.1. Remote sensing of peatlands
2.3.2. What is spectroscopy
2.3.3. Fundamentals of reflection
2.3.4. Sphagnum spectroscopy
2.4. Spectral indices
2.4.1. Red edge Infection Point (REIP)
2.4.2. Chlorophyll Index (CI)
2.4.3. Photochemical reflectance Index (PRI)
2.4.4. Floating Water Band Index (fWBI)
2.5. Gaps within the literature
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STUDY AREA
3.1. Introduction
3.2. Peat development
3.3. Peat cutting
3.4. Conservation and the Mires on the Moor project
3.5. Blackpitts
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METHODOLOGY
4.1. Plant collection
4.2. Cooled incubator
4.3. Experimental design
4.4. Pre-experiment
4.5. Experiment
4.6. Analysis
4.6.1. Reflectance
4.6.2. Spectral Indices
4.6.2.1. CI, PRI, fWBI
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4.6.2.2. REIP
4.6.3. Statistical testing
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RESULTS
5.1. The plants
5.2. Spectral reflectance
5.3. Changes in spectral reflectance caused by the
experiment
5.3.1. Control condition
5.3.2. 15% condition
5.3.3. 30% condition
5.4. Spectral indices
5.4.1. REIP
5.4.1.1. Control condition
5.4.1.2. 15% condition
5.4.1.3. 30% condition
5.4.2. PRI, CI, fWBI
5.5. Statistical testing
5.5.1. Normal distribution
5.5.2. Spearman‟s rank
5.5.2.1 Control
5.5.2.2. 15% condition
5.5.2.3. 30% condition
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DISCUSSION
6.1. Discussion of the results
6.1.1. Changes in spectral reflectance
6.1.2. REIP
6.1.3. CI
6.1.4. fWBI
6.1.5. PRI
6.2. Evaluation
6.2.1. Climate chamber
6.2.2. Parametric testing
6.2.3. Experimental light
6.2.4. UKCP probability and climate models
6.2.5. Standardisation
6.3. Future research
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CONCLUSION
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REFERENCES
APPENDIX
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Table of Figures
FIGURE
NUMBER
2.1
2.2
2.3
3.1
3.2
3.3
4.1
4.2
4.3
4.4
4.5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
5.16
5.17
5.18
5.19
5.20
5.21
5.22
5.23
5.24
5.25
5.26
5.27
5.28
5.29
5.30
5.31
6.1
HEADING
Table identifying the main differences between
ombrotrophic and minertrophic peatlands.
Spectral reflectance curves comparing Sphagnum with
different genus‟ and species.
Spectral reflectance curves comparing Sphagnum on day
1 and day 12 of a desiccation experiment
Map showing the location of Exmoor National Park
Mires on the Moor restoration sites
Photos of site location
Photos of the Sphagnum samples within the cooler
incubator
Photo of the spectrometer set up in the dark room
BRF equation
Spectral reflectance indices equations
Spearmans rank equation
Photos of Sphagnum samples
Spectral reflectance curves for every Sphagnum sample
S.cuspitdatum (control) Day 1, Day 10
S.fallax (control) Day 1, Day 10
S.palustre (control) Day 1, Day 10
S.subnitens (control) Day 1, Day 10
S.cuspitdatum (15%) Day 1, Day 10
S.fallax (15%) Day 1, Day 10
S.palustre (15%) Day 1, Day 10
S.subnitens (15%) Day 1, Day 10
S.cuspitdatum (30%) Day 1, Day 10
S.fallax (30%) Day 1, Day 10
S.palustre (30%) Day 1, Day 10
S.subnitens (30%) Day 1, Day 10
Comparison of BRF reflectance and smoothed BRF
reflectance
BRF and derivative for S.cuspitdatum (control)
Changes in the first-order derivative for the 15% and 30%
experimental conditions
Table showing how much REIP moved
Changes in fWBI for S.cuspitdatum
Changes in fWBI for S.fallax
Changes in fWBI for S.subnitens
Changes in fWBI for S.palustre
Changes in CI for S.cuspitdatum
Changes in CI for S.fallax
Changes in CI for S.subnitens
Changes in CI for S.palustre
Table showing normal distribution summary statistics
z-score histograms
Table showing significant correlations for the control
condition
Table showing significant correlations for the 15%
condition
Table showing significant correlations for the 30%
condition.
Differences between controlled and field environments
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Acknowledgements
I would like to acknowledge the following people for their support and guidance throughout
the course of this project:
Dr. Karen Anderson from the University of Exeter, for her extensive knowledge of
peatlands and extreme patience with me throughout this dissertation.
David Smith, Mires on the Moor Officer for Exmoor National Park, for letting me
collect Sphagnum samples.
Mark and Lynda Peters, for there unyielding support throughout every stage of
this dissertation.
Laura Thompson, Rosemary Fewings and Charlotte Hayward, for reading
every draft.
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Abstract
Climate change can be considered the biggest threat facing humanity. Globally peatlands
contain approximately one third of soil carbon, although due to climate change reducing
their water tables this carbon could be oxidised into atmospheric CO2. Therefore this
dissertation aims to link changes in moisture status to spectral reflectance indices for
Sphagnum samples collected from Exmoor National Park, to help monitor blanket bogs. In
this experiment, 12 samples of Sphagnum were placed under varying moisture statuses
where their spectral reflectance was monitored daily over a period of 10 days. The results
show marked changes in spectral reflectance at the end of the experiment and reveal
mixed results for the usefulness of certain spectral indices. Ultimately, this research
suggests that the photochemical reflectance index (PRI) would be the best index for
peatland monitoring within Exmoor National Park.
Word count (136)
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1. Introduction
1.1. Overarching aim
The overall aim of this project is to assess the impact that climate change could have on
the moisture status of Sphagnum samples from Exmoor National Park and how this is
linked to spectral reflectance indices such as chlorophyll index (CI), photochemical
reflectance index (PRI), floating water band index (fWBI), and red edge inflection point
(REIP). This was achieved through a controlled desiccation experiment simulating
projected climate change where spectral reflectance was measured over a 10 day period.
The findings could provide essential information help to assess hydrological disturbances
on Exmoor National Park, using remote sensing.
1.2. Radiative forcing and climate change
Earth‟s temperature is a fine balance; between short wave radiation emitted from the sun,
which passes through the atmosphere, either being reflected or absorbed by the Earth and
absorbed radiation that Earth re-emits back into the atmosphere at longer wavelengths,
which greenhouse gases trap in the atmosphere and redirect in all directions (Houghton,
2009). Without this „natural‟ greenhouse effect Earth would be 33K cooler, as noncondensing GHGs (CO2, N2O, CH2, O3) contribute 98% towards this effect creating the
necessary temperature for water vapour and cloud feedback (Lacis et al, 2010).
However, CO2 measured at Mauna Loa has increased from 314.69ppm in March 1958 to
391.76ppm in February 2011 (NOAA, 2010), which is correlated to fossil-fuel burning
which has increased from 6.4 GtC yr-1 in the 1990s to 7.2 GtC yr-1 for 2000 to 2005
(Denman et al, 2007). This increase in CO2 is principally caused by Northern Hemisphere
(NH) sources, with excess CO2 in the NH compared to the Southern Hemisphere
increasing in proportion to emission rates at 0.5ppm per GtC yr-1 (Denman et al, 2007).
This along with land use change is having a positive radiative forcing effect, which is
outside the realm of natural variability (Karl and Trenbreth, 2003) and could potentially be
the biggest threat facing humanity.
1.3. The missing carbon sink, peatlands and their climatic importance
During the 1980s, particular attention was paid to an imbalance in the global carbon cycle
because average emissions where greater than accumulation in the atmosphere and
oceans, leading to the creation of the term „the missing carbon sink‟ (WHRC, 2010).
However, the atmosphere and the layer of vegetation covering the Earth‟s surface, the
phytosphere, are connected through photosynthesis, fixing CO2 into the terrestrial
ecosystem (Cieslik et al, 2009). Hence, „the missing carbon sink‟ has now been found and
renamed „the residual land sink‟.
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Significantly more carbon is stored within soil including; wetlands, permafrost and
peatlands, than is present in the atmosphere (Davidson and Janssens, 2006). With
Northern peatlands alone containing between 270Gt (Turumen et al, 2002) and 450Gt
(Gorham, 1991) of carbon, representing one-third of the total global soil carbon pool (Post
et al, 1982). Leading scientists to suggest that boreal regions of the terrestrial biosphere
could be a significant sink for excessive atmospheric CO2 within the global carbon budget
and an important component of the land residual sink (Denning et al, 1995; Ciais et al,
1995).
Since the start of the Mauna Loa record of atmospheric CO2 approximately half of the CO2
emissions have remained in the atmosphere due to the ocean and land sinks. This
remarkably stable „airborne fraction‟ has meant that carbon absorption by the land and
ocean has increased, in the face of increasing emissions (LeQuere, 2010). However,
climate models that include the carbon cycle predict that the terrestrial sink will reduce in
efficiency over the next century causing a larger fraction of CO2 to remain airborne
causing additional warming ranging between 0.1-1.50C (Friedlingstein et al, 2006).
Specifically within peatlands, carbon sequestration depends strongly upon the height of
the water table; consequently there has been a plethora of empirical research and
modelling studies looking at the effects of climate change on water table positions. The
crucial result being, reductions in the water table caused by climate change could rapidly
convert peatlands into carbon sources (Alm et al, 1996; Belyea and Malmer, 2004; Hilbert
et al, 2000). This process can be monitored and mosses especially Sphagnum, are good
indicators of long term hydrological trends (Gorham and Janssen, 1992). Clearly, this
highlights peatlands importance, as reduced water tables could have a positive feedback
on radiative forcing, helping humans feel the full climatic brunt of our unrelenting CO2
emissions.
1.4. UK climate projections
The UK climate projection (UKCP09) is the fifth generation of regional UK climate change
data, with projections presented for three different future scenarios representing high,
medium and low greenhouse gas emissions (UKCP09, 2011). These future climate
projections are calculated by the Met Office‟s Hadley Centre‟s HadCM3 atmosphereocean general circulation model (GCM). The model represents the physical, chemical and
biological processes governing Earth‟s climate, using equations to summarise these
processes, and predict future climate (Ananthaswamy, 2011). For easier calculation
HadCM3 breaks the Earth into grid cells and layers, with a grid cell resolution over land of
2.50 latitude x 3.750 longitude, with nineteen vertical levels in the atmosphere and four
layers into the soil (Hardy, 2003). Whereas, the ocean has 20 vertical levels and a grid
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size of 1.250 latitude x 1.250 longitude (Hardy, 2003). Although for the large ensemble
experiments forming the basis of UKCP09 the ocean slab model HadSM3 is used
representing only the top 50m of the ocean as one layer prescribing the effects of ocean
heat transport rather than simulating the effects of ocean currents. Hence, it is much faster
to run, for a variety of scenarios (UKCP09, 2011).
In the IPCC Special Report on Emission Scenarios (SRES) six different emission
scenarios were published all having common themes, the scenarios have since been used
in the IPCCs Third Assessment Report (TAR) and Fourth Assessment report (AR4)
(UKCP09, 2011), and by UKCP09. Within UKCP09, three SRES scenarios used have
simply been renamed according to their relative GHG emissions; high (SRES A1F1),
medium (SRES A1B) and low (SRES B1). For this project the high (SRES A1F1) and low
(SRES B1) scenarios were used. These scenarios predict 30% and 15% decrease in
summer precipitation for South West Britain (UKCP09 Results, 2011), where Exmoor
National Park is located. Exmoor stores approximately 1m m3 of carbon, which through
decreased summer precipitation could lower the water table, potentially releasing 3m
tonnes of CO2, making this project crucially important (Exmoor Peat Resources, 2010).
