CTCD Terrestrial Carbon Cycle and Earth Observation
summer school
18-26th August, 2005
Editors
Mat Williams (University of Edinburgh, UK) & Andreas Heinemeyer (University of York, UK)
Organisers
Mat Williams
Andreas Heinemeyer
Staff
Phil Ineson (University of York),
John Grace, Dave Reay (University of Edinburgh)
Phil Lewis, Paul Bowyer, Tristan Quaife (UCL)
Andrew Fox (University of Sheffield)
Dave Reay (University of Edinburgh)
Steve Dobson and John Jones (University of York)
Acknowledgments:
We would like to thank all funding bodies who made this summer school possible:
(i) NERC (UK)
(ii) ESF
(iii) QUEST (UK)
Pictures:
Most of the pictures in this manual are produced from licensed data – no photocopying of this material is allowed.
Cover picture:
Pen-y-ghent field site looking towards Blishmire and Overdale Moor; inset: Malham Tarn, Field Studies Council
field centre (Andreas Heinemeyer).
Index
Introduction ................................................................................................ 2
Course structure and timing ....................................................................... 4
Eddy Covariance ...................................................................................... 13
Measuring Soil Carbon Stocks ................................................................. 17
Measuring Vegetation Carbon Stocks and Leaf Area ............................... 20
NEP chamber........................................................................................... 25
Soil Respiration ........................................................................................ 29
Methane in the Global Carbon and Greenhouse Gas Budget .................. 38
Spatial measurement of Leaf Area using Earth Observation .................... 42
Introducing Ground-Penetrating Radar .................................................... 51
1
Introduction
The goal of the summer school is for students to investigate the processes that drive the
terrestrial carbon (C) cycle, and to learn the tools available to quantify the associated processes
and measure carbon stocks. We will work in an upland heather catchment of a few hectares,
small enough to survey and measure, but large enough to incorporate much of the complexity
associated with the current science of terrestrial C dynamics. The field work will mainly focus
on a 1 km2 area around an Eddy flux tower site, based on a UK national grid reference used for
the UK national carbon inventory. However, some work might be done in a slightly larger 4 km2
area around the core site. You will work in a team of students, rotating among various scientific
activities within the catchment, so that you gain experience with, and data from, each.
Synthetic questions
At the end of the week, you will have worked with an array of instrumentation and gathered
associated data. Your task is to synthesise these data to produce a summary of the catchment C
exchanges over the previous seven days, answering the following questions:





Was the catchment a net sink or source of C?
How certain are you of your estimate of C source or sink strength?
Was the catchment relatively uniform in its behaviour, or were there significant differences
within it?
What fraction of the total C stored in the catchment turned over during the week?
In terms of global warming potential, was CO2 or CH4 the more significant form of
exchanged C?
On the final day of the summer school, you and your team will present your synthesis to the staff
and other students. You will also be presented with a CD containing all the information acquired
during the week and all the presentations given.
Learning Objectives of the Summer-School
The summer school has a series of learning objectives – at the end of the school you should be
able to do the following:
1. Measure C fluxes using multi-chamber soil respiration systems, eddy flux systems, and
chamber CO2 flux systems and to understand the linkage of fluxes to key environmental
factors.
2. Measure C stocks in soils and vegetation.
3. Determine the spectral properties of canopies and relate these to remotely sensed data.
4. Synthesise data collected during the field campaigns to make inter-comparisons between
different measurement approaches.
5. To understand the major uncertainties of different key methods used in carbon budgeting.
Scientific Background
Importance of organic carbon-rich soils in the global carbon cycle
One of the greatest problems facing mankind is the impact of environmental change on
fundamental aspects of the biogeochemical cycle. Soil is a major component in the global carbon
(C) cycle, with about 1,500 Gt of organic C to a depth of 1 m and a further 900 Gt from 1-2 m[1],
and vulnerable to impacts of human activity. Globally twice as much carbon is stored in soils as
in the atmosphere[2] with peatlands contributing a third of this[3]. Thus even small changes in soil
C stocks might contribute significantly to global climate change, for example, due to a negative
2
feedback as a result of global warming[4]. Whereas above ground carbon cycling is well
understood, there is great uncertainty in climate impacts on soil carbon cycling[5]. For example,
the biological significance and relevance of the most critical C-turnover parameter used in soil
organic matter (SOM) sub-models of Dynamic Global Vegetation Models (DGVMs), the Q10
value, representing soil respiration responses of different C-pools to temperature, is not
sufficiently understood[6, 7]. More importantly, the understanding and process representation of C
cycling in organic C-rich soils is so far insufficiently included in only a few SOM models and
not incorporated in any DGVM.
Critical feedback: How important is the recalcitrant SOM pool?
Recent literature suggests that the decomposition of the more recalcitrant SOM pools is at least
as temperature-sensitive as the decomposition of the more labile carbon pools[9,10]. Model
predictions, however, show that the response of this “slow” carbon pool will determine the
degree to which terrestrial ecosystems will cause a positive feedback to global warming[4,11].
This slow pool represents very large carbon stores within terrestrial ecosystems, most notably in
peatlands. Therefore there is the potential for peatlands to respond in a much less transient way
to elevated temperature than other low-C soils. However, a number of recent isotopic studies
have highlighted the fact that a large percentage of the CO2 being respired by soils appears to
have been recently fixed by plants[8]. There appears to be little data on what determines the
relationship between photosynthesis and below-ground respiration.
Tackling uncertainties in C stocks and fluxes
Even relatively small uncertainties in soil C-stock and flux estimates lead to large uncertainties
in model predictions. Despite containing the largest soil C stocks, C cycling in organic C-rich
soils and its links to the environment (e.g. temperature and photosynthesis) are not incorporated
into global model predictions due to insufficient process understanding. Thus, there is an obvious
need to link scientists from different key research areas to work on an organic-rich C site (e.g. a
heather peatland) to: improve data acquisition, compare C flux measurement accuracies, improve
our general understanding about the uncertainties in the terrestrial carbon cycle, and find
measures to reduce them, all of which are key strengths of CTCD. This summer school will
provide up-to-date equipment as well as expertise gained from links to the CarboEuroflux group.
The course will focus around a field experimental campaign designed to address a series of
critical scientific and technical questions:
Science Questions
You will be working alongside scientists from the NERC Centre for Terrestrial Carbon
Dynamics (CTCD), and their colleagues, who - aside from training you - are also interested in
collecting data during the week to answer several key science questions related to C cycle
science.
1. What are the natural scales and processes behind ecosystem CO2 flux variability?
We want to know how heterogeneous the landscape is, as any variability influences the
resolution required for accurate assessments of regional C exchanges. Approach:
Compare chamber flux measurements with eddy-flux tower.
2. Can we distinguish overall CO2 efflux into fluxes from specific pools? (for instance,
separate net exchange of carbon in photosynthesis and respiration). Approach: Soil
respiration data, chamber data, & tower data.
3. Can canopy spectral data explain carbon flux variability? We want to know if
satellite observations can improve regional assessments of C exchange. Approach:
Compare measurements of leaf reflectance with gas exchange.
3
4. What is the uncertainty on assigning vegetation parameters across a landscape?
Approach: Compare detailed soil and vegetation surveys with remotely sensed data.
5. How effective is our up-scaling of ecosystem C dynamics? Can we divide the
landscape into functional units and upscale data from these to produce a reasonable
estimate of landscape activity? Approach: Characterisation of flux footprint into
dominant vegetation types via mapping and remote sensing, up-scaling of flux
predictions from leaf and chamber data, comparison to eddy-flux tower.Technical
challenges
There are considerable technical difficulties in quantifying C exchanges which you will learn
during the week. CTCD scientists and their colleagues are interested in isolating and reducing
problems. We will test some new methods (e.g. multiplexed soil respiration chambers) and
inter-compare existing approaches. We will also discuss the best approaches to simulation and
modelling.
1. Inter-comparison of chambers and eddy-flux system approaches to CO2 flux
measurement. Both approaches have advantages and problems.
2. Inter-comparison of a new multiplex system with a single chamber approach for soil CO2
efflux measurement.
3. What is the best chamber design to best measure spatial and temporal variability in soil
respiration (e.g. how do chamber size and measurement disturbance affect soil C efflux?)
4. How do we make best use of remote sensing data within the terrestrial carbon cycle
framework (including introductions to data analyses, evaluation and data assimilation)?
5. What are the limitations and advantages of different databases (e.g. spatial scale) and
model structures (e.g. accuracy of processes)?
Course structure and timing
Teams and general organisation:
1. The students are split into 6 teams (A-F), each with 5 or 6 members.
2. Each team is tasked with answering the synthetic questions (see above), and the science
questions if possible.
3. We have 6 activities (see below).
4. Each team spends 1 day on each activity.
5. There is one day to generate the synthesis.
However, some data might be measured (e.g. weight) the following day by another
group.
6. On the final morning each team presents their conclusions to the group.
Activities
1.
2.
3.
4.
5.
6.
Met station and eddy flux [Andy Fox, John Grace]
Soil respiration chambers [Andreas Heinemeyer]
NEP chamber [Mat Williams]
Soil and vegetation C stocks [Phil Ineson, John Grace]
Methane fluxes [Dave Reay]
Canopy characteristics and surface reflectance [Phil Lewis]
4
Also peat depth measurement with Ground Penetrating Radar (GPR) [Steve Dobson/
John Jones] Each activity is described in detail later in this manual.
Fieldwork organisation
Each day in the field you will be familiarised with equipment and trained in usage. You will
then collect data and, back at the lab, work with the staff to understand and analyse the data.
You will be encouraged to develop your own sampling design and scaling strategies to answer
the synthetic questions. There will be time each afternoon for data analysis - by the end of each
day you need to make sure you have completed the critical analyses.
Overall time table
There are six days allocated to the activities, then a day of synthesis, and presentations on the
final day.
Day
Date
Assignment
Thursday
18 August
Students arrive late. 8pm introductory meeting
Friday
19
1st activity
Saturday
20
2nd activity
Sunday
21
3rd activity
Monday
22
4th activity
Tuesday
23
5th activity
Wednesday
24
6th activity
Thursday
25
Synthesis
Friday
26
AM presentations, conclude at lunch, depart.
Teams and Activities
This table indicates which team (A-F) does which activity (1-6) on each day (19-24 August)
Date\Team
19 (Fri)
20 (Sat)
21 (Sun)
22 (Mon)
23 (Tue)
24 (Wed)
A
1
2
3
4
5
6
B
2
3
4
5
6
1
C
3
4
5
6
1
2
D
4
5
6
1
2
3
5
E
5
6
1
2
3
4
F
6
1
2
3
4
5
Daily timetable
07:30
08:30-09:20
09:30
10:00
12:30-13:00
Breakfast
Lecture (activity related)
Depart for the field
Fieldwork
Pack lunch
19:00
20:00
21:00
Return to FSC (~13:30-15:30)
Data analysis (until 18:00)
Dinner
Guest lecture
Bar
Lectures
To supplement the focussed experimental programme, we have two lecture series. Each
morning, from 0830-0920, there will be a lecture relating to one of the field activities, providing
more detailed insight into the techniques and the science. On five evenings there are guest
lectures on a broader range of topics:
Date
Lecturer
Title
Wolfgang Knorr
Modelling the terrestrial carbon cycle
19 (Fri)
Jens Arne Subke
C flux components and the environment
20 (Sat)
Uwe Franko
Modelling regional C-cycling and its uncertainties
21 (Sun)
The role of peatlands in the global carbon cycle (CH4 and
22 (Mon) Mike Billett
DOC)
Werner Kutsch
Challenges in ecosystem flux measurements: experiences
23 (Tue)
within CarboEurope
References:
[1] Miko UF Kirschbaum (2000) Will changes in soil organic C act as a positive or negative feedback
on global warming? Biogeochemistry 48 (1): 21-51.
[2] Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA
(eds). (2001). Climate Change 2001: The Scientific Basis. Cambridge, UK: IPCC, Cambridge University
Press.
[3] Gorham, E (1991). Northern peatlands: Role in the carbon cycle and probable responses to climatic
warming. Ecological Applications 1, 182-195.
[4] Cox PM, Betts RA, Jones CD, Spall SA, Totterdell IJ (2000). Acceleration of global warming due to
carbon-cycle feedbacks in a coupled climate model. Nature, 408, 184-187.
[5] Valentini R, De Angelis P, Matteucci G, Monaco R, Dore S, Scarascia Mungnozza GE (1996).
Seasonal net carbon dioxide exchange of a beech forest with the atmosphere. Global Change Biology, 2, 199207.
[6] Giardina CP and Ryan MG (2000). Evidence that decomposition of organic carbon in mineral soil
do not vary with temperature. Nature, 404, 858-861.
[7] Grace J and Rayment M. (2000). Respiration in the balance. Nature, 404, 819-820.
[8] Ekblad A, Hogberg P (2001) Natural abundance of C-13 in CO2 respired from forest soils reveals the
speed of link between tree photosynthesis and root respiration. Oecologia, 127, 305-308.
[9] Knorr W, Prentice IC, House JI, Holland EA (2005) Long-term sensitivity of soil carbon turnover
to warming. Nature, 433, 298-301.
[10] Fang C, Smith P, Moncrieff JB, Smith JU (2005) Similar response of labile and resistant soil
organic matter pools to changes in temperature. Nature, 433, 57-59.
6
[11] Eliassson PE, McMurtrie RE, Pepper DA, Stromgren M, Linder S, Agren GI (2005)
The response of heterotrophic CO2 flux to soil warming. Global Change Biology, 11, 167-185.
7
Site information
The study site is located in one of the most beautiful regions within England, the North
Yorkshire Dales National Park (established 1954). Please bear this in mind when sampling; we
are here to do science and not to destroy an area of outstanding natural beauty!
Field work will mainly focus on a 1 km2 grid (green square) on Blishmire and Overdale
where several measurement stations such as the Eddy flux tower (FT) have been set up. Most of
this area is on sedimentary Carboniferous limestone (~300 million years ago) and under the
upland climate (wet and cold most of the year) developed an important soil carbon sink with
large areas covered by peaty soils (mostly peaty gley and deep peat soils such as the Winterhill
series). Please find below some images showing details of data accessible to you for a 4 km 2 area
(red square): (i) OS map; (ii) land cover map; (iii) soil types; soil carbon estimates over (iv) 30
cm and (v) 100 cm soil depth.
8
9
However, little is known about the carbon source/sink functioning of these upland regions and
our work will try to investigate this mainly within a 1 km2 grid (green square) around the Eddy
10
flux tower site (FT) which also contains a lime vs. non-lime treatment area (Lime). All the above
datasets have been licensed to CTCD and are not to be used outside the summer school.
The study site is part of a larger upland area, the Yorkshire Dales, which has large areas of peaty
soils mainly due to its relatively high elevation and the oceanic climate. Consequently, there are
major soil carbon stocks. The following picture shows the best UK estimate of soil carbon up to
1 m depth within the wider study area superimposed on a digital elevation model; soil carbon
stocks are very large and increase with altitude to up to 100 kg C/ m2. This is the result of a
changes in both, inputs as net primary productivity (NPP) and outputs through decomposition,
which we will measure as soil respiration. However, there are many other factors involved such
as changes in litter quality and soil pH.
Figure showing the wider study area within the Yorkshire Dales National Park. Soil carbon estimates for 1 m depth
overlaid onto a DEM. The study site (red square) is approximately 10 miles from the field centre (blue circle) at
Malham Tarn.
However, it is important to note that vegetation carbon stocks are only a fraction of this, reaching
values of less than 15 kg C/ m2. Furthermore the soil carbon stocks do not consider peat depth of
greater than 1 m. This is of particular importance as we do not know how these deep soil carbon
stocks will respond to climate change. They certainly have a great potential for a positive
feedback – just think about small changes in big numbers. For clarification the two graphs below
show (i) the global carbon cycle as proposed by the IPCC and (ii) the world’s vegetation and soil
carbon stock estimates.
11
12
Eddy covariance
Eddy Covariance
(Instructor: Andrew Fox)
Introduction to the technique
Eddy covariance (or the analogous, older term, eddy correlation) flux measurements have
become an increasingly popular method for assessing ecosystem carbon exchange, and
complement traditional methods assessing inter-annual changes in biomass and soil carbon (e.g.
Baldocchi et al., 1988; Running et al., 1999; Geider et al., 2001; Lee et al., 2004). This
popularity is due to a number of factors. First, and most importantly, the measurements are made
at the most appropriate ‘canopy-scale’, assessing net CO2 exchange for a whole ecosystem.
Second, it is a direct measurement of net CO2 exchange across the canopy-atmosphere interface,
using micrometeorological theory to derive a flux from the covariance between vertical wind
velocity and scalar concentration fluctuations. And thirdly, the technique is capable of measuring
net ecosystem CO2 exchange across a wide range of temporal scales, ranging from hours to days
to years. Consequently, it is a particularly good technique for investigating ecosystem
physiology, able to assess whole ecosystem response to environmental perturbations and when
utilised in paired studies it can be used to assess the effects of disturbance, stand age or plant
functional types (Anthoni et al., 2002; Baldocchi et al., 1997; Chen et al., 2002).
Since 1997 regional networks of eddy covariance flux towers over a variety of principally forest
ecosystems have been in operation in Europe (CarboEuro-Flux, Aubinet et al., 2000) and North
America (Ameriflux, Running et al., 1999) and there are currently over 180 sites world wide as
part of the FLUXNET program (Baldocchi et al., 2001) involving new regional networks in
Canada, Brazil, Asia, Australia and Africa.
Principles
The eddy covariance technique works by sampling the turbulent motions of upward and
downward moving air parcels that transport trace gases such as CO2 across the canopyatmosphere interface. Statistical analysis (Reynolds’ rules of averaging (Reynolds, 1895)) is used
to determine mean flux density of CO2 (F, mol m-2 s-1) averaged over some time span (normal
30-60 minutes) as the covariance between fluctuation in vertical wind velocity (w) and the CO2
mixing ratio (c = c/a, where c is CO2 density and a air density):
F   a  w' c'
(1)
In Eqn 1 the over bars denote time averaging and primes represent fluctuations from the mean
(e.g. c’ = c - c̄ ). Positive covariance indicates a net CO2 transfer into the atmosphere. The
equation defining the conservation of mass means that over flat, homogeneous landscapes
simultaneous measurements of vertical wind speed made with a three-dimensional sonic
anemometer and trace gas concentration from a suitable sensor made in the surface boundary
layer several metres above the canopy-atmosphere interface can deduce the exchange of carbon
into and out of the underlying ecosystem.
The frequency and duration with which these measurements are required is determined by the
requirement to sample the cospectrum of turbulent motions that exist in the atmosphere.
Sampling rates of 10 times per second usually capture the high frequency component of the
cospectrum. To capture the low frequency contribution to the flux covariance the sampling
duration must be long enough to encompass the atmospheric motions associated with convective
boundary layer activity, but not so long as to be affected by diurnal variations in CO2
13
Eddy covariance
concentrations. This tends to limit result in measurement and averaging periods between 30 to 60
minutes. In practise a number of instrumental, sampling and turbulence issues prevent a
complete sampling of the cospectrum and this spectral filtering can be addressed by using
theoretical or empirically derived transfer functions to correct the eddy covariance flux
measurements, and in general such correction factors have been found to range between 1.04 and
1.25 for CO2 flux densities in the CarboEuro-Flux network (Aubinet et al., 2000).
The type of instrument used to measure the scalar flux can also effect the computation of the
covariance. For example, in the case of CO2 an infrared gas analyzer (IRGA) is used which does
not actually measure mixing ratio (c), but rather molar density (c). This can vary in response to
adding/removing molecules from the control volume, or changing the size of the control volume,
which will occur in response to pressure, temperature and humidity variations in the boundary
layer. When deriving the net CO2 flux density from these molar density measurements a
correction has to be applied which takes these variation in temperature and humidity into account
(Webb et al., 1980). This is particularly important for ‘open path’ sensors, such as the Li-Cor
7500 we will be using.
Practical operation
The University of Edinburgh roving eddy covariance flux measurement equipment used here
consists of a Campbell Scientific CSAT3 sonic anemometer and a Li-Cor LI-7500 open path
IRGA. The CSAT3 is a three dimensional sonic anemometer that uses advanced pattern
recognition techniques to determine the time that the sonic signal takes to travel between pairs of
transducers on three non-orthogonal axes. From these measurements, orthogonal wind speed and
sonic temperature can be computed. The LI-7500 Open Path CO2/H2O gas analyzer consists of
two components: the analyzer sensor head and the control box which houses the electronics. The
sensor head has a 12.5 cm open path, with single-pass optics and a 1 cm diameter optical
beam. The Infrared Source emits radiation, which is directed through a chopper filter wheel,
focusing lens, and then through the measurement path to a cooled lead selenide detector. The
absorption at wavelengths centred at 4.26 µm and 2.59 µm are used for measurement of CO2 and
water vapour, respectively. Reference filters centred at 3.95 µm and 2.40 µm provide for
attenuation corrections at non-absorbing wavelengths minimizing sensitivity to drift and dust,
which can accumulate during normal operation.
Campbell Scientific CSAT3 and Li-Cor LI-7500 mounted adjacently
14
Eddy covariance
An important decision to be made at the start of an investigation is the height at which to mount
the sensors. This needs to be well above the surface roughness sub-layer but within the well
mixed surface boundary layer. The actually height will then depend on the nature of the
underlying canopy, its zero-plane displacement height and roughness length. Above short, moor
land vegetation this corresponds with a height of about 3m.
Once the flux sensors have been set up and calibrated, they generally require very little attention
from the operator. Normal field operation consists of little more than:
1. Checking the general physical condition of the sensors, possible cleaning the windows at
both ends of the optical path
2. Checking any real time observations are giving sensible values for concentrations, fluxes,
temperature, wind direction and time
3. Ascertaining the battery status/power supply
4. Downloading the logged data
Analysis - how to convert the raw data into something usable
To get a flux density measurement from the raw 10 Hz data over a given observation period can
be daunting. Statistical analysis is required to determine the covariance, coordinate rotation is
often used to ensure mean vertical and some time cross wind velocities are zero, and there is the
application of correction factors discussed above (and others). This requires the use of custom
software, such as that developed at the University of Edinburgh (EdiRe,
http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/). EdiRe is a flexible, and (relatively!)
user friendly software tool. It is adaptable to most eddy covariance raw data formats and has a
graphical user interface simplifies development of processing routines and allows rapid redesign
of routines to enhance question/answer cycle of data analysis.
For reasons discussed above it is common practice to average flux densities over 30 minute
intervals, but even then this often produces a time series with numerous ‘spikes’ and curiosities,
which may have to be removed. These, and any other gaps in the record due to issues like
instrument breakdown, calibration, or measurements over range, will need to be filled to
generate a continuous time series of fluxes. In addition, measurements are also often discarded
when the wind is coming from an unfavourable sector, or the sensors are wet. A number of
approaches to gap filling have been tried, including empirical derived algorithms driven by
meteorological variables, interpolation between adjacent data points (only useful for small gaps!)
or binning data by hour over a one or two week period and using a time dependent mean to gap
fill.
Caveats - problems and weaknesses in the method
To address ecological relevant questions we need to sum eddy covariance fluxes over extended
periods to determine daily, seasonal or annual carbon balances. The accuracy of doing this will
depend on a set of random and systematic bias errors caused by flaws in measurements,
sampling and theoretical issues. With a well set up system measurement errors should be small,
and is generally less than 7% during the day, and less than 12% at night. Natural variability in
turbulence is in the order of 10-20%, which will cause variability in flux measurements under
similar conditions. Constructing long-term averages reducing this sampling error to +5%
(Moncrieff et al., 1996).
The eddy covariance flux community continues to address two specific systematic bias errors.
The first is the widely experienced failure to close the energy balance when eddy covariance
measurements of latent and sensible heat do not match independently measured available energy
(radiation and ground heat flux) (Wilson et al., 2002). The second apparent bias is towards
15
Eddy covariance
underestimation of nocturnal ecosystem efflux, particularly during periods with low wind
velocities (Moncrieff et al., 1996).
Historically, eddy covariance flux measurements have been made over homogeneous vegetation
types. To interpret CO2 fluxes from heterogeneous vegetation mosaics additional information is
required about the ‘flux footprint’, that is the area of the canopy-atmosphere surface “seen”
upwind of the sensor and for which the measurements are valid. This issue is beginning to be
addressed using footprint models (e.g. Schmid and Lloyd, 1999).
Anthoni, P.M. et al., 2002. Seasonal differences in carbon and water vapor exchange in young
and old-growth ponderosa pine ecosystems. Agricultural and Forest Meteorology, 111(3):
203-222.
Aubinet, M. et al., 2000. Estimates of the annual net carbon and water exchange of forests: The
EUROFLUX methodology, Advances in Ecological Research, Vol 30. Advances in
Ecological Research, pp. 113-175.
Baldocchi, D. et al., 2001. FLUXNET: A new tool to study the temporal and spatial variability of
ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the
American Meteorological Society, 82(11): 2415-2434.
Baldocchi, D.D., Hicks, B.B. and Meyers, T.P., 1988. Measuring Biosphere-Atmosphere
Exchanges of Biologically Related Gases with Micrometeorological Methods. Ecology,
69(5): 1331-1340.
Baldocchi, D.D., Vogel, C.A. and Hall, B., 1997. Seasonal variation of carbon dioxide exchange
rates above and below a boreal jack pine forest. Agricultural and Forest Meteorology,
83(1-2): 147-170.
Chen, J.Q. et al., 2002. Biophysical controls of carbon flows in three successional Douglas-fir
stands based on eddy-covariance measurements. Tree Physiology, 22(2-3): 169-177.
Geider, R.J. et al., 2001. Primary productivity of planet earth: biological determinants and
physical constraints in terrestrial and aquatic habitats. Global Change Biology, 7(8): 849882.
Lee, X., Massman, W.J. and Law, B., 2004. Handbook of Micrometerology A guide for surface
flux measurement and analysis. Kulwer Academic Publishers, Dordrecht, 250 pp.
Moncrieff, J.B., Malhi, Y. and Leuning, R., 1996. The propagation of errors in long-term
measurements of land- atmosphere fluxes of carbon and water. Global Change Biology,
2(3): 231-240.
Running, S.W. et al., 1999. A Global Terrestrial Monitoring Network Integrating Tower Fluxes,
Flask Sampling, Ecosystem Modeling and EOS Satellite Data. Remote Sensing of
Environment, 70(1): 108-127.
Schmid, H.P. and Lloyd, C.R., 1999. Spatial representativeness and the location bias of flux
footprints over inhomogeneous areas. Agricultural and Forest Meteorology, 93(3): 195209.
Webb, E.K., Pearman, G.I. and Leuning, R., 1980. Correction of Flux Measurements for Density
Effects Due to Heat and Water-Vapor Transfer. Quarterly Journal of the Royal
Meteorological Society, 106(447): 85-100.
Wilson, K. et al., 2002. Energy balance closure at FLUXNET sites. Agricultural and Forest
Meteorology, 113(1-4): 223-243.
16
Soil stocks
Measuring Soil Carbon Stocks
(Instructor: Phil Ineson)
Introduction
The objective of this exercise is to quantify the organic C stock held in the soils within the ‘1 to
4’ square km grid cells. This will be achieved through a programme of field sampling, followed
by subsequent analyses of samples in the laboratory to determine total organic C content. Soils
are extremely heterogeneous and this creates considerable problems when measuring C stocks or
attempting to detect change. This heterogeneity is the result of many contributory factors,
including local geology, hydrology and management and has to be allowed for as much as
possible when planning and executing the field samplings. According to existing soil survey
maps there are several (~5) soil associations represented within the ‘1 to 4’ km and these maps
will have been produced based largely on distribution of vegetation distribution coupled with a
very limited number of soil test pits. Surface vegetation frequently provides a very useful, rapid
and non-invasive method of locating the extent of a particular soil type and assists in defining
boundaries between soil associations.
Unfortunately, soil organic C content frequently changes down a soil profile, yet tends to
be fairly consistent throughout a particular soil horizon within a particular soil type. It is
therefore necessary to dig appropriate soil pits (or take cores) and to quantify the depths and C
contents of each horizon going down to a depth of 1 m. So, it is important that horizons are
identified, measured and sampled to a depth of 1 m. Additionally, since you will report the final
C stocks on an area basis (kg C per m2) it is important to have an appropriate means of
converting between soil volumes and weights; this is achieved by measuring bulk density (BD;
soil mass per unit volume) by soil association and horizon. Furthermore to relate the soil carbon
to the national database we might have to consider stoniness as to obtain final C density values.
Fig 1 and Table 1-1 (below) provide a written and visual representation describing the general
nature of soil horizons and it may help you in planning how to sample.
The actual analysis of soil organic C content is ideally performed by combustion of the
sample in the presence of oxygen and measuring the amount of CO2 produced, using either gas
chromatography (GC) or some form of infra-red gas analysis (IRGA). A further alternative
method is to use wet combustion (e.g. the Walkley-Black method) yet the most frequently used
indirect measure for soil organic C determination is weight loss resulting from combustion at
17
Soil stocks
high temperature; this method is usually referred to as loss-on-ignition (LOI). LOI analyses are
frequently performed on dry soil samples combusted for several hours in a muffle furnace. The
procedure we describe here follows the method described by Schulte and Hopkins (1996) in
which samples of soil are sieved, oven-dried and then weighed sub-samples are placed in
crucibles and subjected to heat at 360oC. Various factors are used for the conversion of organic
matter (LOI) to organic carbon. The figure for the ratio organic matter/organic carbon which has
often been accepted is 1.72 and is based on the assumption that soil organic matter contains 58%
carbon. However, there are different factors suggested for different soils (Howard, 1966): Mor:
1.77-1.93; alluvial: 1.81-1.83; peat: 1.90-1.95 and mull: 1.97-2.07. However, weight loss at each
stage is calculated and, for organic soils such as these, we can use the following equation to
convert LOI to soil organic C content (Konen et al. 2002):
Soil organic C content (g C kg-1) = 0.6094 * LOI (g kg-1) + 0.1949
Materials & Methods
Spades
Peat corer
Trowels
Plastic bags
Soil maps
Soil profile descriptions
Tape measures
Rulers
Plastic cores
Field mallet
Knives
Wooden boards
Sample bags
Marker pens
GPS system
Muffle furnace (360oC)
Drying oven (105oC)
Crucibles
Balance
Sampling. It is necessary to decide the optimal sampling strategy prior to going into the field.