1.5. Null Hypotheses
1. For single Sphagnum species (cuspitdatum, fallax, palustre, sub-nitens) moisture
status does not influence spectral reflectance.
2. For single Sphagnum species there is no significant relationship between
chlorophyll index (CI), photochemical reflectance index (PRI), and water band
index (WBI) and moisture status.
3. There is no significant difference in spectral reflectance between hummock and
hollow species.
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2. Literature review
2.1. What is Peat?
Peat is decaying organic matter formed through in situ accumulation of plant and animal
remains in anoxic, saturated conditions (Gorham, 1991). Reduced oxygen levels coupled
with waterlogged conditions result in reduced decay, through microbial activity causing
carbon accumulation in the form of peat (Clymo, 1983). As the tips of Sphagnum and
other mosses grow upwards the lower parts die becoming peat causing thick layers to
accumulate over thousands of years to form peatlands in areas of high water input,
impermeable underlying substrate and shallow landscapes (Hingley, 1993; Evans and
Warburton, 2007).
2.1.2. Peat and Peatland Classification
Peat and Peatland classification schemes vary between nationalities and professional
communities, with some scientists having compared finding an all encompassing
classification to finding the Holy Grail (Clymo, 1983). For the purpose of this research it is
important to understand, what classifies as peat and the different types of peatlands.
Peat deposits are defined in two ways: percentage organic material and botanical
composition (Charman, 2002). Organic soils, under the UN Food and Agriculture
Organisation (FAO) are known as Histosols, characterised by at least 30% organic matter
and 40cm thickness (Ashman and Puri, 2002). An article by Myslinska (2003)
demonstrates that under international organic soil classification norms, peat is a distinct
sub-group. Within England and Wales peat is classified as 50% organic matter with a
depth of 30cm whilst in Scotland minimum depth is 50cm (Holden et al, 2004). However, it
has been suggested that defining peat by percentage organic matter is „arbitrary‟ because
there is no clear break between highly organic material and almost purely organic
Sphagnum peat (Clymo, 1983). Botanical composition is important, as the litter (the above
and below ground remains of plants) dictates the type of peat. Peat deposits can be
divided into: moss, herbaceous, wood, and humified peat. Moss is separated into brown
moss and Sphagnum moss, with the latter being suggested as the most significant
peatland genus (Rydin and Jeglum, 2009).
When identifying peatland landscapes the most applicable system refers to hydromorphological classification; with the distinction based on the water source referring to the
fen-bog gradient (Lindsay 1995). Ombrotrophic peatlands colloquially termed bogs receive
their water predominately from precipitation. Whereas minertrophic peatlands called fens
collect water through groundwater and surface run-off; the main differences are
summarised in table 2.1. This project is mainly concerned with ombrotrophic peatlands, of
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which there are two main types: raised bogs, and blanket bogs. Raised bogs normally
have a convex „raised dome‟ profile whereas blanket bogs generally cover the whole
landscape of an area including mounds and slopes and occasionally have a small
minertrophic influences (Charman, 2002). Peatlands within Exmoor National Park are
examples of blanket bogs.
Main nutrient source
Productivity
Decomposition
Acidity (pH)
Peat depth
Floristic diversity
Surface Topography
Geographical
distribution
Abundance
Ombrotrophic bog
Atmosphere (Precipitation)
Low
Low
Acidic (3.5-4.5)
Deep (> 1m)
Low
Raised or convex
Mainly in boreal regions with wet
climates
Less numerous
Minertrophic fen
Groundwater flow
Low/high
Relatively high
Neutral/Alkaline 4.0-9.0
Shallow
Low/high
Flat or concave
Worldwide in most locations
Numerous
Figure 2.1 The main differences between ombrotrophic bogs and minertrophic fens
(Gorham, 1991; Holden, 2005; Lindsay 1995; Rydin and Jeglum, 2009).
2.1.2. Sphagnum mosses
Sphagnum mosses are extremely sensitive; they are good indicators of long term
hydrological trends (Gorham and Janssen, 1992); different climatic conditions (Gignac et
al, 1991) and predicting methane emissions (Bubier et al, 1995) and have been used in
this project. Sphagnum are bryophytes, with somewhere between 150-300 different
species (Daniels and Eddy, 1990).
Sphagnum carpets are formed of vertically growing shoots; with the apex of the plant
known as the capitulum. Their leaves are only one cell layer thick, with living cells
containing chloroplasts for photosynthesis embedded between hyaline cells, containing
large quantities of water (Rydin and Jeglum, 2009).Sphagnum are non-vascular and
poikilohydric. Water is therefore supplied to their capitula either directly by rainfall or via
capillary rise from the water table. During dry periods the water table falls and capillary
supply is exceed by water loss through evaporation, making the species sensitive to
hydrological change (Hayward and Clymo, 1982). When water is lost from the hyaline cells
it is accompanied by a loss of pigmentation resulting in a whitish appearance (Van
Breemen, 1995). Consequently, the genus is often limited by summer desiccation (Rydin
and Jeglum, 2009). Although, Grime‟s (2002) classification of mire plants suggests that
bryophytes are „stress tolerant‟, due to summer desiccation.
Sphagnum develops different micro-topographical features called hummocks and hollows.
Different species within the Sphagnum genus preferentially live in these areas due to a
number of general features. Hollow species are more often and more severely desiccated
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than hummock species (Rydin, 1993), hummock species have higher decay resistance
than hollow species (Belyea, 1996), and hollow species have a higher photosynthetic or
growth rate than hummock species (Clymo and Hayward, 1982). Although these general
features are heavily contested.
Sphagnum mosses are the primary species responsible for CO2 sequestration into peat,
therefore playing an important role in the global carbon cycle. A modelling study looking at
how peat accumulated in a bog in Southern Sweden over the past 5000 years showed
that carbon sequestration was primarily controlled by abrupt changes in vegetation
(Belyea and Malmer, 2004). It has been suggested that there might be more carbon
incorporated into Sphagnum both dead and alive than in any other genus of plant (Clymo
and Hayward, 1982).However, the rate at which Sphagnum mosses fix CO2 through
photosynthesis is highly dependent upon water availability.
2.1.3. Peatland Hydrology
Traditional approaches to peatland hydrology include breaking down the water balance
into inputs, stores and outputs (Charman, 2002). Hemond (1980) broke down the water
balance of Thoreaus Bog, proving it to be ombrotrophic. The hydrological equation can
therefore be reduced to water input through precipitation (1.45m), minus run-off both
surface and sub-surface (0.24m) and evapotranspiration (1.02m), with a storage capacity
of 0.19m between March 1976- June 1977 (Hemond, 1980).
In the mid twentieth century Ivanov (1948) developed the idea that bogs are diplotelmic,
which is they have two layers; the acrotelm and catotelm (Holden, 2005). The acrotelm is
the active, aerated peat layer where most growth, decay and living organisms occur.
Whereas the catotelm is less active, constantly anoxic, inactive layer which is more
humified. The boundary between the two layers is marked by the oscillating water table
(Rydin and Jeglum, 2009). Peat is gradually formed at the bottom of the acrotelm as its
slowly engulfed by water, becoming part of the catotelm. Hydraulic conductivity is high in
the poorly decomposed litter in the acrotelm but declines as the material becomes more
decomposed in its transition to peat. As a result the highest rates of ground water flow
occur at or near the water table (Charman, 2002).
This layering system is widely used in peatland development and ecohydrology modelling
studies (Hilbert et al, 2000; Holden and Burt, 2003). But, reliance on the acrotelm-catotelm
model of peatland hydrology and use in carbon cycling means that many spatial and
temporal hydrological processes are ignored or poorly understood (Holden, 2005)
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2.1.4. Carbon cycle of Peatlands
Carbon enters the peatland system through positive net primary productivity (NPP) when
photosynthesis is greater than plant respiration, causing atmospheric CO2 to become fixed
in plant tissue until decay occurs either in the actroelm or catotelm. CO2 and CH4 are both
produced by oxidation in the upper peat layer (Charman, 2002). CH4 is also produced by
methanogenic bacteria, Archea, that grow anaerobically beneath the water table (Rydin
and Jeglum, 2009). This can then be released through diffusion and ebullition (bubbles
realised from saturated peat) (Holden, 2005). Particulate organic carbon (POC) is also
exported out of the peatland through runoff, and is therefore affected by hydraulic
conductivity of the catotelm (Charman, 2002). It has been estimated that over the past
10,000 years global temperatures have reduced by 1.5-2.0oC (Holden et al, 2004) due to
atmospheric carbon stored in peatlands,
2.1.5. Sphagnum mosses, Hydrology and carbon cycling
Sphagnum and the position of the acrotelm play an important role in peatland carbon
cycling through three main processes:
1. Water being supplied to Sphagnums capitulum determines the plants productivity
as they are non-vascular. Therefore water is either directly supplied through rainfall
or capillary rise from the water table. Throughout dry periods the water table falls,
reducing capillary supply because of water loss through evaporation from the
peatlands surface. The near-surface water content of the peat will fall along with
the capitulums water content causing reductions in the rate of photosynthesis and
therefore carbon fixation (Dorrepaal et al, 2003).
2. In the acrotelm Sphagnum decay is most rapid as aerobic decay is 50 times faster
than anaerobic decay (Clymo, 1983). If the water table falls therefore increasing
the acrotelm, increased aerobic decay will take place. However, if the acrotelm is
small due to a higher water table less aerobic decay will take place and more
Sphagnum is passed into the catotelm where decay is slower, storing more carbon.
3. Peatlands are net sources of CH4. CH4 can be oxidised to produce CO2 by
methanotropic bacteria. Lowering the water table and increasing the acrotelm will
reduce concentrations of CH4 released because the increase in aerobic conditions
will lower the activity of the anaerobic methanogenic bacteria (Holden, 2005).
Although it is important to note, that CH4 has a much higher greenhouse gas
warming potential than CO2.
This is a delicate balance, which was disturbed in a Finish bog following a strong summer
drought in 2004. Researchers observed a net loss of 90g C m-2yr-1, estimating it would
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take four years with normal summer precipitation to compensate for the carbon loss (Alm
et al, 1999).
2.1.6. Peatland resources within the UK
Historically, peatlands have been considered a hindrance, and since the 1800‟s British
peatlands have declined considerably (Charman, 2002) as they have been drained and
harvested for energy, agriculture, forestry and horticultural use (Holden et al, 2004). As
recently as 1980, an article published within Nature argued the economic value of peat as
an energy source, only briefing considering conservation (Taylor and Smith, 1980).
Although, slow peat accumulation rates means that peat extraction is an unsustainable
exploitation of a finite resource (Holden, 2004). Previous exploitation means that
peatlands have already been converted from carbon sinks to sources, with future climate
change increasing the hydrological strain, due to decreased precipitation (McNeil and
Waddington, 2003; Gorham, 1991). As a result, the remaining 7% of the UK covered in
peat should be monitored (Rydin and Jeglum, 2009).
2.2 Peatland monitoring
2.2.1.Carbon fluxes
There have been many studies monitoring peatland CO2 exchange employing a variety of
methods including environmentally controlled enclosures (Bubier et al, 1998), flux
gradients (Schreader et al, 1998) and most recently the use of flux towers (Lafleur et al,
2003). One such study conducted on a bog, demonstrates the water table position is the
most important factor influencing inter-annual differences in peatland net ecosystem
exchange and therefore CO2 as well (Lafleur et al, 2003). However, the use of eddy
covariance techniques is shrouded in stochastic (random) and systematic error (Lafleur et
al, 2003).
2.2.2. Near surface water monitoring
Hydrological processes can be used to provide a wealth of knowledge on carbon balance
processes, with current peatland modelling studies requiring information on water table
position (Frolking et al, 2010; Belyea and Malmer, 2003). The ability to monitor nearsurface hydrological conditions across whole peatland complexes would provide hoards of
information regarding ecology and carbon balances.
Traditionally, hydrological monitoring has involved the collection of large amounts of smallscale processes, demonstrated by Belyea (1999). Ellergower Moss, was studied; dip wells
were installed into six different micro-sites, with bamboo canes covered in red electrical
tape. Discolouration of the tape was highly correlated to positions of the highest and
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lowest water tables, after being left in-situ for 15 months (Belyea, 1999). Although this
data is highly detailed, it is not necessarily representative of larger spatial scales. For
example Belyea (1999) conducted her research on a 30m transect consequently important
variations in peatland micro-topography and consequently water depth aren‟t accounted
for over these small spatial scales. There are also considerable logistical, monetary and
time issues associated with collecting large detailed hydrological measurements (Harris et
al, 2005). Therefore, there is evidently a need for an economic, high resolution, synoptic
tool capable of identifying and quantifying hydrological changes over whole peatlands.
Remote sensing could be just the tool, allowing for detailed spatial and temporal detection
of changes in near surface hydrological conditions.
2.3. What is remote sensing?
Remote sensing is essentially: the science and art of gathering information from a
distance without contact (Lillisand and Kiefer, 1999). However, more precisely it‟s defined
as “the practice of deriving information about the Earth‟s land and water surfaces using
images acquired from an overhead perspective, by employing electromagnetic radiation
(EMR) in one or more regions of the electromagnetic spectrum, reflected or emitted from
the Earth‟s surface” (Campbell, 2006).
2.3.1 Remote sensing of peatlands
Attempts have been made to monitor wetlands, which encompasses peatlands, using a
variety of different electromagnetic regions; specifically microwave (1mm-1m) and optical
(400-2500nm) regions. Synthetic aperture RADAR (SAR) from the European Remote
Sensing Satellite (ERS-1) was used to assess soil moisture at Romney Marsh (Griffiths
and Wooding, 1996). At relatively low incident angles C-band sensitivity to moisture is
high; therefore eighteen C-band images were used from a three day orbit. Field
measurements of soil moisture, surface roughness and rainfall patterns collected from
overpass days were correlated to mean backscatter measurements, revealing a high
positive correlation between volumetric soil moisture on bare soil and a weak correlation
with vegetated areas (Griffiths and Wooding, 1996). This demonstrates that microwave
data isn‟t the most applicable when monitoring vegetated areas like peatlands and that
large quantities of field data is also needed.
The Airborne Thematic Mapper (ATM), which is a multispectral scanner, has also been
used to map water depth at Insh Marshes (Gilvear and Watson, 1995). Field work
measuring the depth of the water table and predominant vegetation types at 60 sites
showed that vegetation type was closely related to water table depth. Stepwise regression
between ATM data and water table depth identified two relationships. Firstly, a direct
15
relationship between the presence of soil moisture which affected the temperature (band
11) and amount of light reflected (band 10). Secondly, an indirect relationship between
different vegetation communities affecting reflectance (bands 5-8) and water table depth,
as vegetation type is closely controlled by the water table (Gilvear and Watson, 1995).
However, this study was restricted to vascular vegetation which is good for identifying
long-term changes in near-surface hydrology, but not short-term, seasonal changes in
water table positions like Sphagnum mosses (Harris et al, 2005). Due to this, recently
there has been a rise in both laboratory and field spectroscopy studies looking at
Sphagnum (Vogelmann and Moss, 1993; Bubier et al, 1997; Bryant and Baird, 2003;
Harris et al, 2005).
2.3.2. What is spectroscopy?
Techniques for measuring objects in-situ spectral characteristics predate quantitative
remote sensing from airborne and space-borne platforms, with the first studies dating back
to the early 1900‟s. However, the term „field spectroscopy‟ was first introduced by
Longshaw in 1974 and can be described as the study of the inter-relationships between
the spectral characteristics of objects and their biophysical attributes in the field
environment (Milton, 1987). This technique is important in remote sensing, as it supports
vicarious calibration of airborne and satellite sensors, predicting optimum spectral bands
and provides a means of scaling up measurements from small areas (leaves and rocks) to
composite scenes (vegetation canopies) and ultimately to pixels (Milton, 1987; Milton et al,
2007).
2.3.3. Fundamentals of reflection
The radiation environment is composed of two hemispherical distributions of
electromagnetic radiation: incoming irradiance and radiance or reflectance. There are two
basic types of reflectance: specular and diffuse, which are both primarily a function of
surface roughness (Campbell, 2006). Most natural targets like leaves are not perfectly
diffuse lambertian reflectors meaning, that the intensity of the reflected flux varies with the
angle at which is leaves the surface, commonly called anisotropic reflectors. Irradiance
and radiance vary with the zenith angle (angle of illumination) and azimuth angle (angle of
view); consequently reflectance must be measured at all possible source and sensor
positions to calculate the bi-directional reflectance distribution function (BRDF) (McCoy,
2007). BRDF is the fullest definition of reflectance and is therefore problematic due to
infinitesimally small angles and consequently the bi-directional reflectance factor (BRF) is
used. BRF is calculated by measuring the radiance of the target as a proportion of the
radiance of a standard panel which is perfectly diffuse under the same irradiation and
geometry conditions (Milton, 1987). BRF has been calculated in previous spectroscopy
16
studies and plotted as a function of wavelength to create spectral reflectance signatures,
which this research also does.
2.3.4. Sphagnum spectroscopy
Previous research mainly centred upon describing the differences between vascular
vegetation and Sphagnum spectral reflectance signatures, and how different species
within the same genus have different spectral reflectance‟s (Voglemann and Moss, 1993;
Bubier et al, 1997). Figure 2.2 demonstrates these differences but also highlights the key
features of Sphagnum spectral reflectance. The green peak found at ~550nm is similar in
both vascular plants and Sphagnum, and is caused by chlorophyll which absorbs red and
blue light while reflecting green. Although for some species of Sphagnum the green peak
can extend into longer wavelengths, due to secondary pigmentation (Bubier et al 1997).
The near infrared spectrum isn‟t used in plants and is therefore heavily reflected by plant
cells creating the red edge between ~660-730nm. Although Sphagnum species tend to
have a lower reflectance and have absorption features 1000 and 1200nm due to foliar
water content and 850nm due to cell structure which vascular plants don‟t have (Bubier et
al, 1997).
Figure 2.2 A Spectral reflectance curves for typical broadleaf (Acer saccharum), conifer
(Picea rubens), moss (Sphagnum fallax), and lichen (Cladina stellaris). This demonstrates
the difference between vascular vegetation and non-vascular vegetation. B Spectral
reflectance curves for Sphagnum fuscum, Sphagnum magellancium and Sphagnum fallax
demonstrating the inter-species variability. (Source: Bubier et al, 1997)
Although more recent work conducted by Bryant and Baird (2003) sought to clarify how
the spectral signatures of Sphagnum mosses changed under a range of different wetness
conditions (Figure 2.3). There results demonstrated a range of marked changes between
400-2500nm, as reduction in water content increased reflectance in the visible, nearinfrared, and shortwave infrared regions. The distinctive water absorption features (1000
17
and 1200nm) and cell absorption feature (850nm) had largely disappeared (Bryant and
Baird, 2003). Future work has now also been conducted using spectral indices.
Figure 2.3 Spectral reflectance curve for Sphagnum capillifolium on day 1 and 12 of a
controlled desiccation experiment (Source: Bryant and Baird, 2003)
2.4. Spectral indices
Remote sensing scientists use spectral indices to quickly and non-destructively help
predict, model and infer surface processes; benefiting numerous disciplines interested in
the assessment of biomass, water use, plant stress, and crop production. This study and
previous work on Sphagnum mosses have used the four spectral indices briefly explained
below.
2.4.1. REIP
The REIP is the point of maximum slope located between the red and visible regions of
the electromagnetic spectrum, occurring in slightly shorter wavelengths in Sphagnum
mosses (6200-720nm) (Harris et al, 2005). As plants become stressed, chlorophyll levels
decrease causing a shrinkage of the red and blue chlorophyll absorption features, which in
turn moves the red edge into shorter wavelengths (Rock et al, 1988). As REIP is related to
both chlorophyll levels and internal cell structure due to its position between the visible
and near-infrared wavelengths it may be used to provide indirect evidence for water stress
within Sphagnum canopies (Harris et al, 2005) because as water availability decreases,
photosynthetic efficiency also decreases within Sphagnum plants (Rydin and Jeglum,
2009).
2.4.2.CI
A chlorophyll index created by Gitelson et al (2003) was also employed to track changes
in chlorophyll levels, by using reflectance values from the green, red and near-infrared
regions of the electromagnetic spectrum. This index uses larger bands of 10-20nm
18
compared to previously created chlorophyll indices which use bands of 10-20nm, which
therefore increases sensitivity and the signal to noise ratio, making this index more adept
(Gitelson et al, 2003). The index is defined as:
{(R750-800) / (R695-740)}-1
2.4.3. PRI
Within plants when photosynthesis declines for example due to drought, plants absorb
more light than necessary (Demming-Adams and Adams, 2006), therefore plants must
employ specific mechanisms to safely dissipate this extra energy. The exact processes
within mosses is poorly understood but it is believed they either dissipate the energy as
heat or re-emit the energy as light, a process known as chlorophyll fluorescence (Deltoro
et al, 1998). Heat dissipation is believed to occur via the conversions of xanthophylls cycle
pigments into their photo-protective state (Demmig-Adam and Adams, 2006).
Photosynthesis, heat dissipation, and chlorophyll fluorescence are in competition; with an
increase in heat dissipation reducing photosynthetic efficiency. Consequently, Gamon et al
(1992) created PRI, which incorporates reflectance changes at 531nm which is directly
related to xanthophyll pigments in their protective state. There has been lots of previous
research correlating PRI at the leaf scale, canopy scale and ecosystem scale (Garbulsky
et al, 2011). The index is defined as:
(R531-R570)/ (R531+R570)
2.4.4. FWBI
The water band index, developed by Penuelas et al (1997) uses two spectral channels
located in the near-infrared. The idea behind the WBI is that reflectance values at
wavelengths within the near-infrared absorption bands are compared with a reference
wavelength where no water is absorbed, but due to the same plant structure is reflected in
the same way (Penuelas et al, 1997). Therefore the water band index has been used in
previous research and in this research to characterise the Sphagnum specific water
absorption features at 1000nm and 1200nm (Harris et al, 2005). Although in this research
only the absorption feature at 1000nm is looked at due to the spectrometers range. The
index is defined as:
fWBI980= R920/MIN(R960-1000)
fWBI1200= R920/MIN(R1150-1220)
Work by Harris et al (2005) utilised the fWBI, CI, and REIP to try and determine changes
in near-surface moisture conditions of Sphagnum through laboratory spectroscopy. All
19
spectral indices were significantly correlated with volumetric water content, although the
relationship between the spectral indices and near-surface wetness was species-specific
(Harris et al, 2005). This research clearly highlights the use of spectral indices, but the
choice of spectral indices used will be specific for each peatland depending upon their
predominant Sphagnum species. Research has also been conducted, exploring the use
of fWBI and PRI to assess the photosynetic efficiency of Sphagnum mosses exposed to
reductions in water. The results showed that fWBI was the index least affected by species
specific differences and had the most significant correlation (Harris, 2008).