Using the maps and images provided you should decide a stratified sampling system for the soil
associations in the km2 under study. We are not providing you with a prescriptive methodology
but are leaving it to each group to devise an optimal sampling strategy. The main constraint is
that you have only 90 crucibles available to you for LOI determination and you are therefore
limited to a total of 90 soil samples for organic C analysis. You are advised to consider carefully
the descriptions of the soil associations to decide on a profile sampling strategy and the number
of cores/pits to sample. Note that the sampling of peat soils can best be achieved using the peat
corer provided; mineral soils require a more extensive soil profile to be dug and for soil sampling
plastic pipes to be inserted. Adequate plastic sample bags are provided. Care must be taken not
to compress soils when BDs are being determined. Always label any material appropriately
with an identifier and date.
Analysis. Samples of known weight and volume are placed in the drying oven to determine
moisture content and to prepare samples for the muffle furnace. Samples are oven-dried at 105°C
overnight, cooled in a desiccator, and weighed. Equivalent volumes (approximately 8 g) of < 2mm air-dry soil are then placed into 15 cm3 crucibles and samples combusted at 360°C for 2 h in
a muffle furnace. Samples are then transferred after the 2-h combustion period to he drying oven
at 105°C for several hours. Samples are then cooled in a desiccator and re-weighed. Loss-onignition is calculated using the following equation:
LOI (g C kg-1) = (oven dry soil wt – soil wt after combustion) * 1000
oven dry soil wt
18
Soil stocks
Reporting. Using a combination of available maps, group surveying, sampling and subsequent
LOI analysis the group should derive a single figure for the soil C stock in the sample km 2.
Remember to subtract any stone volume if necessary. This value will then be compared with data
from the UK national data base for the same square. We also want the group to report on where
they consider the greatest sources of error and to provide some consideration of how to derive an
uncertainty estimate for the final figure.
References:
Schulte, E.E. and B.G. Hopkins (1996). Estimation of organic matter by weight loss-onignition. p. 21–31. In F.R. Magdoff et al. (ed.) Soil organic matter: Analysis and interpretation.
SSSA Spec. Publ. 46. SSSA, Madison, WI.
Michael E. Konen, Peter M. Jacobs, C. Lee Burras, Brandi J. Talaga and Joseph A. Mason
(2002). Equations for Predicting Soil Organic Carbon Using Loss-on-Ignition for North Central
U.S. Soils. Soil Science Society of America Journal 66:1878-1881 (2002)
19
Vegetation Carbon Stocks
Measuring Vegetation Carbon Stocks and Leaf Area
(Instructor: John Grace)
Introduction
Terrestrial Ecosystems of the world represent a carbon store of about 2100 Gt of carbon, and a
single hectare of land may have from ten to several hundred tons of carbon (Table 1). It is
important to know the size of the carbon stocks, and especially to characterise the changes in
stocks when land is managed or converted from one use to another. The carbon stores in NW
European dwarf shrublands are not well known, and during this course we hope to produce some
provisional figures.
For land-based measurement (as opposed to measurements from remote sensing) we generally
use the simple method of destructive sampling. Often, we measure Leaf Area Index at the same
time, as this describes the fundamental capacity of ecosystems to capture energy from sunlight.
Table 1. The estimated carbon stocks of ecosystems of the world, and the corresponding values of Net
Primary Productivity, NPP (based on Saugier et al. 2001). 1 Pg = 1015 g = 1 gigatonne= 109 tonnes (1Gt).
Biome
Area
(million km2)
Average C
stocks (tC ha-1)
Total C
pool (Pg C)
Average NPP
(tC ha-1 year-1)
Total NPP
(Pg C year-1)
Trop. forests
Temp. forests
Boreal forests
Arctic tundra
Med. shrublands
Crops
Trop. savanna
& grasslands
Temp. grasslands
Deserts
Ice
TOTAL
17.5
10.4
13.7
5.6
2.8
13.5
27.6
316
280
288
208
314
11
118
553
292
395
117
88
15
326
12.5
7.7
1.9
0.9
5.0
3.1
5.4
21.9
8.1
2.6
0.5
1.4
4.1
14.9
15
27.7
15.3
149.3
121
61
182
169
3.7
1.2
5.6
3.5
2137
62.6
Principles
Carbon stocks
To measure the carbon stocks in herbaceous or shrubby ecosystems, we generally harvest the
biomass, necromass and soil from a suitable number of sample plots, dry it in an oven set to 80
o
C, determine the mass, and convert from mass to carbon content using a particular conversion
factor. It is best to determine the carbon percentage using a mass spectrometer but most biomass
is close to 50% carbon, and so it is usually good enough to assume a conversion factor of 0.50.
When dealing with woodlands or forests, this direct method is not practical and then we find
empirical relationships between mass and diameter (‘allometric relationships’) and estimate the
mass of trees from their diameter. There is a large and rapidly growing literature on this subject,
in which allometric relationships are given (Zianis & Mencuccini 2004).
20
Vegetation Carbon Stocks
Leaf Area Index
Leaf Area Index (LAI) is defined as leaf area per land area, and it is a dimensionless quantity. To
measure LAI directly we simply remove the leaves from square sample plots and measure their
combined area. This could be done with graph paper but is more conveniently and rapidly done
using a mechanical device known as an area meter. For really rapid work, or work in woodlands
and forests, we use an optical method. There are two optical methods available to us: (i) use a
special camera that can view an entire hemisphere to capture an image of the canopy viewed
from the ground and (ii) use a Li-Cor canopy analyser which takes a measurement and makes the
calculation in real time based on the light that penetrates the canopy at different angles. Both of
these methods depend on the same ‘gap-fraction’ theory: the amount of gap in the canopy for
light to penetrate declines in a predictable manner as the LAI increases. By measuring the gap in
zones of varying angular elevation, and after making a few assumptions, we get the LAI
(Norman & Taylor, 1989).The photographic method is not very useful for dwarf shrub
communities because the camera cannot ‘fit’ under the canopy. The canopy analyser can easily
be poked under the canopy, and readings taken in a number of locations. Note that the data
logger of the Li-Cor canopy analyser does the calculations of mean and standard error for us.
Fig. 1 The Li-Cor LAI-2000 canopy analyser. The right diagram shows the optical arrangement of
the sensor whereby an image of the canopy is projected onto a photo-detector made of concentric
rings. The left diagram shows sensor and hand-piece connected to the logger Li-Cor datasheet.
Practical Aspects
Sampling
Once the study site has been identified, decide on the sampling strategy required to capture the
pattern of variability (Fig. 2). For a more or less homogenous moorland, you might want to
define a number of random stations for intensive sampling. However, if there is a visual pattern
of well-drained slopes and, for instance, valley bog, you may wish to find their areas in m 2 and
designate these as the two strata from which you sample at random. This would be a simple form
of stratified random sampling. For unbiased quantitative results it is better to use stratified
random sampling.
21
Vegetation Carbon Stocks
Fig. 2. Two ways to lay out the sample stations in a landscape with five sorts of vegetation. Left, purely
random (determined using random co-ordinates); right, systematic sampling. A third type (not shown) is
random sampling within the designated sorts of vegetation (stratified random sampling).
Irrespective of the sampling strategy, you need to address the issue of sampling intensity. For
above-ground vegetation, it is usual to lay out n sample squares of area a (m2) in the study area
and then proceed to harvest them. The question arises, how large does n and a need to be? Of
course, ‘the more samples the better’ but what is ‘reasonable’? For herbaceous and shrubby
vegetation the squares need to be large enough to encompass the basic pattern, otherwise the
population variance will be too large. For dwarf shrubs a is typically 0.5 m2. To determine how
large n should be for a stated precision, we should first do a pilot study to estimate the population
variance (s2). Do this by sampling 5-10 plots.
s2  
( x  x) 2
n 1
Then we can find the find the estimated standard deviation s by taking the square root of the
variance.
And we can find the standard error of the mean (S) for any value of n as:
S
s
n
And we can find the 95% confidence interval Ic for any value of n as:
I c  tn S
where tn is found from t-tables (Annex 1). For example, using a two-tailed t table with a
probability level of 0.05, t decreases from 2.66 at n=6 to 1.98 at n=120.
These calculations are conveniently carried out on a spreadsheet (please try them with a dummy
data set). In fig. 3 we show the result for a data set of biomass measurements from a pilot study:
six determinations gave 57, 120, 98, 87, 110, 111 g m-2 .
22
Vegetation Carbon Stocks
95% confidence interval
30
25
20
15
10
5
0
0
50
100
150
n, sample size
Fig. 3. Dependency of the 95% confidence interval on the number of samples taken, for the data set given
in the text (mean = 97.2, s estimated as 22.7). This leads us to the conclusion that we would need 100
samples to get within 5 g m-2 of the true value.
Sorting
Please try to identify the species. You will be required to sort the harvested material into
fractions for the main species, corresponding to leaf, woody biomass, and litter. There are
problems of classification that we need to agree upon at the start. Here are two issues that we
need to discuss. (i) In many communities the litter does not decay rapidly, and the soil profile has
a recognised organic layer on the surface. Is this regarded as litter or part of the soil? (ii) the bog
moss Sphagnum has a photosynthesising canopy of unknown LAI. Pull out one plant and see that
the distinction between green biomass and litter is not a sharp one. How shall we treat Sphagnum
and other bryophytes?
Drying
Biomass and necromass contain a variable fraction of water, and so we begin by driving off the
water in an oven set at 80 oC. Usually it takes only 8 hours to dry leaves and fine shoots, but
much longer for bulky wood samples such as limbs of trees. One ought to ‘dry to constant mass’
which requires repeated removal from the oven, cooling and weighing until you are sure that all
water has been driven off. We might have to pass on some of the weighing work to the group of
the next day as to make sure that the samples are ‘dry enough’. Always label any material
appropriately with an identifier and date.
Caveats - problems and weaknesses in the method
Please bear in mind that any measurement of LAI using optical equipment will always count the
dead leaf biomass as well. Consequently, for canopies with large amounts of leaf litter within the
canopy this will lead to overestimating LAI. Just think about a Pine forest with dead leaves
hanging in between the branches or have a look at the heather plants – do they have dead leaves
still attached to their branches?!
23
Vegetation Carbon Stocks
References
IPCC (2004) Land Use and Land Use Change, Good Practice Guidelines. http://www.ipccnggip.iges.or.jp/public/gpglulucf/gpglulucf_files
Norman JM & Campbell GS (1989) Canopy Structure. Pp 301-325 in Pearcy RW, Ehleringer J,
Mooney HA & Rundel PW. Plant Physiological Ecology, Field Methods and Instrumentation.
Chapman & Hall, Lndon.
Saugier, B., Roy, J. & Mooney, H.A. (2001) Estimations of global terrestrial productivity:
converging toward a single number? Pp 543-557. In Roy J, Saugier B & Mooney HA Terrestrial
Global Productivity. Academic Press, San Diego.
Zianis D, Mencuccini M (2004) On simplifying allometric analyses of forest biomass. Forest
Ecology & Management 187, 311-332.
24
Net Ecosystem Productivity
NEP chamber
(Instructor: Mathew Williams)
Introduction to the technique
Our goal is to measure the net exchange of CO2 from the soil and vegetation, and record how C
exchange responds to changing environmental conditions. Our approach is to seal a unit of
vegetation and soil under a clear Perspex chamber and record the changes in CO2 concentration
within the chamber, via an infra-red gas analyser. By manipulating the conditions in the
chamber (for example, shading the chamber or darkening it completely) we can determine how
vegetation responds to changes in light, and also determine the respiration of the vegetation and
soil. The ability to easily manipulate the environment and to separate photosynthesis from
respiration are distinct advantages of this chamber approach, compared to eddy flux techniques.
Of course, the scale of measurement by chamber (1m2) is much smaller than the footprint of an
eddy flux system (1ha+), and manual chambers are not suitable for continuous measurements.
Your goal is to characterise the light response of photosynthesis, and explores relationships
between photosynthesis and respiration for the key vegetation cover types within the catchment.
Each complete measurement set takes ~1 hour, so you can collect ~4 data sets. You must decide
where to collect your data. By determining light response curves, you will be able to construct a
simple models of photosynthesis, and estimate daily GPP for the entire day using radiation data
from the meteorological station.
Principles
Perspex is transparent to photosynthetically active radiation, so plants and soil can be enclosed in
a clear chamber and their activity monitored by measuring the changes in the concentration of
gases within the chamber. In the light, photosynthesis will draw down CO2, while transpiration
will increase water vapour concentration. Both CO2 and water vapour are greenhouse gases, so
their concentration can be determined by the absorption of distinct components of the infra-red
spectrum. The technique of infra-red gas analysis (IRGA) is well developed, and we use a
system constructed by LI-COR to monitor the concentration changes in the chamber head space.
The Perspex chamber itself has internal dimensions of 1 m wide x 1 m deep x 0.25 m tall. The
chamber sits on an flat aluminium base with itself rests on 4 steel legs which are driven into the
ground. The legs’ lengths are selected based on the height of the vegetation. The air tight seal
between chamber and base is achieved with a foam tape gasket. The aluminuium base has an
opaque plastic skirt long enough to drape down to ground level. A heavy chain weights the skirt
to the ground to ensure an air-tight seal. The chamber has 4 small internal fans powered by a
separate 12v battery to circulate the air.
Practical operation - a step-by-step field protocol
1. Locate the measurement site, and then set up the base, with each of the corner pegs slotted
onto a hollow leg, driven into the ground. Make sure the base is level.
2. Seal the chamber to the ground with the skirt plastic and heavy chain. Take care when
sealing the chamber so that small hummocks or depressions are tightly sealed.
3. Once the chamber base is set up, lay the chamber upon it and check the seal visually. Then
remove the chamber to avoid moisture build-up. Connect the fans to their power source and
start them up.
4. Seal the chamber to the base and initiate the closed programme on the LICOR 6400. When
light conditions are high (PAR at 1000 or greater) and steady, take 2 measurements at high
light, followed by 2 each at 3 levels of light reduction. The light reduction is achieved by
using mosquito netting laid over the chamber. Finally, take 4 dark measurements, with the
25
Net Ecosystem Productivity
blackout blanket over the chamber. At each measurement, also record the temperature in the
chamber and the PAR. Each measurement lasts 60s and data is recorded at a 2s interval.
5. After and between each measurement, lift the chamber to restore ambient conditions – watch
the graphs of CO2 and H2O until they have stabilized at pre-measurement levels.
6. When light conditions are low or unsteady, alter the order of the measurement series, and do
your best to cover a range of PAR values as well as take measurements when the light is at
its steadiest.
7. Measure the volume within the base by taking depth measurements at 36 points using a grid
(PVC frame with strings tied across) laid on top of the base. In plots where the substrate is
unstable, an initial and final volume can be measured to account for any sinking due to the
weight of the chamber.
8. To estimate the vegetation cover in the chamber, use the Skye 2-channel sensor. This sensor
measures reflectance in the red and near-infra red. Make sure to record both the channel
readings as you will need these to calculate the NDVI, the normalised difference vegetation
index. Make 9 measurements in a 3 x 3 grid, so that you record the heterogeneity of the
vegetation.