2.5. Gaps within the literature
The main consensus coming from related literature is that predicted climate change could
rapidly convert peatlands into carbon sources, which can be monitored through remote
sensing, classifying and quantifying spatially-detailed, seasonal and sub-seasonal
hydrological changes across whole peatlands. More research is needed, as response to
water loss is driven by species specific differences in photosynthetic processes (Deltoro et
al, 1998). In addition the relationship between spectral indices and photosynthesis can
differ between species because of previous in situ environmental conditions causing
plants to acclimatize and regulate photosynthetic processes in response to environmental
disturbances like drought. For example Sphagnum species from higher drier locations
have been shown to exhibit improved water transport compared to species occurring in
lower microforms, hence maintaining the supply of water to the plant head during drier
conditions (Li et al, 1992). As a result, the individual species-specific spectral reflectance
curves and spectral indices produced through laboratory spectroscopy as part of this
research can be used exclusively to monitor Exmoor bog environments in response to
ongoing management and climate change. In the future, this work could also provide vital
information needed for predicting optimum spectral bands, and providing a means of
scaling up measurements from small areas (test plots) to composite scenes covering
entire peatlands (Milton et al, 2007).
20
3. Site location
3.1. Introduction
There‟s 18,000km2 of peatlands within the UK (Rydin and Jeglum, 2009), with ~10m
tonnes of peat stored within Exmoor (Exmoor Peat Resources, 2010), making the area an
important part of the carbon cycle. Exmoor is a broad area of upland moor located in
South-West England spanning Somerset and Devon (Figure 3.1).
Figure 3.1. The location of Exmoor National Park (Source: OS Maps, 2011)
3.2. Peat development
Exmoor‟s landscape is the response of human exploitation. A radiocarbon dated peat
monolith, collected from The Chains, shows undisturbed woodland before the
development of farming (Merryfield and Moore, 1974). Farming developed 5,000-3,500
years b.p with a period of heighted farming and woodland clearance 2,300-1,500 years b.p
(Merryfield and Moore, 1974), occurring when climate become cooler and wetter (Exmoor
Moorland Development, 2010). This information suggests that the ombrogenuous
peatlands formed as result of paludification. Paludification occurs where peat forms over
previously less wet mineral ground, due to decreased evapotranspiration and increased
run-off as a result of deforestation causing increased soil moisture and raised watertables, consequently decreasing the rate of decay (Charman, 2002).
3.3. Peat cutting
Peat cutting has occurred for centuries through channelling natural springs away from
peat deposits therefore drying the peat, which was then extracted for fuel (Peat Cutting,
2010). Exmoor‟s landscape is scarred with evidence of previous peat cutting, which
occurred at Brendon Common and Exe Head until recently, with the last peat cutting
occurring just under a decade ago at Lanacombe (Peat Cutting, 2010).
21
3.4. Conservation and the Mires on the Moor Project
Exmoor National Park was designated a National Park in 1954. In 1993 Exmoor was
selected as an environmentally sensitive area (ESA) and due to the high conservation
value of moorland and coastal heaths, eight different sites of special scientific interest
(SSSI) have been designated. Exmoor contains 480 hectares of blanket bog with, all high
and low quality sites located within the SSSIs (Exmoor BAP, 2001).
Mire restoration started in 1998 through a pilot project which later developed into the
Exmoor Mire Restoration Project. The project starting restoring mires in 2006 using
peat/ditch spoil to build water resistant dams at fifteen moorland drainage locations
(Figure 3.2), resulting in 270 hectares of mire being re-wetted (Mire Restoration, 2009).
The project has stopped 499,500 tonnes of CO2 being released back into the atmosphere
(Mire Restoration, 2009). At all sites, a vegetation monitoring transect and dip wells have
been set up, with surveys occurring every 1-2 years (Mire Restoration, 2009). Exmoor and
Dartmoor National parks have also received £4.1m to share on restoring mires (Mire
Restoration, 2009).
Figure 3.2. Mire Restoration sites on Exmoor National Park (Source: Mire
Restoration, 2009).
3.5. Blackpitts
Within Exmoor the Sphagnum samples were collected from blackpitts, a blanket bog
(Figure 3.3), which has been restored as part of the project, located roughly 5km away
from Simonsbath. Blanket bogs can be sub-divided into different types (Lindsay, 1995). It‟s
believed that Blackpitts is a valley side bog due to its position on the side of hill, the source
of the River Exe within the bog, and the presence of Molinia Caerulea (purple moor grass).
22
Since restoration vegetation surveys have shown a shift from purple moor grass
dominated M25 plant community to a more diverse M17 blanket bog vegetation (Mire
Restoration, 2009). This area was specifically chosen because vegetation surveys
conducted in June 2009 showed increased growth of Sphagnum species (See Appendix
1) suggesting active peat formation. All species chosen for this project were abundant.
A
C
B
D
Figure 3.3 A-The River Exe within the bog. B- A wooden dam constructed as part of the
mires on the moor project. C-A bog pool created by a wooden dam. D- Landscape view of
the area (Source: Authors photos)
23
4.Methodology
4.1. Plant collection
Twelve samples of four Sphagnum species (S.cuspitdatum, S.fallax and S.palustre,
S.Subnitens) were collected from Blackpitts on the 21st August 2010. The samples were
identified using a magnifying glass and transferred into labelled plant pots. Each sample
was 10cm in diameter and contained 10cm of underlying litter.
Previously laboratory studies have been criticised due to excessive sample disturbance
caused by taking samples from their natural habitat (Longshaw, 1974). Although there is
evidence that some plant material can be preserved, delaying senescence and not altering
the spectral properties in the 400-1100nm region by effective storage and sampling
(Milton, 1987). Therefore the samples had to contain between 5-15cm of underlying litter,
as between this depth, Sphagnum samples become part of the peat (Rydin and Jeglum,
2009).
4.2. Cooled incubator
The samples were kept in a cooled incubator for 24 days prior to the experiment to
acclimatize, and during the experiment (Figure 4.1). The incubator was set up to simulate
average Exmoor summer climate. Average climate data was acquired from the Met Office
for Nettlecombe gathered over a 29 year period (Met Office, 2010). The incubator used
four 4W fluorescent lamps providing a luminance of 2.1KLux. To simulate the natural
diurnal environment the incubators had a photoperiod of 15 hours at a temperature of
190C providing light within the photosynthetically active range (PAR). The lamps were
automatically turned off for 9 hours while temperatures decreased to 10oC. A thermohygrometer was used to calibrate temperature and measure relative humidity which the
incubator couldn‟t control. The maximum variation from the set temperature was +6.40C
and -4.10C. Relative humidity stayed between 45-71%.
24
Figure 4.1 The Sphagnum samples within the cooled incubator (Source: Author)
4.3. Experimental design
A nested experimental design was used for this experiment where the independent
variable manipulated was the amount of water given to each sample. There were three
different conditions: control, 15% water reduction, and 30% water reduction. These
conditions were used as UKCP09 predict 15% and 30% reductions in precipitation in their
high and low scenarios (UKCP09 Results, 2010). Each condition contained one sample of
every Sphagnum species.
4.4. Pre-experiment
One week prior to the commencement of the experiment the samples health was checked,
with any plant genus or species which was incorrect removed. This ensured that the
reflectance data was solely from the correct Sphagnum sample. At this stage, daily
watering of the experimental conditions ceased to effectively lower the water table.
4.5. Experiment
The experiment lasted 10 days and on the first, fourth, and seventh days the plants were
watered different amounts according to their experimental condition. On every day of the
experiment the plants were weighed and their spectral reflectance was measured.
Laboratory spectroscopy techniques were used within this experiment because they are
advantageous for three main reasons:
1. Artificial light allows ideal control of viewing and illumination geometry.
2. Measurements can be made in the absence of direct solar light, including cloudy
weather and nigh-time.
25
3. Under the controlled conditions of artificial light, wind and haze are not problems.
Spectral measurements were therefore collected in a dark room using an Ocean Optics
USB 2000 fiber optic spectrometer, collecting data between 330-1030nm at approximately
0.3nm intervals, making it hyperspectral. A bi-conical method was used. Prior to each
measurement, reference spectra from a spectralon panel were collected in order to
convert final measurements into BRF. The dark current was also measured with every
sample and automatically subtracted from the reference panel and sample through the
spectrometer‟s computer programme OOI Base 32. The field of view of the spectrometer
was 80, and the height of the fiber optic head was 30cm above the table ensuring an
instaneaous field of view of 5cm. Illumination was provided by one dedolight with a
strength of 3400K and incident angle of 45o (Castro-Esau et al, 2006). The lamp was
turned on ten minutes before the first measurement so it was stable. Before the first
measurement a pilot measurement of both the reference panel and plant sample was
taken to check for saturation. To ensure the spectrometer measured temporal differences
instead of spatial differences within the plant canopy, the plant pots were placed in the
same place for every measurement. This was guaranteed by the use of masking tape. The
measurements were taken as fast as possible otherwise the changes in illumination would
alter the plants photosynthesis.
Figure 4.2 Sphagnum sample in the correct position signified by the masking take with
fibre optic head positioned about the plant at nadir ready to take a spectral measurement
(Source: Author)
26
4.6. Analysis
4.6.1. Reflectance
Target and reference spectra were processed into BRF within Microsoft Excel; this was
done through the equation in figure 4.3. This data was then plotted against wavelength to
create spectral reflectance signatures similar to examples in section 2.
Pλ = (Tarλ / Refλ) * Kλ
Pλ Reflectance factor
Tarλ Digital Number of target minus dark voltage
Refλ Digital Number of reference panel minus dark
Kλ
voltage
reflectance calibration
Figure 4.3- BRF reflectance equation
4.6.2. Spectral indices
After reflectance was calculated four common spectral reflectance indices were used;
each designed to detect different physical and chemical responses to changes in
desiccation prescribed by the experimental conditions. The spectral indices used have all
been described within section 2. CI, PRI, and fWBI are calculated in a similar ways while
REIP was calculated differently.
4.6.2.1 CI, PRI, fWBI
These spectral indices were simply calculated using the equations in figure 4.4. For the
fWBI, min (R960-1000) is the minimum in reflectance between these points. For the CI, R750800
and R695-740 is the average reflectance between 750-800 and 695-740. For the PRI,
R531-R570 is reflectance at 531nm minus reflectance at 570nm. For PRI and fWBI, as the
spectrometer has a wavelength interval of 0.3nm, to create a single reflectance value for
the wavelength required (531,570, and 920), the three corresponding reflectance values
were averaged.