9. Record the soil temperature at 9 locations in a 3 x 3 grid.
10. Estimate the soil and vegetation percentage cover, and note the dominant species.
Analysis - how to convert the raw data into something usable
Your goal is to generate the parameters for a simple model of photosynthetic light response, and
to correlate the model parameters with your measurements of canopy cover (NDVI) and soil
respiration. You can also relate your estimates of soil respiration to soil temperature.
Calculate fluxes from chamber concentrations recorded by the Li-Cor 6400, according to the
formula
Fc = w V dC/dt
A
(1)
where Fc is net CO2 flux (μmol m-2 s-1), w is molar air density (mol m-3), V is the chamber
volume (m3), dC/dt is the slope of chamber CO2 concentration against time (mol mol-1 s-1) and
A is the chamber surface area (m2). The chamber volume is simply its area (1 m2 multiplied by
0.25 (chamber height) plus the average in m of the 36 depth measurements).
w = P/TK
where P is pressure (Pa), Tk is air temperature (K), and  is the gas constant (8.3144 Pa m3 mol-1
K-1).
Quality check your data by plotting light response curves. Are they smooth? Are your dark
measurements reproducible (i.e. give similar estimates)? If not then you likely have problems
with leaks.
Fit photosynthesis model to light response curves. Convert all NEP data to GPP using dark
respiration data to provide and estimate of Re [all data are assumed scalars].
GPP = NEP + Re
(2)
The light response model is
26
Net Ecosystem Productivity
GPP 
Pmax I
kI
(3)
where I is photosynthetic photon flux density (mol m-2 s-1) from the PAR sensor, and Pmax and
K are parameters, the maximum photosynthetic rate (mol m-2 s-1) and the half-saturation value
(mol m-2 s-1) respectively.
Enter your light response curve data into an excel spreadsheet with 3 columns for PAR, GPP,
temperature. Label a fourth column “model GPP”.
To the right of the data & model columns, create two new columns, one for parameter names,
and to its right, a column for parameter values. In the name column, enter the parameter names Pmax and K. In the value columns, enter initial guesses for the two parameters, e.g. 10 and 200.
You can “name” these value cells for ease – highlight the four cells with the names and values,
then click on “Insert”, “name”, “create” and tick “make names in left column”. Click OK. Now
you can simply write “Pmax” or “K” in any formula, and the numbers in the values column will
be substituted.
Make sure that solver is activated in your spreadsheet. Go to “Tools”...”add-ins” and then click
on the solver add-in. In the “model GPP” column, for each row with data, write the model
equation (3), making sure that “Pmax” and “K” refer to the named parameter cells, while I refers
to the specific irradiance for the row. Once complete, each row will have a modelled GPP based
on the initial values you set for “Pmax” and “K”, and the measured irradiance. To provide the
best estimate of the model parameters, we minimise the sum-of-square differences between
“model GPP” and “GPP”. Create a new column for the squared difference calculations, with
each row corresponding to the squared difference between model and observation. In another
cell elsewhere, sum these squared differences.
Use the solver to determine the best parameter estimates. Go to “Tools”, “Solver”. Set the
target cell to the cell with the sum of square differences (SSD).
Click the “min” button, as the goal is to minimise the SSD.
Click in the “by changing cells” box, and then highlight the two initial values set for “Pmax” and
“K”. Then click solve.
Excel will use optimisation methods to find the best parameter estimates. Check these are good
by plotting the modelled light curve over the data you collected.
Collate the daily time series of PPFD. Construct a model to estimate instantaneous GPP for each
half hour of the day. Integrate these date to generate a daily GPP estimate for each of your
chamber locations. Decide how to aggregate your data to produce a catchment estimate.
Plot your Pmax values against your NDVI
NDVI = (RIR- RVIS)/( RNIR + RVIS),
(3)
Plot Re against soil temperature and against Pmax - are there any signs of relationships?
27
Net Ecosystem Productivity
Caveats - problems and weaknesses in the method
Closed systems are inherently problematical because the conditions within the chamber change
immediately the system is disconnected from the environment. Thus, within the illuminated
chamber there is an immediate draw down in CO2 concentration and a rise in water vapour
concentration. These changes in conditions will alter the functioning of the vegetation over time.
Thus, the longer the chamber is left in position, the less the measurements reflect the activity of
the ecosystem to the ambient conditions outside the chamber. For this reason, measurements
must be made rapidly so the chamber is only in place for a minute or two, and internal conditions
change only by a small amount during this time.
Reading list
Johnson L.C., Shaver G.R., Cades D., Laundre J.A., Nadelhoffer K.J. & Rastetter E.B. (1996)
Effects of drainage and temperature on carbon balance of tussock tundra microcosms.
Oecologia, 108, 737-748.
Shaver G.R., Johnson L.C., Cades D.H., Murray G., Laundre J.A., Rastetter E.B., Nadelhoffer
K.J. & Giblin A.E. (1998) Biomass and CO2 flux in wet sedge tundras: responses to
nutrients, temperature and light. Ecological Monographs, 68, 75-97.
Vourlitis GL, Oechel WC, Hope A, Stow D, Boynton B, Verfaillie J, Zulueta R, Hastings SJ.
2000. Physiological models for scaling plot measurements of CO2 flux across an arctic
tundra landscape. Ecological Applications 10: 60-72.
Williams M., Street L., Wijk M.T.V. & Shaver G.R. (in press) Identifying differences in carbon
exchange among arctic ecosystem types. Ecosystems.
Equipment list
Perspex chamber (1 m x 1m x 0.25 m)
Aluminium Base (1 m x 1 m)
Base legs (various lengths in groups of 4)
Hammer
Thick opaque plastic skirt
Duct tape
12V battery to power fans
Small saw or secateurs to cut stems
Heavy chain (4 m +)
LICOR LI-6400 Photosynthesis system
PAR sensor
Shade netting
Blackout blanket
Skye 2-channel light sensor for NDVI
Soil thermometer
PVC frame with grid for depth measurement
Spirit level
Straight ruler and measuring tape.
Acronyms
GPP = gross primary production
NEP = net ecosystem production
Re = ecosystem respiration
NDVI = normalised difference vegetation index
28
Soil Respiration
Soil Respiration
(Instructor: Andreas Heinemeyer)
Introduction to the technique
Our aims are (i) to measure the soil CO2 efflux (Rs), (ii) to determine how much this flux
contributes to the total carbon budget, (iii) to understand measurement uncertainties and (iv) to
assess how Rs is controlled by changing environmental conditions such as temperature and light.
The system we are going to use is a LI-COR 8100 closed-dynamic system (LI-COR 8100) which
uses chambers placed on soil inserted PVC collars. The approach is based on the increase in
headspace CO2 from a given soil surface over time within the chamber volume, measured with
an infra-red gas analyser; where air is circulated between the chamber and the IRGA. We will
use two different systems: (i) a single portable survey system either for a 4 inch or 8 inch collar
size and (ii) a purpose build automated system which has been multiplexed by CTCD using 12
long-term LI-COR (8 inch) chambers. The complete system thus enables us to perform (A)
frequent measurements on 12 plots around the flux tower using the long-term automated system
and (B) many spot measurements for spatial coverage within the flux tower footprint and at an
experimental site.
A major ‘unknown’ in eddy flux tower measurements is Rs. Consequently, the first aim of our
team will contribute to a better estimate of the contribution of soil respiration to the measured
NEE by obtaining frequent and direct measurements of soil respiration within the flux tower
footprint area (1ha+). Of course, the scale of measurement by the 4 and 8 inch chambers (0.0083
m2 and 0.0346 m2, respectively) is much smaller than the footprint of an eddy flux system
(1ha+), and thus the second aim will be to use a combination of 12 long-term plots with many
spot measurements around the flux tower to enable us to up-scale soil respiration from spot to
ecosystem scale and estimate its uncertainty. Obviously, certain key environmental variables,
such as soil temperature, soil moisture and water table will need to be monitored during the soil
respiration measurement period in order to assess the impact of those factors on soil CO2 efflux.
The third aim is therefore to obtain a correlation matrix for soil respiration data vs. soil
temperature (e.g. Q10) and soil moisture. The fourth aim will be to assess land use impacts such
as liming and burning on soil C cycling in organic rich soils. It is also intended to address
certain technical issues in the correct chamber design such as the role of pressure vents.
Your goal is to characterise the response of soil respiration to key environmental factors, and to
explore relationships between soil respiration and land use management in the catchment. Each
complete measurement set takes 2 - 5 minutes, so you can collect ~12 data points per hour using
the automated system and 20 data points per hour using either survey chamber. As to avoid
disturbance effects, 40 collars (20 cm depth) for the small survey chambers and 12 collars (20
cm depth) for the long-term chambers were placed into the ground around the flux tower (100 x
100 m; Overdale) already in July. You will also find some more collars in burnt heather areas
near the tower. Further, within a 100 x 100 m area away from the flux tower site (Blishmire)
there are 4 plots (5 x 5 m) each to which 1 lime application (at 500g/ m2) were given on 18th July
which can be compared to 4 control plots (no added lime); within this plot area there are 24
small survey collars (3 within each plot; 20 cm depth). Placing thermistor (soil temperature),
theta probes (soil moisture) and water table PVC tubes (each 1.5 cm diameter; 50-100 cm depth)
within the measurement areas will enable us to determine response curves, which we will use to
construct simple models of soil respiration, and estimate daily soil respiration losses for the
wider flux footprint area using other data available to you such as land use and soil type areas.
29
Soil Respiration
Principles
Carbon dioxide movement from soils is the result of a concentration gradient between the soil
and the open atmosphere but mass flow can also occur under windy conditions caused by
pressure fluctuations induced by atmospheric turbulence. Soils release CO 2 mainly as a result of
biological activity mainly from three pools i.e. microbial soil, microbial litter and root (n.b. in
most cases mycorrhizal) respiration. The combined flux can be measured by one of several
methods: (1) closed dynamic system - increase in headspace CO2 within a known enclosed
volume of air where air is circulated between chamber and IRGA, (2) open dynamic system –
increase of chamber air compared to a reference incoming ambient air line, (3) chemical system
– fixing released CO2 to NaOH (4) soil CO2 profile – flux rate based on CO2 gradient over
certain soil depth or less common (5) micrometeorological – either measuring the CO2 air profile
or the vertical fluxes (eddies) of
CO2, both are based on mixing of
soil and ambient air. We are going
to use method 1 (e.g. LI-COR
8100) in which soil flux is
determined by measuring the
change in CO2 concentration with
time in an enclosure where the soil
surface is isolated from the
atmosphere (see Fig. 1, LI-COR
Fig. 1
manual).
By assuming that the rate of
change in gas concentration is constant, the appropriate equation for calculating the flux is:
Q = ∆c V
∆t S
(1)
where
∆c is the gas concentration (μmol m-3) difference within the chamber in the time interval ∆t
(s),
V is the volume of air within the entire enclosure (i.e. chamber + tubing + IRGA),
S is the soil surface area covered by the chamber (m2).
The chambers volumes and soil area are given in the software. However, as we use collars in the
ground we have to correct for additional air volume between chamber bottom and soil surface
(see below). The technique of infra-red gas analysis (IRGA) is well developed, and we use a
system constructed by LI-COR to monitor the concentration changes in the chamber head space.
Measurements of dry CO2 (corrected for water vapour) are made every second and data can be
stored on an internal memory or a flash card and are transferred to a computer at the end of each
field day. N. b. Dry CO2, 10 s flux rates and many other data can be monitored in real time using
wireless technology with either a Palm handheld or a laptop. You will be shown how to use this
equipment in the field. Obviously, you have to be careful not to breathe into the chamber when
placing it on the collar, you should also avoid putting too much pressure on the chamber when
placing it onto the collar as this will cause air to be pressed out of the ground into the chamber.
Remember: The weight of the chamber itself is enough to form a good seal but you do have to
check for branches etc. lying on the soil collar.
30
Soil Respiration
Practical operation - a step-by-step field protocol
11. Locate the measurement site (see Annex 3). Open the yellow LI-COR 8100 box and check
that either the power inlet (automated system) or battery (survey system) are properly
connected, then turn on the IRGA (press ON/OFF once). Close the box (the IRGA-optical
bench needs to warm up to >51°C before any measurement can be taken).
12. Wait until both the READY (communication with chamber OK) and the IRGA READY
(ready for measurements) light comes on and check that the bench temperature is >51°C.
This will take about 5-10 minutes (keep on checking by opening the box or better by
interrogating with the Palm (will be explained to you). The ACTIVE lamp will only come on
once you start a measurement.
13. Whilst you are waiting check that:
 the chamber is attached properly and clean on the inside,
 survey chamber has the auxiliary sensor interface (soil temperature probe) attached,
 the Delta-T data logger is still logging (soil temperature and moisture) and possibly
relocate the probes (press READ and use the up and down arrow).
14. Once the bench temperature has reached >51°C (IRGA READY) you can start measuring:
 Start the LI-8100 software (or the Palm OS LI-COR interface).
 Place the chamber onto the collar (survey chamber).
 Connect via Serial Port or TCP/IP; n.b. communication with the long-term system will be
via serial port only  (click) Connect
 Check the data and time  Utilities  Remote Date and Time
 Choose New Measurement and set Observation Length (~ 3 minutes), Observation Count
(whatever but for long-term system 12!), Observation Delay (~1-2 minutes) and Dead
Band (~ 35 s). Leave RH box unchecked. If using the long-term system then check the
Repeat box as 1 hour repeat x-amount of times (daytime as many hours you will be
there to do and overnight run about 20); leave Turn off flow pump box unchecked. 
Next
 Log data on the Onboard Internal flash memory (n.b. but external flash card for longterm system!), log every second and check the Log raw data records box and check the
Voltage 1 and Thermocouple 4 and all other right hand column data.  Next
 Enter a File Name, Treatment Label (e.g. flux tower, footprint, burn or lime) and
Comments such as sunny, few clouds, very windy, Start immediately, Chamber Offset
to whatever appropriate (ask but ideally the same for all e.g. 5 cm!).  Edit
 Choose Instrument Name e.g. survey 4 inch, IRGA averaging = 1 sec, change Instrument
Settings to 4 inch or 8 inch default accordingly, set flow rate for 4 inch ~ Med and for 8
inch chamber ~ High  click apply and OK.  Start Measurement.
 The chamber should start to close immediately!
15. Once the chamber closes check the running data with the Palm or laptop (n.b. serial port only
with the long-term system). (View  Monitoring). You can change the displayed data (such
as dryCO2, RH, 10 s flux, T4 (survey system only: soil temperature, V1 (long-term system
only: chamber position) etc.
16. Write down the time of the start of the measurement and note any disturbance (trampling,
wind etc.) or a wrong measurement (badly placed chamber, twig on collar etc.).
When using the survey or long-term chamber system:
31
Soil Respiration
 Download any data first using the laptop (n.b. long-term system: (i) communication only
via serial port and (ii) data transfer will be by using the external flash card which can
be removed after turning off the LI-COR box and then reinserting it into the laptop!).
(Connect  Utilities  File Manager  Transfer. N.b. you might have to click
Stop Measurement first in case the long-term system is still active. Just download to
the Desktop
 Look at the data with the File Viewer (double click on the file) and check that they are
OK!
Now
transfer
the
files
to
the
appropriate
folder:
E:\CTCDSS\Respiration\LICOR.
 Delete the transferred data from the internal LI-COR memory only once you have saved
them on the laptop  Close the File Manager!
 Long-term system: Reset the black control box! (Open  press Stop  press Start). The
numbers 1 and 12 should alternatively flash slowly. Close the black box.
 Reset the LI-COR as above by connecting via TCP/IP then  Start Measurement.
For chamber offset determination:
 For 4 inch collars: measure the distance between soil surface and collar rim.
 For 8 inch collars: measure the distance between the soil surface and the lower edge of
the chamber metal base (use two rulers for this!).
For data logger download:
 Connect logger cable to serial port of the laptop. Open MyLogger  wait until
connection has been established  Stop logging  Dataset  Retrieve  name
appropriately e.g. MTfluxtower16/08/05 and save on desktop  wait until the dataset
is displayed  check if all data are OK  close this data window.
 Delete all records  Delete Retrieved Records.
 Restart logger  Logger  Start.
32
Soil Respiration
Analysis - how to convert the raw data into something usable
Flux data
You are lucky as the LI-COR software does all the calculations for you. However, sometimes
you need to recalculate the fluxes due to a different chamber offset (e.g. 1.5 cm and not 2.0 cm)
or the need to alter the time frame used for the flux calculations (e.g. 80 – 160 sec. instead of the
intended 40-120 sec.) due to a delayed initial mixing. A look at the data in the file viewer will
help to decide if everything went OK:






Open the File Viewer, open the wanted file  double click on a data record 
Regression Analysis. You will see a plot of all dryCO2 (water corrected) data against
time. The green and red line show begin and end of the flux calculation, this should be a
near linear slope.
You can move the two lines in order to capture the most appropriate time and watch how
the flux rate changes. You can compare the linear rate calculation (Mean Flux) vs. the
exponential rate (IV flux). Quality control checking… .
Look at all the data (are they good or were there any problems?!) and then decide on a
common time frame for all data (or do only individual rows) and then  Edit 
Recompute… .
Check all rows ( or only selected) and set new Curve Fit timings  Recompute. N.B.
you should do this anyway as only this way it will give you the R 2 (only 0.0000 so far)
for the exponential and linear fit, so even if the timings are OK do this recomputation but
set the timings slightly different e.g. 40-121 sec. (make sure you either check All or
Selected).
If needed you can also adjust the Chamber Volume or Soil Area or the Chamber Offset
by checking the individual box BUT note that if All is checked it will do this for all
observations. It is more likely that you want to do the chamber offset individually for a
certain chamber. Then you first have to select those rows in the File Viewer data table
(hold Ctrl and click individual rows), then  Edit  Recompute… check the Selected
box and fill in new Chamber Offset value.
After post-processing the data we can now export the final dataset as a .txt file which we
will then import into Excel as a spreadsheet. Do  File  Export and check All and
Define which Columns to be exported: I recommend:
Orig. File; Obs; Date; Flux; ExpR2; Mean Flux; LinR2; T chamb; RH; V1 (for
long-term system only); T4 (for survey chamber only).
Give the file a name and Save as a .txt file.


Open Excel  Open (make sure you select All Files (*.*) and select delimited  Next
select Tab  Next  Finish!
You should now see all the data (in case not…maybe you have to enlarge some column
width!).
Data logger data
The environmental data collected with the logger have been saved as .bin files on the desktop.
Transfer these into the appropriate folder (see below). Then open Excel  File 
ImportDataset(s)  Import data from dataset file(s)  Next. This will ask for the data file
(on desktop) and then subsequently needs confirming of what time frame and what sensors – just
always click  Next and Finish until the Data appear. The graphs are on sheet 5 and the data set
is on sheet 4.
33
Soil Respiration
Temperature function: Q10
For the temperature sensitivity of soil respiration we need to combine the Delta-T soil
temperature data with the LI-COR soil CO2 flux data. Think about how to do this (e.g.
exponential curve fit of flux vs. soil temperature). N.b. For this we need to make sure that the
logger and the Li-COR have the same timings! We will use a spreadsheet for this purpose:
E:\CTCDSS\Respiration\LICOR\Temperature
Soil moisture impacts
In order to test for soil moisture impacts on soil respiration we also need to combine the Delta-T
soil moisture data with the LI-COR soil CO2 flux data. Think about how to do this (e.g. curve fit
of flux vs. soil moisture and possibly also with soil temperature). N.b. For this we need to make
sure that the logger and the Li-COR have the same timings! We will use a spreadsheet for this
purpose:
E:\CTCDSS\Respiration\LICOR\Moisture
Scaling-up
We certainly want to now how many sample points we need to reduce the measurement
uncertainty due to spatial heterogeneity to less than 10%. For this purpose we will use an already
prepared spreadsheet (from the vegetation carbon analysis), which will help us with the
calculations:
E:\CTCDSS\Respiration\LICOR\Spatial.
For spatial up scaling we need to have a mean value for each land use type. However, we will
not be able to acquire this much information in one week but will get close to a mean value by
assuming that our land use is the dominant one for the area (also possibly including % burnt vs.
un-burnt flux area). A major problem with up scaling over time is much more difficult as we
cannot assume that the temperature function (Q10) will be the same throughout the year! In fact
the temperature response changes with substrate supply from the roots to the soil and for
peatlands mainly changes in the water table will likely alter temperature responses.
To verify any spatial up-scaling methodology, the estimated (scaled-up) soil CO2 flux should be
compared with the flux measured by another technique that integrates over a larger scale. For
example, when chamber measurements are scaled-up to provide an estimate of the flux over a
large spatial scale, the flux should agree with eddy covariance measurements that measure
directly at the larger scale.
We will also assess the variability related to distance. Prediction of the spatial process at
unsampled locations by techniques such as ordinary kriging requires a theoretical semivariogram
or covariance. We will use a semivariogram spreadsheet in the same directory.
34
Soil Respiration
Caveats - problems and weaknesses in the method
Closed systems are inherently problematical because the conditions within the chamber start to
change immediately the system is disconnected from the environment. Closed automated
systems are also problematic as their cover will partially shade the surrounding area thus
possibly causing changes in vegetation around collar or changing root growth under the collar.
As seen in equation 1, we rely on the assumption that the CO2 diffusion rate is proportional to
the CO2 concentration gradient between the soil surface and the chamber atmosphere. It is
important to note that with such a system we have to operate at near ambient CO2 concentrations
and consequently closure times have to be as short as possible (e.g. < 5 minutes) as otherwise the
increase in chamber CO2 would alter the concentration gradient between soil and chamber air
and thus suppress diffusion, resulting in a lower flux. Accordingly, our LI-COR system gives us
two options to analyse the CO2 data to obtain a flux, either using a linear (assuming no
suppression) or an exponential fit (taking into account possible suppression). We also assume
that other factors driving diffusion such as barometric pressure, temperature and moisture of the
soil remain constant during the measurement. Whereas the latter two are unlikely to change over
such a short time scale there might be pressure changes due to either wind or internal pump
speed changes (i.e. in vs. outflow speed). This is why there has to be a major focus on the design
of the pressure vent for closed dynamic systems, allowing for equilibration of pressure changes
but preventing Venturi Effects. According to the Bernoulli equation, for a free air stream,
P + 0.5ρ v2 + ρ gh = constant
(2)
Static pressure (P) + dynamic pressure (p v2)+ gravitational pressure (p gh) = constant. At
constant height, when wind speed increases, dynamic pressure increases as the square of wind
speed and static pressure declines an equal amount to keep the sum constant. Thus, still air
inside a vent tube will have a higher static pressure than moving air outside a vent tube. Air will
flow out of the chamber down this pressure gradient, reducing pressure in the chamber and
drawing CO2 out of the soil. Thus, soil CO2 efflux is over-estimated when the open end of a
vent tube is exposed to wind.
Li-COR designed a new vent (Fig. 2; LI-COR patent pending) based on the Bernoulli equation
that reduces the speed of wind as it passes over
the vent opening, thus increasing the static
pressure of the air at the vent opening, and
eliminating the pressure difference between
chamber and air.
Fig. 1
LI-COR pressure vent preventing Venturi effects.
Another issue is that equation 1 ignores dilution effects of water vapour, which interferes with
CO2 absorbance. Rh >90%
One problem with all chamber flux measurements is the soil collar on which the chamber is
placed; a collar is needed to form a good seal between chamber and soil air. Apart from soil
disturbance effects which will increase the flux rate it is likely that roots are cut which will also
alter any flux rates. Thus the soil collars should be in place several weeks before any
35
Soil Respiration
measurements are made. Chamber offset needs to be measured correctly. Please note the
different procedure for survey vs. long-term chambers (see above)! Further, for the long-term
system we have to allow for a full flush of the air within the long tubing system (2 x 10 m @
chamber) as to prevent CO2 carry-over between lines. We have shown that allowing about 2
minutes observation delay between each measurement will prevent this. The below extract
(edited) is taken from an European Science Foundation (LESC Exploratory workshop
Investigating the role of soils in the terrestrial carbon balance – harmonising methods for
measuring soil CO2 efflux; Edinburgh, 6-8 April 2000) workshop (see Annex 1) provides a
valuable summary on most systems, like closed or open dynamic, closed static, chemical CO 2
trapping or micrometeorological systems; all have their strength and weakness.
Reading list
Nakayama F.S. (1990) Soil Respiration. Remote Sensing, 5, 311-321.
Grace J. & Raymont M. (2000) Respiration in the balance. Nature, 404, 819-820.
Reichstein M., Rey A., Freibauer A. et al. (2003) Modelling temporal and large-scale spatial
variability of soil respiration from soil water availability, temperature and vegetation
productivity indices. Global Biogeochemical Cycles, 17 (4) 15.1-15.15.
Kirschbaum M.U.F. 2004 Soil respiration under prolonged soil warming: are rate reductions
caused by acclimation or substrate loss? Global Change Biology, 10, 1870-1877.
Eliasson P.E., McMurtie R.E., Pepper D.A., Strömgren M., Linder S. & Ågren G.I. 2005
The response of heterotrophic CO2 flux to soil warming. Global Change Biology, 11, 167181.
Fang C., Smith P., Moncrief J.B. & Smith J.U. 2005 Similar responses of labile and resisatant
soil organic matter pools to changes in temperature. Nature, 433, 57-59.
Knorr W., Prentice I.C., House J.I. & Holland E.A. 2005 Long-term sensitivity of soil carbon
turnover to warming. Nature, 433, 298-301.
Hanson P.J., Edwards N.T., Garten C.T. & Andrews J.A. (2000) Separating root and soil
microbial contributions to soil respiration: A review of methods and observations.
Biogeochemistry, 48, 115-146.
Fitter A.H., Graves J.D., Self G.K., Brown D.S. & Taylor K. (1998) Root production,
turnover and respiration under two grassland types along an altitudinal gradient: influence of
temperature and solar radiation. Oecologia, 114, 20-30.
Information list:
LI-COR web-side: http://www.licor.com/env/Products/li8100/8100.jsp
Equipment list
LICOR LI-8100 with 12 long-term chambers
LICOR LI-8100 with either of the two survey chambers
Palm Tungsten handheld (wireless communication)
4 Delta T data loggers
with ~30 thermistor (soil temperature)
and 3 theta probes (soil moisture)
50 x (50 cm) PVC tubes for soil water table measurement
40 x 4 inch collars
55 x 8 inch collars
Lime 8.0 kg
Hammer (for inserting the PVC tubes)
GPS (for site locations)
Two rulers (for chamber offset determination)
36
Soil Respiration
Acronyms
NEE
Rs
Q10
Rh
= net ecosystem exchange
= soil respiration
= increase in Rs over a 10 degree temperature rise
= relative humidity
37
Methane Fluxes
Methane in the Global Carbon and Greenhouse Gas Budget
Instructor (Dave Reay)
Introduction
Methane concentrations in the atmosphere have more than doubled since the start of the
industrial revolution. Wetland and peat bog methane emissions dominate the natural sources of
methane. Global emissions from natural sources total around 250 million tonnes each year, with
wetland/peat bog areas comprising around 80% of this. Natural emissions of both methane and
carbon dioxide can be greatly affected by climate change and increased release of both gases
from peat bogs, with increasing global temperatures, is a big potential positive feedback to
global warming.
Energy related and ruminant methane dominate human-made methane sources. Global
human-made methane emissions are estimated to total about 320 million tonnes each year.
Tropospheric destruction of methane by hydroxyl (OH) radicals is the dominant sink for
atmospheric methane. With stratospheric destruction and oxidation by soil bacteria, the total sink
for methane is estimated to be between 500 and 600 million tonnes of methane each year.
Principles
Methane (CH4) contains carbon and so its flux is part of the overall carbon cycle; there are
natural and human-made sources of methane. In addition to this, methane is a powerful
greenhouse gas having a global warming potential (GWP) 21 times greater, molecule per
molecule, than that of CO2.
Our aim is to measure the methane flux from the peatland using a combination of a giant tunnel
chamber and a state-of-the-art laser system. The frames for the tunnel chambers are already in
place. Once covered with the plastic sheeting and secured around the edges these tunnels become
large flux chambers in which the change in methane concentration over time can be measured.
The laser system inside the tunnel measures the changing methane concentration by firing an
infrared beam down the tunnel and back. Any molecules of methane which get in the way of the
beam are therefore measured. We can therefore estimate how much methane the peatland is
belching out and its contribution in terms of carbon and, more importantly, its climate forcing
effect. To compare our methane emissions with CO2 in terms of climate forcing we will multiply
our methane fluxes by 21 to give ‘CO2-equivalents’.
38
Methane Fluxes
Practical Operation
For detailed use of the Boreal Laser system, refer to the manuals (see Dave) or ask for help.
In brief:
1. Position laser on tripod or stable mount at one end of tunnel
2. Position reflector at other end of tunnel (i.e. tape to frame)
3. Turn on the laser
4. Once warmed up it will probably say ‘Low light’
5. The supervisor will help remedy this.
6. Measure distance between laser and reflector and input data
7. Laser should now be measuring background methane concentration
8. Set laptop to log data
9. Draw plastic cover across chamber and fasten around sides
10. If a laptop is being used, any change in methane concentration can be seen using
GasView2.
11. The cover should be left in place for up to 30 mins at a time – keep in mind the need for
replication, good linearity and avoidance of superambient heating
12. Maximise the number of incubations, aiming for at least three per site
13. Make a note of the times and filenames used for each logging period
14. Having recorded changes in methane concentration within the chamber(s) the surface
area the chambers cover and the total volume of the chamber must be calculated (see
below).
15. Now, open the data in Excel (use comma delimiting) and plot the various datasets as
ppmm against time to view linearity.
16. To calculate the methane flux for the chambers use only data where a linear increase is
apparent, then… the maths
Analysis
Calculate change in methane concentration in ppm (ppmm divided by m)
 To get the flux rate divide change in methane by change in time.