27
Spectral
index
CI
PRI
fWBI980
Formula
Reference
{(R750-800) / (R695-740)}-1
Gitelson et al, 2003
(R531-R570)/ (R531+R570)
Gamon et al, 1992
R920/MIN(R960-1000)
Penuelas et al, 1997
Figure 4.4-spectral reflectance indices
4.6.2.2.REIP
The REIP was extracted from the first-order derivative by numerically locating the highest
peak in the derivative spectra. Derivative spectroscopy uses changes in spectral
reflectance in relation to wavelength to sharpen spectral features, allowing components of
the spectrum like the green peak and red edge to be clearly separated. The reflectance
data was smoothed by averaging the results by 20, removing any noise within the data
set. Then the derivatives were calculated by dividing the difference between successive
spectral values from the smoothed data by the wavelength interval separating them. The
derivative values were plotted against wavelength. To clearly show the difference in the
first and last derivative for every plant it was plotted on the same graph.
4.6.3 Statistical testing
To determine what type of correlation to use between the spectral indices and plant weight
normal distribution was tested by calculating measures of central tendency and dispersion.
Central tendency aims to find the average, for example arithmetic mean, median, and
mode. Although central tendency isn‟t a meaningful measure when data are widely spread
therefore measures of dispersion are also calculated. This is done through histograms,
skewness, and kurtosis. The data for all spectral indices and weight weren‟t normally
distributed. Consequently a non-parametric test was needed (Ebdon, 1999). Therefore
Spearmans rank correlation was used to test the relationship between every spectral
index (CI, PRI, fWBI) and weight, which differed due to the amount of water given to the
plants due to the experimental conditions.
28
6
Rs = 1 -
d2
n3- n
N The number of units within the sample
D Difference between the ranks
Sum of the difference
6 Constant value
Figure 4.5 Spearman‟s rank correlation co-efficient
Spearman‟s correlation coefficient was calculated using figure 4.5, providing an Rs value
between -1 (negative correlation) to +1 (positive correlation). The Rs value is then
compared to the critical value, which if smaller, means there‟s no statistically significant
correlation (Ebdon, 1999).
29
5. Results
This section displays all relevant information regarding the experiment through a range of
photographs, graphs and statistics to help achieve the aim of this study. The results will be
described in this section before being discussed in section 6.
5.1. The plants
There are extensive visible differences between Sphagnum species that distinguish
hummock and hollow species. Hummock species are typically characterised by a small,
compact capitulum, and short branches, that are normally red or brown in colour. While
hollow-carpet species normally have a large, lose capitulum, with long branches and are
green or yellow in colour (Daniels and Eddy, 1990). Figure 5.1 shows the four different
Sphagnum samples collected from Exmoor, demonstrating their differences in
appearance. S.subnitens is a hummock species displaying the small red capitula.
Whereas, S.fallax. S.cuspitdatum, S.palustre are all hollow-carpet species exhibiting the
large, loose capitula. There are variations in colour with S.fallax and S.palustre having a
strong yellow tint while S.cuspitdatum is green.
A
C
B
D
Figure 5.1. Sphagnum samples. A-Subnitens. B-fallax. C-cuspitdatum. D-palustre.
5.2. Spectral reflectance
As previously mentioned in section 4, spectral reflectance curves were created. Figure 5.2
reveals the differences between the four different plants spectral reflectance. Due to the
plants colour, the location of the green peak caused by chlorophyll varies. S.palustre has a
30
green peak located at 545nm, while the green peak for S.cuspitdatum and S.fallax are
located at 548nm and 550nm respectively further into the yellow portion of the spectrum.
S.subnitens has a peak at 542nm although it extends to 610nm into the orange/red portion
of the electromagnetic spectrum, due to its colouring. S.subnitens also has the lowest BRF
value for the green peak of 0.012 due to presence of anthocyanins absorbing light. The
hollow-carpet species have a more rounded red edge shoulder, compared to the sharply
peaked shoulder of S.subnitens. All species show absorption features around 850-890nm
and 1000nm caused by cell structure and water absorption.
5.3. Spectral reflectance changes caused by the experiment
The spectral profiles reveal a series of marked changes in all experimental conditions in
response to moisture reduction changes, this will now be explained.
5.3.1. How do the control plants change?
Figures 5.3-5.6 show the spectral reflectance of the Sphagnum plants on the first and last
day of the experiment under the control condition.
Figure 5.3 shows the control condition for S.cuspitdatum, highlighting that relatively little
change has occurred, as all relevant characteristics: green peak, red edge, 890nm
reflectance peak, 1000nm absorption feature haven‟t changed. Although, there is a slight
increase in NIR reflectance on day 10.
Figure 5.4 shows the control condition for S.fallax, demonstrating that small changes have
occurred. There has been an increased in visible reflectance with the biggest increase
occurring between 540-640nm. There has also been a decrease in NIR reflectance.
Although changes have occurred, the overall shape of the curve hasn‟t changed as it still
shows the main characteristics.
Figure 5.5 shows the control condition for S.palustre. This graph exhibits a small increase
in visible reflectance around the green peak and an increase in NIR reflectance. But the
shape of the curve has remained similar displaying the key features.
Figure 5.6 shows the control condition for S.subnitens, displaying relatively little change in
visible reflectance and increased reflectance for NIR.
These four graphs show that relatively little change has occurred, with the change that has
taken place caused by stress due to being part of the experiment. The smallest change
has occurred within S.cuspitdatum and S.subnitens.
31
Spectral reflectance curves
0.6
BRF (relfectance)
0.5
0.4
S.cuspitdatum
0.3
S.fallax
S.palustre
0.2
S.subnitens
0.1
0
340
440
540
640
740
840
940
1040
Wavelenth (nm)
Figure 5.2 spectral reflectance curves for all species used within the experiment.
S.cuspitdatum (control) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
Day 1
0.3
0.2
Day 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.3 Sphagnum cuspitdatum (control) spectral reflectance curve
32
S.fallax (control) spectral reflectance curve
BRF (reflectance)
0.6
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.4 Sphagnum fallax (control) spectral reflectance curve
S.palustre (control) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.5 Sphagnum palustre (control) spectral reflectance curve
33
S.subnitens (control) spectral reflectance curve
0.6
BRF (reflectance
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.6 Sphagnum subnitens (control) spectral reflectance curve
34
5.3.2 How do the 15% condition plants change?
Figures 5.7-5.10 show the spectral reflectance of the Sphagnum plants on the first and
last day of the experiment under the 15% water reduction condition.
Figure 5.7 shows S.cuspitdatum, highlighting that there has been an increase in visible
and NIR reflectance. All key features; the green peak, red edge, 850nm cell absorption
feature and the 1000nm water absorption feature are present at the end of the experiment.
There is also an absorption feature at ~790nm, which is present within the control.
Figure 5.8 shows S.fallax. This graph demonstrates that there has been virtually no
change in visible reflectance but an increase in NIR reflectance. The spectral signature
overall still remains the typical shape although it does look allot flatter comparatively to
S.fallax within the control condition.
Figure 5.9 shows S.palustre, displaying an increase in both the visible and NIR
reflectance. The spectral signature for day 10 still retains the typical shape. There has
been an increase in 0.01 BRF at 545nm and 0.05 BRF at 740nm.
Figure 5.10 shows S.subnitens. This graphs exhibits increased reflectance in both the
visible and NIR regions and retains its shape. The visible section of this spectral signature
does look a little strange, as it doesn‟t display a blue absorption feature and the green
peak is really a red peak at 621nm due to anthocyanin pigments.
These four graphs show that a bigger distinctive change has occurred compared to the
control plants. There has been an increase in reflectance in both the visible and NIR
regions for most plants, with the red absorption feature staying the same.
35
S.cuspitdatum (15%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
Day 1
0.3
0.2
Day 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.7 Sphagnum cuspitdatum (15%) spectral reflectance curve
S.fallax (15%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
Day 1
0.3
0.2
Day 10
0.1
0
340
440
540
640
740
840
940
1040
wavelength (nm)
Figure 5.8 Sphagnum fallax (15%) spectral reflectance curve
36
S.palustre (15%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
Wavelength (nm)
840
940
1040
Figure 5.9 Sphagnum palustre (15%) spectral reflectance curve
Sphagnum subnitens (15%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.10 Sphagnum subnitens (15%) spectral reflectance curve
37
5.3.3 How do the 30% condition plants change?
Figure 5.11-5.14 shows the spectral reflectance of the Sphagnum plants on the first and
last day of the experiment under the 30% water reduction condition.
Figure 5.11 shows S.cuspitdatum demonstrating an increase in reflectance for both visible
and NIR regions. The increase in reflectance isn‟t as high compared to the 15% condition,
but the spectral signature looks allot flatter in comparison losing its distinctive features.
Figure 5.12 shows S.fallax. The graph shows an increase in the visible reflectance
although there appears to be very little change within the NIR region. This is the opposite
of what occurred within the 15% experimental condition as there was no change in the
visible section and an increase in the NIR region.
Figure 5.13 shows S.palustre, highlighting an increase in visible and NIR reflectance
although the red absorption feature doesn‟t change. Compared to the 15% experimental
condition there is a higher increase in reflectance of 0.02 BRF.
Figure 5.14 shows S.subnitens. This graph reveals an increase in the visible and NIR
regions, with the red absorption feature staying the same. Although the plant‟s spectral
reflectance looks really unhealthy on both days.
.
38
S.Cuspitdatum (30%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
Day 1
0.3
0.2
Day 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.11 Sphagnum cuspitdatum (30%) spectral reflectance curve
Sp.fallax (30%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
Day 1
0.3
0.2
Day 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.12 Sphagnum fallax (30%) spectral reflectance curve
39
S.palustre (30%) spectral reflectance curve
0.6
BRF (reflectance)
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
0
340
440
540
640
740
840
940
1040
Wavelength (nm)
Figure 5.13 Sphagnum palustre (30%) spectral reflectance curve
S.subnitens (30%) spectral reflectance graph
0.6
BRF (reflectance)
0.5
0.4
DAY 1
0.3
0.2
DAY 10
0.1
6E-16
-0.1
340
440
540
640
740
840
940
1040
wavelength (nm)
Figure 5.14 Sphagnum subnitens (30%) spectral reflectance curve
40
5.4. Spectral indices
After this initial analysis, these trends were further investigated by creating vegetation
indices as mentioned in sections 2 and 4.
5.4.1. REIP
The REIP values were extracted from the first-order derivative by locating the highest
peak in the corresponding derivative spectra. In order to do this, the data was smoothed.
Figure 5.15 compares BRF and the smoothed BRF, simply showing that there is little
difference in the shape of the spectrum. The smoothed data, which is shown by the red
line, just removes noise from the data set. As derivatives provide an indicator of slope, if
the BRF data used are noisy, the derivative will magnify this noise, making the results
unreliable.
Figure 5.16 shows how the first derivative is related to the red edge, providing the REIP.
The bottom graph shows the first-order derivative for S.cuspitdatum with a peak at 697nm.
As derivatives reveal slope, the peak coincides with the maximum slope on the spectral
signature (top graph) which is within the red edge and is the wavelength position of the
REIP.
Comparing BRF and Smoothed BRF
0.6
0.5
BRF (reflectance)
BRF
0.4
0.3
BRF
smoothed
data
0.2
0.1
0
340
440
540
640
740
840
Wavelength (nm)
940
1040
Figure 5.15 comparison of BRF reflectance and smoothed BRF reflectance for Sphagnum
cuspitdatum within the control condition.
41
First-order derivative in relation to S.cuspitdatum (control) BRF
0.6
BRF (reflectance)
0.5
0.4
0.3
S.cuspitdatum
0.2
0.1
0
400
450
500
550
600
Wavelength (nm)
650
700
750
800
0.015
First-Order derivative
0.01
0.005
S.cuspitdatum
0
400
450
500
550
600
650
700
750
800
-0.005
-0.01
Wavelength (nm)
Figure 5.16 BRF and derivative of S.cuspitdatum control
42
Derivatives were calculated for every plant in every condition for everyday. Over the
duration of the experiment the derivative peak changes wavelengths.
5.4.2.1 How does the REIP value change in the control condition?
Figure 5.18 reveals the changes within the REIP position for the control group. For all
species the REIP moved into shorter wavelengths although, the movement is very small
varying between 0.34nm for S.cuspitdatum and S.fallax to 1.02nm for S.palustre. The
control plants move the least as they weren‟t under as much stress. Due to the small
changes, these weren‟t graphically displayed.
5.4.2.2 How does the REIP value change in the 15% condition?
Figure 5.17 and 5.18 show the changes that occurred to the REIP position within the 15%
water reduction condition. The left-hand side of figure 5.17 graphically reveal the changes
within the first-order derivative peak. In all the cases the peak has visibly moved to the left
into shorter wavelengths, although there are differences in amplitude. The derivative
peaks for S.subnitens and S.palustre have all increased in amplitude, while S.cuspitdatum
and S.fallax have slightly decreased. Figure 5.18 numerically displays the changes that
have occurred. The REIP values for the 15% condition have moved further into smaller
wavelengths compared to the control condition. S.fallax has moved the most, with the
REIP at 677.57nm on day 1 and at 672.79nm on day 10 meaning a movement of 4.78nm.
5.4.2.3 How does the REIP value change in 30% condition?
Figure 5.17 and 5.18 show the changes that occurred to the REIP position within the 30%
water reduction condition. The right-hand side of figure 5.17 displays the changes in the
first-order derivative peak graphically, showing every peak has moved into smaller
wavelengths. The most noticeable change is in amplitude, with S.cuspitdatum,
S.subnitens and S.palustre increasing the amplitude of the peak, which coincides with
figures 5.11, 5.14 and 5.13 as the red edge becomes steeper with an increase in NIR
reflectance. Figure 5.18 numerically shows the change revealing, that S.fallax has once
again moved the most. Unexpectedly, S.subnitens in both the 15% and 30% experiment
conditions have both moved into shorter wavelengths by 2.04nm.
These results agree with previous work by Bryant and Baird (2003) who detected a
maximum change of 5nm in the REIP position over the course of a 12 day experiment.
Although their results suggest that the amplitude of the day 12 derivative peak is rather
constant across the range of Sphagnum species, whereas these results don‟t agree.
43
Sphagnum cuspitdatum
S.cuspitdatum (15%)
DAY 10
0.005
2E-17
660
680
700
720
740
-0.005
-0.01
First-order derivative
First-order derivative
DAY 1
0.01
640
S.cuspitdatum (30%)
0.015
0.015
DAY 1
0.01
DAY 10
0.005
0
640
660
-0.01
Wavelength (nm)
680
700
720
740
-0.005
Wavelength (nm)
Sphagnum fallax
S.fallax (15%)
DAY 1
0.01
DAY 10
0.005
2E-17
640
660
680
700
720
740
-0.005
-0.01
S.fallax (30%)
0.015
First-order derivatives
first-order derivative
0.015
DAY 1
0.01
DAY 10
0.005
2E-17
640
660
-0.01
Wavelength (nm)
680
700
720
740
-0.005
Wavelength (nm)
Sphagnum subnitens
S.subnitens (15%)
First-order derivative
DAY 1
0.01
DAY 10
0.005
2E-17
640
660
680
700
720
740
-0.005
S.subnitens (30%)
0.015
First-order derivative
0.015
-0.01
DAY 1
0.01
DAY 10
0.005
2E-17
640
660
680
700
720
740
-0.005
-0.01
Wavelength (nm)
Wavelength (nm)
Sphagnum 44palustre
S.palustre (15%)
S.palustre (30%)
First-order derivative
DAY 1
0.015
DAY 10
0.01
0.005
2E-17
-0.005
640
660
680
700
720
740
First-order derivative
1
0.02
DAY 1
0.8
DAY 10
0.6
0.4
0.2
0
640
-0.01
Wavelength (nm)
-0.2
660
680
700
720
740
Wavelength (nm)
Figure 5.17 Changes in the first-order derivative peak, which corresponds to REIP in the
30% and 15% experimental conditions
44
Sphagnum species and
experimental condition
S.cuspitdatum (control)
S.fallax (control)
S.palustre (control)
S.subnitens (control)
S.cuspitdatum (15%)
S.fallax (15%)
S.palustre (15%)
S.subnitens (15%)
S.cuspitdatum (30%)
S.fallax (30%)
S.palustre (30%)
S.subnitens (30%)
Position day 1
(nm)
Position day 10
(nm)
Control condition
697.94
697.6
678.25
677.91
691.51
690.49
688.28
687.67
15% experimental condition
696.25
694.22
677.57
672.79
687.44
686.08
678.93
676.89
30% experimental condition
677.91
674.84
680.3
675.18
682.00
679.27
685.74
683.7
Movement to
smaller
wavelengths
(nm)
0.34
0.34
1.02
0.61
2.03
4.78
1.36
2.04
3.07
5.12
2.73
2.04
Figure 5.18 Movement of the REIP in all plants and experimental conditions
5.4.2. PRI, CI, fWBI
PRI, CI, and the fWBI were all calculated using equations within section 2 and 4 from the
BRF data for every plant, daily.
Figures 5.19-5.22 reveal the changes in the fWBI for every plant. Initially on the first day
the fWBI value for the control plants is the highest, followed by the 15% water reduction
condition and then the 30% water reduction condition. This follows the pattern of
correlation between the spectral index and water content (Penuelas et al, 1997). Although
surprisingly, throughout the course of the experiment this changes and isn‟t always the
case. Figure 5.19 and 5.21 reveal that on day 5 of the experiment the fWBI value for
S.cuspitdatum and S.subnitens 30% water reduction condition dramatically increased. In
both plants this has occurred on the day after watering and is a hysteric response, likely to
be caused by water retention. This concurs with previous work conducted by Harris et al
(2005) who found a hysteric response in S.magellanicum after the samples were rewetted.
45
Changes in fWBI values for S.cuspitdatum
3
2.5
fWBI value
2
Control condition
1.5
15% condition
30% condition
1
0.5
0
1
2
3
4
5
6
7
8
Experimental days
9
10
Figure 5.19 Changes in the fWBI for S.cuspitdatum for all experimental conditions
Changes in fWBI for S.fallax
2
fWBI value
1.5
Control condition
1
15% condition
30% condition
0.5
0
1
2
3
4
5
6
7
8
9
Experimental days
10
Figure 5.20 Changes in the fWBI for S.fallax for all experimental conditions
Changes in fWBI for S.subnitens
2.5
fWBI value
2
1.5
Control Condition
15% conditon
1
30% condition
0.5
0
1
2
3
4
5
6
7
8
9
10
11
Experimental days
Figure5.21 Changes in the fWBI for S.subnitens for all experimental conditions
46
Changes in fWBI for S.palustre
2
fWBI values
1.5
Control condition
1
15% condition
30% condition
0.5
0
1
2
3
4
5
6
7
8
9
10
Experimental Days
Figure 5.22 Changes in the fWBI for S.palustre for all experimental conditions.
Figures 5.23-5.26 display the changes in CI values for every plant. It was expected that
the CI index would decrease in the 15% and 30% water reduction conditions however, this
isn‟t the case as the graphs reveal. Figures 5.24 and 5.25 for S.fallax and S.subnitens
show that for the majority of the experiment the CI value for the control condition is lower
than the other conditions. These results seem to indicate that there is a critical threshold
where there is too much water causing low CI values, which is the case with the control
condition. This is also explained by the 15% water reduction condition having the highest
CI values, possibly being the optimum condition. While, the 30% water reduction condition
has lower values because it is below the optimum water condition.
Changes in CI for S.cuspitdatum
0.35
0.3
0.25
CI value
0.2
control condition
0.15
15% condition
0.1
30% condition
0.05
0
1
2
3
4
5
6
7
8
Experimental days
9
10
Figure 5.23 changes in CI for S.cuspitdatum for all experimental conditions
47
Changes in CI for S.fallax
0.2
CI value
0.15
control condition
0.1
15% condition
30% condition
0.05
0
1
2
3
4
5
6
7
8
9
Experimental days
10
Figure5.24 changes in CI for S.fallax for all experimental conditions
Changes in CI for S.subnitens
0.35
0.3
CI value
0.25
0.2
control condition
0.15
15% condition
0.1
30% condition
0.05
0
1
2
3
4
5
6
7
8
9
Experimental days
10
11
Figure 5.25 Changes in CI for S.subnitens for all experimental conditions
Changes in CI for S.palustre
0.2
CI value
0.15
Control condition
0.1
15% condition
30% condition
0.05
0
1
2
3
4
5
6
7
8
9
10
Experimental days
Figure 5.26 changes in CI for S.palustre for all experimental conditions
48
PRI hasn‟t been graphically represented here as it doesn‟t show any relevant patterns
throughout the experiment. For this reason, statistical testing was undertaken to see if
there is a significant relationship between PRI, CI and fWBI and plant weight. Plant weight
was chosen because it varies according to the amount of water given and consequently
the experimental condition
5.5 Statistical Testing
Before statistical testing could occur, it was imperative to find out if the data was
parametric or not, so normal distribution was tested.
5.5.1 Normal distribution
As described in the method a range of descriptive and dispersive statistics were
calculated, for every plant‟s weight, CI, PRI, fWBI. Figure 5.27 summarises these statistics
for one plant. The table reveals that neither weight, CI, PRI, nor fWBI is normally
distributed. This is because the mean, median and mode aren‟t the same number and
because the skewness values aren‟t 0 and kurtosis values aren‟t 3. Figure 5.28 shows zscore histograms for weight, CI, PRI, and fWBI, clearly demonstrating that weight, CI, and
PRI are negatively skewed while fWBI is positively skewed. These two figures clearly
demonstrate that weight, CI, PRI, and fWBI aren‟t parametric; this was the same for every
plant. Due to this, spearman‟s rank correlation co-efficient was used to test the
relationship between the vegetation indices and plant weight.
Mean
Median
Mode
Skewness
Kurtosis
Weight
361.778
364
N/A
-0.515
-0.669
CI
0.196
0.187
N/A
-0.321
-0.044
PRI
0.113
0.130
N/A
-2.722
8.173
fWBI
1.745
1.594
N/A
2.351
5.766
Figure 5.27 Normal distribution summary statistics for Sphagnum Palustre from the
control condition for weight, CI, PRI, and fWBI
49
4
5
PRI
fWBI
4
Frequency
Frequency
3
2
1
3
2
1
0
0
-0.5
-0.25
0
0.25
0.5
0.75
-1
1
-0.5
0
0.5
1.5
2
2.5
3
z-score bins
Z-score bins
5
6
CI
Weight (g)
4
5
4
3
Frequency
Frequency
1
2
1
3
2
1
0
0
-2
-1
0
1
Z-score bins
2
-1.5
-1
0
1
1.5
Z-score bins
Figure 5.28 Z-score histograms for Sphagnum Palustre from the control condition.