 Multiply this by the volume (v) of the chamber (in m3) divided by the area (A) it covers
(in m2). (In case of acute memory loss: the area of a circle is calculated by pi x radius2)
 Multiply this by the Molecular weight (M) of the gas (16) divided by the universal gas
constant (V) (0.0224 m3)
 Finally, multiply by 273/temperature in Kelvin during measurement (i.e. 273 + temp. in
chamber in oC) to correct for ambient temperature
As an example:
 Say change in CH4 ppmm was 100 in 10 minutes
 So flux was 10ppmm per minute
 Path length was 10m
 So flux was 1ppm per minute
 Say chamber volume = 15m3, area = 18m2, then v/A = 0.83
 Multiply 1ppm per minute by 0.83 (i.e. 0.83 micromoles per mole)
 Then multiply this by 16/0.0224 (M/V). (NB if we wanted µmoles rather than µg CH4 it
would be 1/0.0224)
39
Methane Fluxes
 Finally, given temperature at site of a balmy 20oC, Multiply by 273/293




Gives methane flux of = 552.4 µg CH4 per m2 min-1
= 92 µg CH4 m-2 s-1
= 331.2 mg CH4 m-2 h-1
= 3.312 kg CH4 ha-1 h-1
If we wanted the carbon (C) emission we just need to multiply this by (12/16):
= 69 µg C m-2 s-1
The final step is to compare these CH4 fluxes on the basis of CO2-equivalents.
As CH4 has a global warming potential of 21 times that of CO2 we can calculate our CO2equivalent flux as 92 µg m-2 s-1 x 21
So for our example:
= 1.932 mg CO2-equivalents m-2 s-1
= 6.955g CO2-equivalents m-2 h-1
Greenhouse gas flux from the peatland is = 69.55 kg CO2-equivalents ha-1 h-1
(NB there are 10,000 m2 in a ha)
We should have several flux measurements from which to derive a mean flux rate and a standard
error. Additionally, if you have data from more than one site you must decide how to extrapolate
these data for the peatland as a whole to give the most representative CH4 flux for the area.
Caveats and potential problems







Spatial variation is our enemy here. The tunnel chambers are large so they negate some
of this problem but we must be aware that methane fluxes may be significantly different
in other parts of the peatland. Methane fluxes tend to increase as the soils get warmer and
wetter, so as a rule of thumb the wetter the soil the more methane we’ll get coming out.
The amount of soil carbon, in our case the depth of the peat, is also a key factor – the
more carbon then the greater potential for methane emission.
Soils not only produce methane but can also oxidise it. As such, when the soils are dry
and oxic (large acrotelm) the flux of methane may be into the soil rather than out of it.
This can be hard to detect, but keep an eye out for it.
As regards the above, there may be little or no change in methane concentrations at some
sites – methane oxidation is equalling production
Creating a good seal around the chambers can be tricky and care should be taken.
If the methane accumulation is clearly non-linear then it is likely you have a leak
somewhere.
The use of chambers can affect the methane flux through increasing the temperature and
reducing air movement. For short incubations this shouldn’t be a problem but it is always
worth keeping a record of external wind speeds and external and internal temperatures.
The rate of methane flux is likely to change over the course of the day and certainly
seasonally. By making several measurements at the same site we can overcome some of
this temporal variability, but when citing our ‘Site-wide methane flux’ we need to make
clear the assumptions this makes about both spatial and temporal variability.
40
Methane Fluxes
Readings and websites
IPCC 2001 Climate Change: The Scientific Basis
See: http://www.grida.no/climate/ipcc_tar/wg1/134.htm#4211
Conrad R. 1996. Soil microorganisms as controllers of atmospheric trace gases (H-2, CO, CH4,
OCS, N2O, and NO). Microbiological Reviews 60:609 (634 pages).
Dunfield. P., Knowles, R., Dumont, R., Moore, T.T., 1993. Methane production and
consumption in temperate and subarctic peat soils: Response to temperature and pH. Soil
Biology & Biochemistry 25, 321-326.
GreenHouse Gas Online http://www.ghgonline.org/
Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J., Heimann, M., 1999. Inverse modelling
of methane sources and sinks using the adjoint of a global transport model. Journal of
Geophysical Research 104, 26137-26160.
Panikov, N.S., Dedysh, S.N., Kolesnikov, O.M., Mardini, A.I., Sizova, M.V., 2001. Metabolic
and environmental control on methane emission from soil: mechanistic studies of mesotrophic
fen in West Siberia. Water, Air and Soil Pollution: Focus 1, 415-428.
Reay D.S., McNamara, N. Nedwell, D.B., 2001. Physical determinants of methane oxidation
capacity in a temperate soil. Water, Air and Soil Pollution: Focus 1, 404-414.
Equipment List
Boreal Gas Laser
Laser Tripod or stable base
Laser power pack
Reflectors
Measuring tapes and rule
Thermometers
Laptop with GasView2 and connection cable
Calculators
Sticky tape
41
Remote Sensing & Earth Observation Techniques
Spatial measurement of Leaf Area using Earth Observation
Instructor (Phil Lewis)
Introduction
Providing data on terrestrial ecosystems over large areas is only feasible using Earth Observation
(EO) data. However, EO does not generally (except perhaps in the case of LiDAR) make a direct
measurement of the properties we are interested in: it measures scattered or emitted radiation at
some set of wavelengths. The properties of the ecosystems must therefore be inferred from such
measurements. EO data can of course be used for ‘simple’ applications such as land cover
classification, where the differing spectral properties of land cover types or the temporal
trajectories of these ‘signatures’ are used to discriminate between the cover types of interest and
provide a spatial map of such data. Such data are extremely useful in ecosystem modelling, for
instance in allocating properties of specific Plant Functional Types (PFTs) over spatial grids to
model the state and carbon exchange of the land surface. Perhaps more powerfully still, we can
use EO data to infer physical and biochemical properties of ecosystems and provide spatial maps
of these to: (i) test model runs; or (ii) reduce uncertainty in model outputs by calibrating model
components (e.g. phenology) against EO data or by assimilating EO data into the models.
To provide this inference of biophysical properties from EO data, we use various forms of
models. The two main approaches are: (i) empirical models relating the biophysical quantities to
EO data; or (ii) physically-based models which treat the physics of the radiation scattering for a
given set of ecosystem parameters, either to provide an ‘EO operator’ to an assimilation scheme
or to provide an estimate of the biophysical quantities through model inversion. The former of
these two approaches is much more common in EO applications as it is simpler and faster to
apply. The main drawbacks of this method are: (i) the empirical relationship used in the model is
only valid over similar conditions to those under which it was originally developed, and so may
not apply to different cover types, at different spatial scales, or under different illumination and
viewing geometries; (ii) most of these empirical relationships use only a limited subset of the
information content of the data; (iii) most of these empirical relationships rely on correlations
between biophysical quantities (e.g. leaf area index and biomass) which may not be precise
enough for some applications. The use of physically-based models is therefore generally
preferable and more powerful, but these generally involve much more stringent requirements on
the data, such as the need for well-calibrated measurements and the ability to account for
atmospheric scattering and absorption. In addition, the inversion of the typically non-linear
models used can be time-consuming and a complex task for multidimensional models (a typical
physically-based model requires at least seven parameters). A useful modern text on physicallybased models and their applications is Liang (2003)1. In this exercise, we will concentrate on
simpler, practical methods for providing spatial datasets on a particular biophysical quantity
(Green Leaf Area Index – gLAI, which is defined as the one-sided leaf area per unit ground area
(dimensionless)). gLAI is an important quantity in ecosystem modelling and monitoring as it is
related to the ability of the vegetation to intercept shortwave radiation and convert this energy to
carbohydrates through photosynthesis.
1
Liang, S., Quantitative Remote Sensing of Land Surface, John Wiley and Sons, Inc., 534 pages,
Nov. 2003.
42
Remote Sensing & Earth Observation Techniques
Materials and Methods
You will be using the following data and equipment to develop an LAI map of the study area:
1.
2.
3.
4.
GPS to locate your sample sites;
Spectroradiometers to measure surface spectral reflectance at the sample sites;
LAI2000 instruments to measure LAI;
Optical satellite imagery to provide the spatial mapping of LAI
Ground measurement of LAI and radiometry
The aim of this is to provide observations of LAI (LAI2000) and surface spectral reflectance (in
at least two wavebands: red and near infrared; spectroradiometers) at known ground locations
(GPS). The measurements should be averaged over the spatial resolution of the satellite data
(15m or 30m) to provide data on a comparable scale to the satellite data. Since we will be using
these data to calibrate an empirical relationship, the range of LAI data measured should cover the
full range that we are expecting in the landscape. Since the empirical relationship can also
depend on cover type, the cover type at each location should be noted, and ideally, LAI
measured over the full range of values for each cover type.
The detailed operation of the equipment will be explained to you in the field class. Some details
on one of the radiometers (ASD Fieldspec Pro) we will use are given on
http://fsf.nerc.ac.uk/instruments/asd_fieldspec.shtml. Such equipment measures spectral
reflectance in a large number of narrow wavebands across the solar reflective spectrum (usefully,
around 400-2400 nm) and costs upwards of £50,000. You will also find details at
http://fsf.nerc.ac.uk on how similar equipment can be borrowed for NERC-funded projects. We
will also be using ‘cheaper’ more mobile Skye radiometers2 (which cost a few £1000) that
measure in only a few (broader) wavebands. Measurement of reflectance is achieved by
measuring the spectral radiance of a target area and rationing this by a measurement of incident
irradiance. This latter task is achieved either through a radiance measurement on a near-perfect
diffusing white panel (e.g. made of spectralon) (ASD) or through a cosine receptor that directly
measures the incoming irradiance from the sun and sky (Skye). To achieve measurements
comparable to the satellite data, radiometry should be conducted under clear, stable illumination
conditions, preferably with the solar zenith angle similar to that of the image acquisition.
LAI is generally most accurately measured by destructive sampling and measuring leaf area
through some scanning system (this can be done with a flatbed scanner, although specific
instruments have been designed along these lines for this task). This is however, rather a slow
method to achieve much spatial sampling, so we tend to use indirect estimates of LAI. These
methods rely on inferring LAI through some measure of gap probability in the canopy. For an
idealised homogeneous canopy, the probability of a gap, Pgap  z,  in the canopy at depth z at a
zenith angle of  is:
Pgap  z,   e

LAI ( z ) G ( )
cos( )
where: LAI(z) is the downward cumulative LAI; and G() is the so-called ‘G-function’, the areanormalised mean projection of leaves in the direction defined by . For a spherical leaf angle
distribution, G()is 0.5, although it will vary as a function of  in the more general case. If
2
http://www.skyeinstruments.com/
43
Remote Sensing & Earth Observation Techniques
measurements of gap probability are made at a range of zenith angles, the leaf angle distribution
can be inferred along with LAI. Note that the equation defined above is for a homogeneous
canopy. Further complications arise for a clumped canopy. What impact might this have on gap
probability and inferred LAI? Two main methods are used for measuring gap probability: (i) a
radiometric measurement using an instrument such as an LAI2000 from Li-COR3 which
measures the incoming radiance at the top and bottom of the canopy in a number of azimuthal
and zenith bins to determine gap probability, thence LAI; or (ii) film or high resolution digital
cameras with fisheye lenses which can effectively measure gap probability through image
classification in either upward-looking (taller canopies, in which one classifies ‘sky’ or
‘vegetation’ to calculate gaps) or downward-looking (for low canopies, in which gaps are found
by distinguishing soil and vegetation). The reliability of both methods somewhat depends on the
incident illumination conditions, and are best conducted under overcast skies. Note that these
non-destructive methods are less accurate for increasing LAI due to the exponential nature of the
relationship with gap probability. This effect also means that there is effectively a saturation in
the relationship for high LAI values.
Pre-processing of the satellite imagery
We will be using ‘high’ spatial resolution satellite data to provide the mapping of LAI. Such data
are available from instruments such as quickbird4 at around 2.5m resolution, but a relatively high
data cost, or from (cheaper) coarser resolution sensors such as ASTER (15m)5, SPOT HRVIR
(20m)6, or Landsat TM (30m)7. The data you should use for any particular application will
depend on the resolution you require, the date for which you require coverage, the area required,
and, not unimportantly, data availability. This latter point is particularly relevant for optical
sensors such as those noted above, as we generally require cloud-free scenes for mapping.
Typical repeat coverage times of these sensors are a few weeks (this depends on latitude and can
be less for pointable sensors such as SPOT HRVIR), but the frequency of cloud-free images is
likely to be much less than this. An alternative to using satellite images over relatively small
study areas is to obtain data from airborne sensors, such as those made available to NERC
projects through the NERC ARSF8.
A calibrated satellite image is able to measure the radiance received at a sensor, but this is not an
intrinsic property of the surface being observed as it depends on the illumination and
atmospheric conditions at the time of imaging. We generally therefore wish to process this to
obtain an image of at-surface spectral reflectance. This can be achieved in two main ways: (i) by
modelling the impact of atmospheric scattering and absorption on the measured signal and
inverting the model to estimate surface reflectance; or (ii) empirical calibration of reflectance by
deriving a model of atmospheric influence for each waveband based on ground measured
reflectance – the so-called empirical line method. Various codes are available to achieve the
former, the most typical of which is 6s9. This method has the requirement that you are able to
characterise the atmospheric state at the time of imaging. In the simplest sense, this means that
you need information of the atmospheric ozone, water vapour and aerosol type and
concentration. These can be measured with ground instruments10, and some of the properties are
3
http://www.glenspectra.co.uk/glen/licor/lai2000.htm
http://www.digitalglobe.com/about/quickbird.html
5
http://www.gds.aster.ersdac.or.jp/gds_www2002/index_e.html
6
http://www.spot.com/html/_401_402_.php
7
http://edc.usgs.gov/products/satellite/tm.html
8
http://www.nerc.ac.uk/arsf/Home.htm
9
http://www-loa.univ-lille1.fr/SOFTWARE/Msixs/
10
http://aeronet.gsfc.nasa.gov/
4
44
Remote Sensing & Earth Observation Techniques
available from other, e.g. satellite sources (MODIS aerosol properties11; MODIS water vapour12,
TOMS ozone13), though these are only available for the locations these sensors happened to be
viewing at any particular time and are quite coarse resolution. The empirical line method
requires nominally contemporaneous measurements of ground reflectance, ideally over bright
and dark targets. A linear relationship is then calibrated between the ground measured
reflectance and the satellite measurement and applied to the whole image. This method is usually
reasonable, provided such measurements are made at the same time as imaging, the atmospheric
conditions are near constant across the scene, and topographic variation is not too large
(otherwise the optical thickness varies across the scene). The assumed linear relationship is
reasonable, except for very bright targets or relatively high aerosol loadings (in which case
multiple scattering between the ground and atmosphere results in a non-linear relationship).
For this exercise, we will not have contemporaneous ground reflectance or atmospheric
measurements, so we will have to perform an empirical line correction based on the ground
reflectance measurements you make. Note that it is important that you measure reflectance over
as large a range of targets (bright, dark) as possible. Since the images are not exactly the same
time as your ground surveys, there will be some uncertainty in the mapping and you may have to
remove outliers in the relationship should the ground properties have changes significantly. Note
also that since the bandpass functions of the sensors will be different to those of the ground
instruments, this will introduce additional uncertainty.
Calibration of the relationship between radiometric measurements and LAI
Healthy green vegetation tends to strongly absorb incident radiation at red wavelengths (around
650nm) for use in photosynthesis. Near infrared radiation (around 750 nm) on the other hand is
mostly not absorbed. This is a distinctive feature of vegetation and allows it to be discriminated
from other cover types. Thus, satellite measurements of vegetation appear bright in the near
infrared and dark in the red. It is found that a combination of information in these bands is
therefore strongly related to vegetation amount. We typically term such a relationship a
vegetation index. If we plot measured reflectance in red and near infrared as a scatter plot
(below), we can note that increasing vegetation amount is generally given in the direction
indicated.
The most commonly-used such index is the NDVI, the Normalised Difference Vegetation Index,
which is a measure in this ‘direction’. It is defined as:
NDVI 
 nir   red
 nir   red
where  nir ( red ) is the reflectance in the near infrared (red) waveband. It is a normalised measure
lying between -1 and 1. Positive values are related to vegetation amount, although they can also
be influenced by various extraneous factors such as soil brightness variations, topographic
effects and variations in viewing and illumination conditions.
11
http://modis-atmos.gsfc.nasa.gov/MOD04_L2/
http://modis-atmos.gsfc.nasa.gov/MOD05_L2/
13
http://toms.gsfc.nasa.gov/ozone/ozone.html
12
45
Remote Sensing & Earth Observation Techniques
Line of increasing
vegetation amount
Near-infrared-red scatterplot
Once you have measured a set of reflectance data, examine a scatterplot in red-near infrared
feature space as above to confirm the behaviour of the observations. You can then simply
calculate NDVI in a spreadsheet and plot this as a function of LAI. Note the form of this
relationship and fit an appropriate function to it. Is the form of relationship what you expected?
Why might it be of this particular form? What implications does this have (if any) for uncertainty
as a function of LAI?
If you have sufficient samples for this relationship, you should use some of them to assess the
uncertainty in the derived LAI data. If the sample size is small, you might consider using a
jackknifing method (step through the dataset, removing one sample at a time and re-fitting the
relationship to the remaining points). Alternatively, you might consider using data collected by
other groups in assessing this (if available). As with all datasets during this summer school you
can join and link datasets acquired during the entire week!
Mapping of LAI
Once you have the relationship between NDVI and LAI defined from above (along with an
assessment of uncertainty), you can apply this relationship to NDVI calculated from the satellite
data. Ideally, you should have a further set of independent measurements of LAI to further test
your relationship.
Practical Aspects
Sampling
There are various aspects to consider in defining your sampling strategy. First of all, you have to
be able to relate your sample sites to the satellite imagery. This will be achieved by measuring
the location of the sites with GPS. Second, you need to be able to match the resolution of your
satellite data to the area you will sample on the ground (e.g. 15 m x 15 m for ASTER or 30 m x
30 m for Landsat TM). You then need to think about the sampling pattern you will use within
this area. The geolocation of the satellite imagery is likely to be around 1 pixel (i.e. 15 m or 30
46
Remote Sensing & Earth Observation Techniques
m). What impact might this have on your results? Third, you will need to consider sampling
density. Since you require a ‘reasonable’ number of samples to define your empirical
relationship and you have only limited time in this exercise, there will be a trade-off between the
sampling intensity at each site and the number of sites. Remember that the sample sites you
chose should cover the range of variability of the quantity we are trying to map. How might you
use the satellite data to help you decide on this?
Figure 1. Example Landsat 5 TM imagery over the study area, the image was acquired on the 14 May 1988. The top
image is a false colour composite, where vegetated areas appear in shades of red, while the bottom image is of the
NDVI computed from the same 1988 image.
47
Remote Sensing & Earth Observation Techniques
Finally, remember also that the ‘optimal’ conditions for doing radiometric measurements are
rather different from those required to use the LAI2000 and that this might impact you results
(ideally, you would have a longer period of time in which to perform your measurements).
Figure 2. Example Landsat 7 ETM+ imagery over the study area, the image was acquired on the 7 May 2000. The
top image is a false colour composite, where vegetated areas appear in shades of red, while the bottom image is of
the NDVI computed from the same 2000 image.
48
Remote Sensing & Earth Observation Techniques
References
Baret, F., and Guyot, G., 1991, Potentials and limits of vegetation indices for LAI and apar
assessment, Remote Sensing of Environment, 35, 161-173.
Chen, J.M., Liu, J., Leblanc, S.G., Lacaze, R., Roujean, J.L., 2003, Multi-angular optical remote
sensing for assessing vegetation structure and carbon absorption, Remote Sensing of
Environment, 84, 516-525.
Duncan, J., Stow, D., Franklin, J., and Hope, A., 1993, Assessing the relationship between
spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico, International
Journal of Remote Sensing, 14, 3395-3416.
Fang, H., and Liang, S., 2005, A hybrid inversion method for mapping leaf area index from
MODIS data: experiments and application to broadleaf and needleleaf canopies, Remote Sensing
of Environment, 94, 405-424.
Gower, S.T., Kucharik, C.J., and Norman, J.M., 1999, Direct and indirect estimation of leaf area
index, FAPAR, and net primary production of terrestrial ecosystems, Remote Sensing of
Environment, 70, 29.51
Huete, A.R., and Jackson, 1987, Suitability of spectral indices for evaluating vegetation
characteristics on arid rangelands, Remote Sensing of Environment, 23, 213-232.
Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., and Baret, F., 2004,
Review of methods for in situ leaf area indexdetermination. Part 1. Theories, sensors, and
hemispherical photography, Agricultural and Forest Meteorology, 121, 19-35.
Liang, S., Fang, H., and Chen, M., 2001, Atmospheric Correction of Landsat ETM+ Land
Surface Imagery-Part I: Methods, IEEE Transactions on Geoscience and Remote Sensing, 39,
11, 2490-2498.
Leprieur, C., Kerr, Y.H., and Pichon, J.M., 1996, Critical assessment of vegetation indices from
AVHRR in a semi-arid environment, International Journal of Remote Sensing, 17, 13, 25492563.
Myneni, R.B., Maggion, S., Iaquinta, J., Privette, J.L., Gobron, N., Pinty, B., Kimes, D.S.,
Verstraete, M.M., and Williams, D.L., 1995, Optical remote sensing of vegetation: modeling,
caveats, and algorithms, Remote Sensing of Environment, 51, 169-188.
Myneni, R.B., Hoffmann, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song,
X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., and
Running, S.W., 2002, Global products of vegetation leaf area and fraction absorbed PAR from
year one of MODIS data, Remote Sensing of Environment, 83, 214-231.
Running, S.W., and Coughlan, J.C., 1988, A general model of forest ecosystem processes for
regional applications I. Hydrologic balance, canopy gas exchange and primary production
processes, Ecological Modelling, 42, 125-154.
49
Remote Sensing & Earth Observation Techniques
Running, S.W., Nemani, R.R., Peterson, D.L., Band, L.E., Potts, D.F., Pierce, L.L., and Spanner,
M.A., 1989, Mapping regional forest evapotranspiration and photosynthesis by coupling satellite
data with ecosystem simulation, Ecology, 70 (4), 1090-1101.
Sellers, P.J., Dickinson, R.E., Randall, D.A., Betts, A.K., Hall, F.G., Berry, J.A., Collatz, G.J.,
Denning, A.S., Mooney, H.A., Nobre, C.A., Sato, N., Field, C.B., and Henderson-Sellers, A.,
1997, Modelling the exchanges of energy, water, and carbon between continents and the
atmosphere, Science, 275, 502-509.
Verstraete, M. M., and Pinty, B., 1996, Designing optimal spectral indices for remote sensing
applications, IEEE Transactions on Geoscience and Remote Sensing, 34, 1254-1265.
Verstraete, M.M., Pinty, B., and Myneni, R.B., 1996, Potential and limitations of information
extraction on the terrestrial biosphere from satellite remote sensing, Remote sensing of
environment, 58, 201-214.
Walthall, C., Dulaney, W., Anderson, M., Norman, J., Fang, H., and Liang, S., 2004, A
comparison of empirical and neural network approaches for estimating corn and soybean leaf
area index from Landsat ETM+ imagery, Remote Sensing of Environment, 92, 465-474.
Weiss, M., Baret, F., Myneni, R.B., Pragnere, A., and Knyazikhin, Y., 2000, Investigation of a
model inversion technique to estimate canopy biophysical variables from spectral and directional
reflectance data, Agronomie, 20, 3-22.
Weiss, M., Baret, F., Smith, G.F., Jonckheere, I., and Coppin, J., 2004, Review of methods for in
situ leaf area index (LAI) determination, Part II. Estimation of LAI, errors, and sampling,
Agricultural and Forest Meteorology, 121, 37-53.
50
Ground Penetrating Radar and Peat Depth
Introducing Ground-Penetrating Radar
(Instructor: Steve Dobson and John Jones)
Ground-Penetrating Radar (GPR) provides a means of inspecting and mapping sub-surface
geology, archaeology and modern buried features such as structures or services. High-frequency
electromagnetic radio (radar) is transmitted into the ground and the time of transmission,
reflection and return to the antenna is calculated by the instrument. The radar signal typically is
reflected by minute changes in the electrical properties of soil sediment, water content,
geological nature, density or stratigraphic change. The antenna then receives the reflected waves
and stores them in the digital control unit. The ground penetrating radar antenna (transducer) is
pulled along the ground by hand or behind a vehicle.
Electromagnetic waves travel at a specific velocity that is determined primarily by the electrical
permittivity of the material. The velocity is different between materials with different electrical
properties, and a signal passed through two materials with different permittivity over the same
distance will arrive at different times. The interval of time that it takes for the wave to travel
from the transmit antenna to the receive antenna is simply called the transit time. The basic unit
of electromagnetic wave travel time is the nanosecond (ns), where 1 ns=10-9 s. Since the
velocity of an electromagnetic wave in air is 0.3 m/ns, then the travel time for an electromagnetic
wave in air is approximately 3.3333 ns per m travelled. The velocity is proportional to the
inverse square root of the permittivity of the material, and since the permittivity of earth
materials is always greater than the permittivity of the air, the travel time of a wave in a material
other than air is always greater than 3.3333 ns/m.
The penetration depth of GPR is based on many factors - one of the most influential of
these is the frequency of the antenna. Typically antenna frequencies range between 10-1000
MHz with lower frequencies achieving much greater depths, however due to the longer
wavelength of these frequencies, vertical resolution is much lower.
Another major factor is the nature of the materials that the radar wave passes through.
Ground penetrating radar waves can reach depths up to 100 feet (30 meters) in low conductivity
51
Ground Penetrating Radar and Peat Depth
materials such as dry sand or granite. Clays, shale, and other high conductivity materials, may
attenuate or absorb GPR signals, greatly decreasing the depth of penetration to 3 feet (1 meter) or
less. In this case the electromagnetic energy of the signal needs to be able to pass through a
material without being absorbed or dissipated – this attribute is described as the Relative
Dielectric Permittivity of a material and outlines the velocity of radar waves through the material.
Examples of high RDP materials:
Water
Saturated Silt
Clay
80-88 RDP
10-40 RDP
5-40 RDP
Examples of low RDP materials:
Air
Dry Sand
Concrete
1 RDP
3-5 RDP
6 RDP
Consequently, large reflections will be observed at the interface between thick layers of very
different RDP, conversely gradual change or small changes ever few centimetres will generate
very small responses.
Other factors that affect the ‘visibility’ of subsurface features or geological interfaces are the
effects of focussing/dispersion and the alignment of the feature or interface to the signal.
Focusing or dispersion occurs when transmitted energy passes through a series of materials with
increasing (focusing) or decreasing (dispersion) RDP.
FOCUSING
RDP 1
RDP 5
RDP
50
52
Ground Penetrating Radar and Peat Depth
DISPERSION
RDP
50
RDP 5
RDP 1
The alignment of a surface or interface will also play a large part in its detection since if it is
steeply sloping transmitted waves will be deflected away from the antenna. Since the return
signal in this case will be absent (or greatly weakened) the feature will be ‘invisible’.
DEFLECTION
53
Ground Penetrating Radar and Peat Depth
GPR EXAMPLE PLOT: This example plot shows a number of very clear return signals from
localised buried features.
The GPR approach has been used in the past to determine peat depth (have a look at :
http://nesoil.com/gpr/gprgis.htm) and we will try to use this technique at our field site and
compare estimates to real peat core measurements. Remember, a problem might always be the
high water content (i.e. high RDP) of the peat.
Establishing depth scales:
As mentioned earlier, the vertical scale of the radar profile is a time scale which shows the time
it takes for the signal to travel through the subsurface and return to the antenna. This time scale
can be converted to a depth scale if the signal propagation velocity is known. Propagation
velocities can be either calculated or estimated if ground truthing is not performed. Calculating
the propagation velocity is usually performed by burying a reflector at a known depth and
determining the velocity using the following formula:
Vm = 2D/t
Where:
D
T
Vm
= measured depth to reflecting interface.
= elapsed time between transmitted and received pulse (nanoseconds).
= effective propagation velocity (feet/nanosecond)
If propagation velocities are known, depths can be estimated using the following formula:
Depth = Vm(t)/2
Propagation velocities are often estimated using standard published values for various materials
(see RDP table above). Anyhow, the software will do this for us!
54
Ground Penetrating Radar and Peat Depth
55
Annex 1
Annex 1 - Two-tailed t-table
t-tables
n
p=0.05 p=0.01 p=0.001
n
p=0.05 p=0.01 p=0.001
1
12.706
63.656
636.578
22
2.074
2.819
3.792
2
4.303
9.925
31.6
23
2.069
2.807
3.768
3
3.182
5.841
12.924
24
2.064
2.797
3.745
4
2.776
4.604
8.61
25
2.06
2.787
3.725
5
2.571
4.032
6.869
26
2.056
2.779
3.707
6
2.447
3.707
5.959
27
2.052
2.771
3.689
7
2.365
3.499
5.408
28
2.048
2.763
3.674
8
2.306
3.355
5.041
29
2.045
2.756
3.66
9
2.262
3.25
4.781
30
2.042
2.75
3.646
10
2.228
3.169
4.587
32
2.037
2.738
3.622
11
2.201
3.106
4.437
34
2.032
2.728
3.601
12
2.179
3.055
4.318
36
2.028
2.719
3.582
13
2.16
3.012
4.221
38
2.024
2.712
3.566
14
2.145
2.977
4.14
40
2.021
2.704
3.551
15
2.131
2.947
4.073
45
2.014
2.69
3.52
16
2.12
2.921
4.015
50
2.009
2.678
3.496
17
2.11
2.898
3.965
75
1.992
2.643
3.425
18
2.101
2.878
3.922
100
1.984
2.626
3.39
19
2.093
2.861
3.883
20
2.086
2.845
3.85

1.96
2.576
3.29
21
2.08
2.831
3.819
56
ANNEX 2
Annex 2 – Soil Respiration system inter-comparison:
LESC Exploratory workshop Investigating the role of soils in the terrestrial carbon balance –
harmonising methods for measuring soil CO2 efflux; Edinburgh, 6-8 April 2000) workshop
Closed static systems
Three types of closed static systems are distinguished:
1.
Chemical systems, using NaOH, KOH or soda lime to precipitate or adsorb CO2,
2.