5.5.2 Spearman‟s Rank
5.5.2.1 Control experimental condition
Figure 5.29 shows all the correlations for the control condition. Every vegetation index for
every plant has a critical Rs value of 0.683 when the degrees of freedom is 9,
consequently as the observed Rs value is higher there is a significant correlation between
the spectral indices and plants weight at the 95% confidence level. Although, there are two
exceptions. Firstly, there isn‟t a significant correlation between fWBI and S.cuspitdatum
and secondly, there isn‟t a significant correlation between CI and S.subnitens.
50
Spectral index
Rs value
Critical value
Significance
Sphagnum Cuspitdatum
PRI
1.658
0.683
Significant
fWBI
0.667
0.683
Insignificant
CI
1.958
0.683
Significant
Sphagnum fallax
PRI
1.250
0.683
Significant
fWBI
0.764
0.683
significant
CI
1.225
0.683
Significant
Sphagnum Palustre
PRI
1.483
0.683
Significant
fWBI
0.866
0.683
Significant
CI
1.450
0.683
Significant
Sphagnum subnitens
PRI
0.892
0.683
Significant
fWBI
0.768
0.683
Significant
CI
0.175
0.683
Insignificant
Figure 5.29 Significance of the correlations in the control experimental condition between
Sphagnum weights measured throughout the experiment and spectral indices
5.5.2.2. 15% experimental condition
Figure 5.30 shows all the correlations for the 15% water reduction condition. Every
vegetation index for every plant has a critical Rs value of 0.683 when the degrees of
freedom is 9, consequently as the observed Rs values are higher there is a significant
correlation between the vegetation indices and plants weight at the 95% confidence level.
Although, there are two exceptions. Firstly, there isn‟t a significant correlation between
fWBI and S.cuspitdatum and secondly, there isn‟t a significant correlation between CI and
S.subnitens.
Spectral index
Rs value
Critical value
Significance
Sphagnum cuspitdatum
PRI
0.694
0.683
Significant
fWBI
0.533
0.683
Insignificant
CI
1.959
0.683
Significant
Sphagnum fallax
PRI
1.000
0.683
Significant
fWBI
1.183
0.683
Significant
CI
1.217
0.683
Significant
Sphagnum Palustre
PRI
1.415
0.638
Significant
fWBI
1.433
0.683
Significant
CI
1.783
0.683
Significant
Sphagnum subnitens
PRI
1.683
0.683
Significant
fWBI
1.550
0.683
Significant
CI
0.667
0.683
Insignificant
Figure 5.30 Significance of the correlations in the 15% water reduction experimental
condition between Sphagnum weights measured throughout the experiment and spectral
indices.
51
5.5.2.3 30% experimental condition
Figure 5.31 shows all the correlations for the 30% water reduction condition. Every
vegetation index for every plant has a critical Rs value of 0.683 when the degrees of
freedom is 9, consequently as the observed Rs values are higher there is a significant
correlation between the vegetation indices and plants weight at the 95% confidence level.
Although, there is one exception as there isn‟t a significant correlation between CI and
S.subnitens.
Spectral index
Rs value
Critical value
Significance
Sphagnum cuspitdatum
PRI
1.656
0.683
Significant
fWBI
0.801
0.683
Significant
CI
1.085
0.683
Significant
Sphagnum fallax
PRI
1.166
0.683
Significant
fWBI
1.333
0.683
Significant
CI
1.045
0.683
Significant
Sphagnum palustre
PRI
1.417
0.683
Significant
fWBI
0.952
0.683
Significant
CI
1.143
0.683
significant
Sphagnum subnitens
PRI
0.694
0.683
Significant
fWBI
1.133
0.683
Significant
CI
0.267
0.683
Insignificant
Figure 5.31 Significance of the correlations in the 30% water reduction experimental
condition between Sphagnum weights measured throughout the experiment and spectral
indices.
52
6.Discussion .
Previous chapters within this research have described and analysed methods and
observations whereas this chapter aims to put the findings within the context of wider
research and suggest implications.
6.1. Discussion of the results
6.1.1. Changes in Spectral reflectance
The spectral reflectance curves in figures 5.3-5.14 reveal the changes within the
reflectance of every plant under every experimental condition. The biggest changes have
occurred within the 15% and 30% experimental conditions because they received the least
water. This resulted in an increase in reflectance in visible and NIR regions for most
plants. These results agree with previous work by Bryant and Baird (2003) and Harris et al
(2005).
6.1.2. Red edge infection point (REIP)
Figure 5.18 shows that for all Sphagnum samples, the REIP moved to shorter
wavelengths throughout the experiment. The control condition moved the least, and this
movement would have been caused by stress due to being part of the experiment, while
the 30% experimental condition moved the most. For all experimental conditions of
S.subnitens the REIP is in the shortest wavelengths and for the 15% and 30%
experimental conditions the REIP has moved 2.04nm, which can be explained by
secondary pigmentation. S.subnitens has a reddish appearance (Figure 5.1a) due to
anthocyanin within the cell walls, which is associated with drought resistance (Martensson
and Nilsson, 1974). Previous research on broadleaf Amaranthus leaves with similar red
secondary pigments reveals that there is no relationship between the REIP and
chlorophyll concentrations, as the secondary pigment acts like chlorophyll by increasing
the absorption of visible radiation therefore moving the REIP to longer wavelengths. This
demonstrates that when chlorophyll levels decrease due to stress the blue shift that
normally occurs, cannot be detected as the relationship between the red edge and
chlorophyll concentration is weak for dual pigment plants, like S.subnitens (Curran et al,
1991).
6.1.3. Chlorophyll Index (CI)
The spearman‟s rank correlations (Figure 5.29-5.31) reveal significant positive correlations
between plant weight and CI throughout the experiment. This suggests a decrease in
chlorophyll concentrations as the experimental condition plants started to dehydrate,
reducing the plants weight throughout the experiment. Within Sphagnum canopies, as
53
moisture decreases, water is lost through hyaline cells (Murray et al, 1989). If water in the
hyaline cells is in equilibrium with tissue water, water will leave the chlorophyllous tissue,
which closely corresponds with reductions in net photosynthesis (Murray et al, 1989) and
therefore decreases the CI value.
However, there is an exception; regardless of the experimental condition there isn‟t a
significant correlation between CI and S.subnitens. This result is frustrating because
S.subnitens could contain higher chlorophyll levels compared to the other hollow species,
because of its better water holding capabilities (Hakek and Beckett, 2008). As explained
previously S.subnitens acquires it reddish appearance due to anthocyanin, which acts like
chlorophyll by increasing absorption of red light (Martensson and Nilsson, 1974). This can
be seen by Figure 5.2 where S.subnitens has an extended green peak from 548-610nm
and a bigger red absorption feature. Therefore the secondary pigmentation complicates
the relationship between chlorophyll concentrations and spectral reflectance (Gitelson et
al, 2004). This is an issue with the spectral index because during the research stages only
healthy homogenous in colour leaves without any traces of secondary pigmentation were
use within the experiment (Gitelson et al, 2004), when a range of plants leaves should
have been tested. This result therefore has implications for monitoring Exmoor National
Park as S.subnitens isn‟t the only species strongly influenced by secondary pigmentation
and consequently anthocyanin should be monitored using a spectral index developed by
Gitelson et al (2001).
The bar graphs (Figure 5.24 and 5.25) for S. fallax and S.subnitens reveal the CI values
for the control conditions are lower than the 30% water reduction condition for the majority
of the experiment. This is probably caused through the human error of over watering the
plants. During the experiment, all the control plants where kept within a bowl filled with
water ensuring that S.cuspitdatum, as it was collected from a bog pool was nearly
submerged. However, S.fallax is a carpet species and S.subnitens is a hummock species
therefore not requiring as much water (Daniels and Eddy, 1991). This has been
demonstrated in field studies where high levels of water had become sub-optiminal for
photosynthesis (Murray et al, 1989).
6.1.4. Floating Water Band Index (fWBI)
Figures 5.29-5.31 reveal the correlations for every plant and changes within the fWBI
throughout the experiment. There are significant correlations for every plant except for
S.cuspitdatum within the control and 15% experimental condition. This is very surprising,
although previous research looking at spectral indices hasn‟t used such an extreme bog
pool species. It is therefore believed that there isn‟t a correlation because it is a pool
species or because this experiment simply hasn‟t provided enough data to suggest a
54
significant correlation. This result is surprising as previous research by Harris et al (2005)
had the most significant correlations between fWBI and Sphagnum samples. It is also
important to note, that previous research has shown correlation coefficients between fWBI
and plant water content are stronger for controlled experiments than for plants within their
natural environment (Penuelas et al, 1992). This therefore suggests that the strength of
the correlations found here would decrease for any follow up work using in-situ field
spectroscopy to monitor peatlands within Exmoor National Park.
Figures 5.19 and 5.21 reveal a hysteric response to watering for S.subnitens and
S.cuspitdatum, caused by water retention. Hysteresis occurs when drying peats tend to
hold on to water, whereas very dry peats tend to resist rewetting (Rydin and Jeglum,
2009).This can be seen in the two figures, as the control condition for S.cuspitdatums
fWBI index value dramatically increases on day 8 after being watered on day 7. While the
S.subnitens 30% water reduction condition fWBI value dramatically increases on day 5,
after being watered on day 4, as the plants are holding onto their water. This occurs as
Sphagnum contains large numbers of capillary spaces or pores formed by pendent
branches which hang down the stem but when water is added to the canopies, the
capillary spaces hold the water against the pull of gravity therefore causing higher levels of
moisture (Hayward and Clymo, 1982; Harris et al, 2005). Similar results have also been
observed by Harris et al (2005) where as hummock species like S.subnitens experienced
hysteresis, as these species tend to have more capillary spaces. Although, the surprising
result is that the control condition for S.cuspitdatum experienced hysteresis, because it
has a relatively open canopy, lacking pendent branches and therefore capillary spaces
(Hayward and Clymo, 1982). This result has significant implications for the timing of image
acquisition over peatlands, as images collected just after a rainfall event may result in
over-estimation of the plants health and therefore near-surface wetness, due to hysteresis.
The fWBI values decrease the least for S.subnitens, compared to the hollow species, this
can seen in figures 5.19-5.22. These differences are believed to be the result of
differences in cellular elasticity. Large decreases within the fWBI as seen in the hollow
species (S.cuspitdatum, S.palustre and S.fallax), means a greater decrease in cellular
elasticity. Lower cellular elasticity means that the plant is less able to respond to changes
in available moisture, which is common in hollow species (Hayward and Clymo, 1982;
Harris et al, 2005; Hajek and Beckett, 2008).
6.1.5. Photochemical reflectance index (PRI)
Figures 5.29-5.31 reveal that for every plant under every experimental condition, there
was a significant correlation between the plants weight and PRI. When photosynthesis
declines for example in this case due to drought, the absorbed PAR exceeds the capacity
55
of photosynthetic reactions and the xanthophyll cycle pigment violaxanthin de-oxidizes into
zeaxanthin via antheraxanthin (Gamon et al, 1992). A decrease in PRI is indicative of an
increase these xanthophyll cycle pigments in their de-epoxidized state (Harris, 2008).
Consequently, therefore as the samples weight decreases, PRI decreases, which
indicates a decline in photosynthesis. This research has clearly shown that PRI is the best
index.
6.2. Evaluation
6.2.1. Climate chamber
Controlled environments provide long-term, stable conditions, with clear differences
between different experimental conditions. Although this is advantageous for
understanding complex systems and for modelling, the conditions are often substantially
different from those within the field (Lawlor and Mitchell, 1991). These differences with
details specific to the climate chamber used within this experiment are displayed in figure
6.1. Previous work looking at climate change occurring within in-situ and within a climate
chamber, found slight differences between the two results (Lawlor and Mitchell, 1991).
This underlines the need to confirm that responses to decreased precipitation caused by
climate change can be detected in-situ using remote sensing.
6.2.2. Parametric testing
The results from the normal distribution testing reveal that the vegetation indices and
weight weren‟t parametric; consequently spearman‟s rank correlation coefficient was used.
However, parametric tests are stronger and thus preferred. As a result of this, the
vegetation indices and weight should have been mathematically transformed so
parametric testing was applicable (Ebdon, 1999).
6.2.3. Experimental light
Every day the spectral reflectance of the Sphagnum samples were measured, within the
dark room under a 3400KW dedolight. As already mentioned it was important to ensure
the plants spent as little time in the dark room as possible. Although there were
extenuating circumstances on a couple of occasions, when for example there were
technical difficulties with the spectrometer, however this can dramatically alter
photosynthesis. A recent study highlights this, using Bryophytes the authors found that it
only took 3 minutes for mosses to reach 90% photosynthetic induction after a sudden light
increase of light from 50-600µmol m-2 s-1. This is a lot faster compared to non-vascular
plants which took up to 37mins (Cui et al, 2009). Therefore even a small amount of time
56
under the dedolight could have altered the Sphagnum samples photosynthesis making the
results unreliable.
6.2.4. UKCP probability and climate models
This research is based on results from UKCP09 which is based on HadCM3, although no
model is perfect, because our knowledge of Earth isn‟t perfect. For example there are
studies demonstrating that water vapour will increase due to climate change but scientists
are unsure if it will stay in the atmosphere or leave as precipitation (Ananthaswamy,
2011). This off course leads to uncertainty within the results. The results generated by
UCKP09 have a probability level of 67%, which means 67% of the model runs fell at or
below that 15% and 30% reduction in summer precipitation (UKCP09 probability, 2011).
6.2.5. Standardisation
This research has generated results that are similar and dissimilar to previous research,
however there is a need for standardisation of sampling instruments and methods to ease
comparison. At a field scale to determine the degree of comparability between three
spectrometers; leaf spectra from three different species were recorded for each
instrument, using two illumination, viewing and FOV scenarios (Castro-Esau et al, 2006).
The results reveal significant differences in both spectral reflectance and spectral indices‟
which are attributable to both instrument and sampling method, with some wavelengths
and indices varying more than others (Castro-Esau et al, 2006). This demonstrates that
care should be exercised when comparing indices generated from different spectra
measured using different instruments.
57
Light
Controlled environment
Often low intensity
The climate chamber had a light
intensity of 2.1Klux.
Constant
The four bulbs in the climate chamber
provided constant light.
Spectral differences from daylight
The four light bulbs will have spectral
differences from daylight although the
light bulbs were visible light and
therefore coincide with PAR.
Field environment
Very high intensity
When measured on an overcast day
there was a max of 131.1Klux and
min of 52.2Klux.
High variability
As highlighted above, there is high
variability between the maximum and
minimum light intensity due to cloud
cover.
Spectrally varied
Sunlight covers the whole
electromagnetic spectrum.
Temperature
Constant during day and night
The climate chamber had a constant
0
0
temperature of 19 C and 10 C during
the day and evening. This is a
0
dramatic shift of 9 C, which just
doesn‟t occur in-situ
Very variable through season and
from day to day
Temperatures in-situ vary. For
example the sun is hotter at solar
noon because it‟s direct rays
whereas in the morning and evening
the earth receives slanted rays.
Humidity
No control
The climate chamber couldn‟t control
humidity although relative humidity
varied between 45-71%.
Varies
Varies due to changes in
temperature.
Wind
No Wind
Within the climate chamber there was
no.
Wind
Wind speeds vary due to pressure
gradients.
Nutrition
No application
Within the climate chamber the only
nutrition the plants received was from
tap water, when they were watered
according to their experimental
condition.
Application through rain and
groundwater flow
Rainwater and groundwater flow
provide macronutrients (N,K,P) and
micronutrients (Fe, Zn, Mn, etc...)
which aren‟t as well provided by tap
water.
Large
Rooting volume
Very small
The only rooting volume the plants
had was within their plant pot, which
was filled.
Table 6.1. The main environmental differences between controlled environments and in the field
(adapted from Lawlor and Mitchell, 1991)
58
6.3 Further research
This research has considered how the spectral reflectance of Sphagnum mosses change
under desiccation providing thorough results, although there is potential for further
research. In order to extend this study, these ideas could also be expanded:
The Ocean Optics USB 2000 fiber optic spectrometer provided useful
hyperspectral data for this project, between 330-1030nm. However, if this project
were to be expanded a spectrometer with a longer detection range, such as the
Analytical Spectral Device (ASD) FieldSpec Pro, which has a range of 350-2500nm
should be used. This device would allow further research into the species specific
short-wave infrared reflectance increase and decrease in absorption features
located at 1200, 1450m 1920nm caused by desiccation while also allowing better
comparison with previous research by Harris et al (2005). Instead of weight,
volumetric water content (VMC) should also be used.
Harris and Bryant (2009) followed up their small scale correlations between
changes in Sphagnum water content and changes in spectral reflectance, by upscaling their research to field spectroscopy and finally airborne remote sensing of a
bog. Their work revealed that the strength of the correlations decreased as the
spatial scale increased but still demonstrated that this application of remote
sensing has the potential to monitor whole peatland ecosystems. Upscalling this
research would provide incredibly valuable hydrological information for monitoring
blanket bogs across the whole Exmoor National Park, while helping scientifically by
providing more research into the heterogeneity of Sphagnum canopies within the
field.
The „Jenkensen Effect‟ ccurs when soil respiration rates increase, leading to a net
loss of soil carbon, increasing atmospheric CO2, and global warming. Until recently
soil biological heating generated by microbial respiration, had been excluded from
climate change models, although a tipping point aptly called the „compost bomb
instability‟ can arise. A recent modelling study by Luke and Cox (2011) revealed
this feedback occurs when heat is biologically generated within the soil more
quickly than it can escape to the atmosphere, which could eventually lead to
peatland fires and therefore the abrupt oxidation of soil carbon into atmospheric
CO2.. This research also highlighted the idea that reducing soil moisture
suppresses soil heat conductivity, drawing peatland ecosystems closer to instability
while calling scientists to demonstrate the potential instability through laboratory
experiments (Luke and Cox, 2011). Therefore a final topic that would be beneficial
59
for examination is how the spectral reflectance of Sphagnum varies under
increased temperatures, to further our understanding.
60
7. Conclusion
This dissertation aimed to assess the impact that climate change could have on the
moisture status of Sphagnum samples from Exmoor National Park and how this is linked
to spectral reflectance indices (CI, REIP, PRI, fWBI). The answers to the hypotheses,
stated at the beginning of this research, are as follows:
1. For single Sphagnum species (cuspitdatum, fallax, palustre, sub-nitens)
moisture status does not influence spectral reflectance.
This null hypothesis can be rejected as figures 5.2-5.13 reveal that moisture status
does influence the plants spectral reflectance although there is little change for the
control condition. Both the 15% and 30% water reduction condition show increases
in visible and NIR reflectance.
2. For single Sphagnum species there is no significant relationship between
chlorophyll index (CI), photochemical reflectance index (PRI), and water band
index (WBI) and moisture status.
This null hypothesis can be rejected, mostly. There are significant relationships
between the spectral indices and moisture for the majority of the plants (figures
5.29-5.31). Although the exceptions are as follows: S.subnitens (control, 15% and
30% experimental conditions) and CI, and S.cuspitdatum (control and 15%
experimental conditions) and fWBI.
3. There is no significant difference in spectral reflectance between hummock
and hollow species.
This null hypothesis can be rejected because there is a significant difference
between hummock and hollow species, which can be seen in figure 5.1. The
difference is also present within the spectral indices. S.subnitens secondary
pigmentation means that the REIP and CI don‟t work as well as they do on hollow
species. The results for the fWBI reveal hysteresis within S.subnitens due to its
morphology, although this was also seen in S.cuspitdatum.
This research has provided a wealth of information that can help monitor Exmoor
specifically. The results can be used to select optimum spectral bands for further research,
which should be located around 531 and 570nm, so PRI can be calculated to demonstrate
changes in Sphagnum photosynthesis. These results are important, although other
methods of remote sensing should also be considered for example LiDAR, which detects
changes in surface structure
61
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Appendix A
name: Blackpitts G
Date: 11 /6/09
Eg. Quercus robur
Pendunculate Oak
Molinia caerulea
Purple Moor Grass
Narthecium ossifragum
Potentilla erecta
Juncus effusus
Quadrat No:
Domin Values
Constancy
Range
MIN
-
MAX
5
4
-
4
Bog Asphodel
4
1
-
4
Tormentil
4
1
-
4
Soft Rush
4
1
-
4
4
1
-
4
Hypnum cupressiforme
Eriophorum angustifolium
Bog Cotton-grass
3
1
-
4
Eriophorum vaginatum
Hare's tail
3
1
-
4
3
1
-
4
Sphagnum palustre
3
1
-
4
Anthoxanthum odoratum
Sweet Vernal
3
1
-
3
Polygala serpyllifolia
Milkwort
2
1
-
4
Agrostis spp.
Bent Grasses
2
1
-
4
Deschampsia flexuosa
Wavy-hair grass
2
1
-
4
Trichophorum cespitosum
Deer Grass
2
1
-
4
Carex nigra
Common Sedge
2
1
-
4
2
1
-
4
Sphagnum papillosum
Isopterygium elegans
Sphagnum fallax
Ex- Sphagnum recurvum
2
1
-
4
Carex panicea
Carnation Sedge
2
1
-
3
Aulacomnium palustre
2
1
-
3
Sphagnum capillifolium
1
4
-
4
Heath Woodrush
1
2
-
4
Carex echinata
Star Sedge
1
2
-
3
Festuca spp
Fescue
1
2
-
2
Heath Rush
1
2
-
2
Rhytidiadelphus squarrosus
1
2
-
2
Viola palustris
1
1
-
4
Liverwort
1
1
-
4
Erica tetralix
1
1
-
3
Sphagnum subnitens
1
1
-
3
Sphagnum tenellum
1
1
-
3
1
1
-
2
Mnium hornum
1
1
-
2
Polytrichum commune
1
1
-
2
Cirsium palustra
Marsh Thistle
1
1
-
1
Galium saxatile
Heath Bedstraw
1
1
-
1
Vaccinium myrtillus
Whortleberry
1
1
-
1
Juncus acutiflorus
Sharp-flowered Rush
1
1
-
1
1
1
-
1
1
-
1
Luzula multiflora
Juncus squarrosus
Holcus lanatus
Marsh Violet
Cross-leaved Heath
Yorkshire Fog
Campylopus paradoxus
Asplenium adiantum-nigrum
Black Spleenwort
Species richness
1
39
M25
(Source: David Smith, Mires on the Moor Officer)
68