Physical systems, where CO2 is sampled from the headspace of the chamber,
3.
Automated physical systems, where the closure and headspace sampling is automated.
Chemical systems
Limitations (-) and Advantages (+)
Accuracy
When the chemicals become saturated or when CO2 concentrations within the chamber
rise much above ambient levels, large underestimation errors can occur.
Strong CO2 uptake by the chemicals can lead to a change in the naturally occurring
diffusion gradient.
Long closure times and edge effects, especially in small, shallow chambers, can lead to
increased errors.
Long exposure times might cause heating of the chamber, affecting rate constants for
chemical reactions and root or microbial activities.
Resolution
+
Method offers potential to integrate over time, e.g. at night.
+
Lots of replicates can be used. Thus, good method for inhomogeneous sites.
Fast measurements are impossible because exposure times are long.
Convenience
+
Method is cheap.
+
Laboratory preparation is fast, about 5 - 10 minutes per sample.
+
Easy experimental control in the field (non-emitting surface, same set-up as "real"
chambers).
+
Off site analysis of sample.
Other
There might be problems for subsequent isotope analyses.
Bad prestige among scientists.
Minimum (•) and recommended (*) technical specifications
•
Use NaOH, KOH or sodalime.
•
Solution in tray, 7 cm above the soil surface, with tray covering ~1/3 soil area.
•
Chemical must not be saturated more than 10 - 50 % at the end of the measurement.
•
Chambers up to 5 l in volume, 20 cm in diameter, 15 - 20 cm height.
•
Exposure time between 8 and 10 hours.
•
Liquids are better than granules, granule size should be as small as possible.
•
The bigger the area of chemical, the better the representation of the actual flux.
Summary
Useful for highly diverse sites where a large number of measurements is needed to capture
spatial variation. Method should be cross-compared to other measurements at individual sites.
Could be the method of choice around a small number of automated chambers.
Physical systems and automated physical systems
Accuracy
57
ANNEX 2
+
High accuracy.
If non-linear increase of headspace CO2 concentration is not accounted for by multiple
sampling and non-linear regression, the flux will be underestimated.
Resolution
+
Chambers can be made very large, minimising the problems associated with small scale
spatial variability.
Extensive temporal sampling (e.g. diurnal cycle) is labour intensive.
Convenience
+
Easy to learn and do.
+
Bottles can be stored for weeks or months.
+
Off site analysis of sample.
Requires a GC in the lab.
Other
+
Parallel analyses of other trace gases are possible.
+
Parallel analyses of isotopic composition are possible.
Minimum (•) and recommended (*) technical specifications
•
Chamber at least 20 cm height.
•
Sample stored in 20 ml gas-tight glass vial.
•
At least two headspace samples per measurement.
•
Three or more headspace samples per measurement gives increased confidence in
extrapolation of flux to intercept (at t = 0.)
Summary
If a GC is available this method is very powerful, particularly if the research is focussed on a
limited number of sites or if fluxes of gases other that CO2 are being measured.
Closed dynamic chambers
Two types of closed dynamic systems are distinguished:
1.
Manual system where a chamber is placed in position and removed after measurement.
2.
Automated system where a chamber remains in place for a long time (open & closed).
Limitations (-) and Advantages (+)
Accuracy
+
Because no differential analyser is used, IRGA calibration less important.
If a fan is used for mixing, the speed of the fan can alter flux rates (pressure changes).
The calculated efflux rate depends on the chamber volume and soil air volume (?!).
As not in the steady-state chamber effects might cause measurement artefacts (wind!).
Resolution
+
Measurement can be taken in a very short time, spatial sampling can be extensive.
Extensive temporal sampling (e.g. diurnal cycle) is labour intensive.
Convenience
+
Commercial systems are available and easy to use.
Other
+
Commercial systems could make this method a “standard” methodology.
Minimum (•) and recommended (*) technical specifications
•
The chamber should be fitted with a pressure vent. The tube should be long and thin
(possibly at ground level) to minimise diffusive loss from the chamber.
•
A correction should be made for the volume of air in the soil below the chamber.
•
Ensure good mixing inside the chamber.
•
If using a mixing fan, the effect of fan speed should be investigated.
•
Chamber should be opaque as a 1°C increase in temperature might lead to ~ +300 Pa.
•
The linearity of the increase in concentration over time should be investigated.
•
At least three readings at each measurement spot.
58
ANNEX 2
*
Minimise the ‘trampling’ around the chamber (causing mass flow of air into chamber).
Summary
Simplicity of use and low price make this system an attractive choice.
Open dynamic systems
Again, two types of open dynamic systems are distinguished:
1.
Manual system where a chamber is placed in position and removed after measurement.
2.
Automated system where a chamber remains in place for a long time (open & closing).
Accuracy
+
High, if artefacts removed.
+
All necessary variables can be measured accurately.
+
Steady state measurement.
Depends on clever design to overcome pressure effects.
Prone to condensation (when running continuously).
Resolution
+
Allows continuous measurements.
+
High temporal resolution, once chamber is in steady-state.
Chamber can take up to 10 minutes to reach steady-state.
Small number of chambers (when running automatically).
Convenience
Needs more electrical power than closed chambers (pumps).
Other
Requires costly differential gas analyser and mass flow controller.
Minimum (•) and recommended (*) technical specifications
•
Only steady-state measurements should be reported.
•
The pressure difference should be less than 0.3 Pa (preferably less than 0.1 Pa).
•
The volume of the analysis and reference sample lines should be the same.
*
Care should be taken to prevent condensation in the sample tubes.
*
Smaller chamber volume gives a faster response time.
Summary
If chamber pressurisation artefacts can be eliminated, the steady-state nature of the measurement
means that this system potentially gives the most meaningful chamber data.
Eddy covariance
Accuracy
+
Does not disturb system being measured, especially important in terms of
•
Local pressure fluctuations and natural transport processes,
•
CO2 concentration,
•
Temperature,
•
Soil moisture.
+
Probably ± 25% most of the time.
Difficult to test accuracy of method against standard.
Many uncertainties (frequency response, mean flow direction, non-turbulent motions).
Resolution
+
Spatially integrated measurement, scale 10 - 100m.
+
Continuous measurement.
+
Potential for identifying turbulence effects such as pressure pumping.
Low spatial resolution.
Measures bulk properties, i.e. cannot separate soil from ground vegetation.
Variable sample area (footprint).
Trade off between time averaging and representativeness.
59
ANNEX 2
Convenience
Complex, not plug and play.
Understanding the data requires more measurements/knowledge.
High power requirements.
Other
Expensive.
Limited applicability on slopes or under dense canopies.
Minimum technical specifications
•
Standard protocol of additional measurements/corrections important.
•
Deployment protocol is site dependent.
•
Special attention must be paid to frequency related aspects.
Summary
This method gives the most accurate representation of the naturally occurring soil CO2 flux, but
this might not be the same as soil respiration because it includes understorey assimilation etc.
Collecting, correcting and interpreting the data is demanding and can be problematic.
General considerations - summary
All important variables must be measured accurately, (i) closed chambers: volume,
concentration, time, chamber area; (ii) open chambers: flow rate, concentration differential,
chamber area. There is a general trade off between sampling and resolution, a few locations can
be measured continuously (with an automatic system) or many locations can be measured less
frequently (with a manual system).
To ensure no alteration of the soil environment (temperature, moisture, litter input) chambers
should not be left in place for more than 24 hours unless they are of the automatically opening
type. Closed static chambers should cover at least 400cm2 of soil. For other systems chamber
size should be chosen according to requirement. Small chambers can be placed between plants or
in different micro topographical positions. Larger chambers can integrate over larger areas and
might give a more representative measurement. For open systems, very large chambers (> 1m2)
require large air flows and are power intensive.
Collars should be placed at least 24 hours prior to measurement to avoid measuring any initial
flush of CO2. Collars have two roles, to seal the chamber/soil boundary, and to provide
anchorage for the chamber. The depth of insertion into the soil will therefore vary from site to
site. Factors that should be considered are,
•
Soil porosity – more porous soils means deeper insertion
•
Depth of the root mat
•
Depth of the water table
When measurements are to be made only once, severing roots with the collar will not cause
unreliable data. When a collar is inserted and left for a long period, severing the root mat will
alter the carbon dynamics of the soil under the chamber and should be avoided. Where this is
unavoidable, the collar should be placed in the winter before measurements are made, and should
have holes (Swiss cheese collar) through which new roots can grow. An alternative is to have a
shallow collar with anchoring legs that are inserted deeper into the soil.
60
Annex 3
Annex 3 - Field Site Soil Respiration Coordinates:
Flux Tower Site 1
UK grid:
Easting
Northing
GPS:
SD
83535
RNG 71749
383535
471749
Collar locations (always 3 x 8 inch collar each with a 4 inch collar inside):
SD
RNG
Plot1
83549
71751
Plot2
83537
71741
Plot3
83517
71741
Plot4
83530
71732
Plot5
83524
71722
Plot6
83544
71754
FT
83535
71749
Flux Tower Site 2
(incl. burnt vs. un-burnt area)
UK grid:
Easting
Northing
GPS:
SD
83575
RNG 71588
383575
471588
Lime treatment
UK grid:
Easting
Northing
GPS:
SD
83711
RNG 71808
383711
471808
Semivariogram collars
UK grid:
Easting
Northing
GPS:
SD
83519
RNG 7172
383519
471721
61
Annex 4
Annex 4 - List of Participants
Record
First
Name
Surname
Organisation
Type
Email
Address
Tel:
1
Ilaria
Inglima
Second
University of
Naples
phd
ilaria.inglima
@unina2.it
Department of
Environmental Sciences
390823274648
Via Vivaldi, 43 Caserta
81100
2
Daniel
Metcalfe
University of
Edinburgh
phd
d.b.metcalfe@s
ms.ed.ac.uk
Flat 2F1, Warrender Park
Terrace
+44 (0) 7745 492 655
Edinburgh
EH9 1EE
3
Tim
Jupp
CEH Monks
Wood
pod
tju@ceh.ac.uk
Abbots Ripton
+44 (0)7941 906814
Huntingdon
PE28 2LS
4
Sabine
Göttlicher
Swedish
University of
Agricultural
Sciences
phd
sabine.goettlic
her@sek.slu.se
Department of Forest
Ecology
0046 (0) 90-786 8361
Petrus Laestadius väg
SE - 901 83 Umeå
5
Luiz
Eduardo
Aragao
University of
Oxford
Pod
laragao@ouce.
ox.ac.uk
62
School of Geography and
the Environment
+44 (0)1865 281980
Annex 4
University of Oxford
Mansfield Road
OX1 3TB
6
Chris
Miller
University
Of Aberdeen
phd
c.j.miller@abd
n.ac.uk
29 Hilton Road
7974945642
Aberdeen
AB24 4HR
7
Iain
Hartley
University of
York
phd
iph106@york.a
c.uk
Area 2, Biology
Department
01904 328594
Heslington, York ,
YO10 5YW
8
Timothy
Hill
CTCD,
University of
Edinburgh
phd
t.c.hill@sms.ed
.ac.uk
The University of
Edinburgh
7793058251
Crew Building The Kings
Buildings
EH9 3JN
9
Suresh
Kumar
Jogi
Department
of Botany
phd
jogis@tcd.ie
Trinity College
00353-1-6081051
Dublin-2
10
Katherine
Chadwick
University of
Aberdeen
phd
k.chadwick@a
bdn.ac.uk
Plant & Soil Science
Department
07832 192962
St Machar Drive ,
Aberdeen, AB24 3UU
11
Luke
Spadavecchia
Univerity of
Edinburgh
(CTCD)
phd
s0198247@sms
.ed.ac.uk
63
Flat 2F1
07838 116632
Annex 4
16 Gillespie Crescent
Edinburgh, EH10 4HT
12
Ana
Prieto
University of
Wales
Swansea
phd
304376@swan.
ac.uk
13 Clifton Hill
+44 7800757671
SA1 6XQ
13
Rui
Zhang
University of
Edinburgh
phd
R.Zhang2@sms.ed.ac.u
k
Crew building (Attic)
(44)131 6506480
School of Geosciences,
Edinburgh University ,
EH9 3JN
14
Angela
Gallego-Sala
University of
Bristol
phd
Angela.Gallego
Sala@bristol.a
c.uk
Wills Memorial Building
+ 44 (0) 117 954 9500
Queens Road, Bristol,
BS7 8TP
15
Casey
Ryan
University of
Edinburgh
phd
me@caseyryan
.co.uk
44 Lordship Rd
2088021641;
01603-593516
London, N16 0QT
16
Claudia
Di Bene
Scuola
Superiore
Sant-Anna
Pisa
phd
claudine@sssu
p.it
P.zza Martiri della
Libertà 33, 56127 Pisa
393334562263
17
Louise
Grøndahl
National
Environmen
tal Research
Institute
phd
lgr@dmu.dk
Frederiksborgvej 399
P.O.Box 358,
+45 46 30 19 31
DK-4000 Roskilde
64
Annex 4
18
Hildegard
Meyer
University of
Vienna
phd
loiseleuria@ya
hoo.de
Dep. of Chemical Ecology
and Ecosystem Research
0043 1 42 77 54 251
Althanstrasse 14
Vienna, A-1090
19
Gustavo
Saiz
Univ.College
Dublin, Dep.
Env.
Resource
Manag.
phd
gustavo.saiz@u
cd.ie
Faculty of Agri-Food and
the Environment,
+353 87 925 7794
Dublin 4
20
Diana
Hernandez
CSIC, Center
for
Environmen
tales Science
phd
dhernandez@c
cma.csic.es
C/ Serrano 115 bis.
Madrid, 28006
21
Albin
Hammerle
Universtiy of
Innsbruck
phd
csac3404@uib
k.ac.at
Tiergartenstr. 25b/3/31
+34 91 411 5301
A-6020 Innsbruck
22
Michael
Schmitt
University of
Innsbruck
phd
schmitt.michel
@gmx.de
Fischnalerstrasse 12
43512937953
A-6020-Innsbruck
23
NOEL
ROBERTSON
UNIVERSIT
Y OF
SHEFFIELD
phd
n.a.robertson
@sheffield.ac.u
k
HICKS BUILDING
0114 2223749
HOUNSFIELD ROAD
SHEFFIELD S3 7RH
24
Jessica
Lopez
University of
Helsinki
phd
jessica.lopezbe
llido@helsinki.
fi
Niemenkatu 73, 15140
Lahti - Finland
Pääjarventie 320, 16900
Lammi-Finland
65
358-3-89220308
Annex 4
25
Torbjörn
Johansson
Lund
University
phd
torbjorn.johan
sson@nateko.l
u.se
GeoBiosphere Science
Centre (CGB)
+46 (0)46 222 39 74
Physical Geography and
Ecosystems Analysis
(INES) Sölvegatan 12, SE
223 62 Lund
26
Inge
Vande Walle
Ghent
University
phd
Inge.VandeWal
le@UGent.be
Coupure links 653, 9000
Gent 9000
+32 9 264 61 26
27
Teresa
Bertolini
C.N.R.I.S.A.Fo.M.
pod
t.bertolini@isp
aim.na.cnr.it
Via Patacca, 85
0039 081 5746606
28
Helen
Bowes
University of
Bristol
phd
h.l.bowes@bris
tol.ac.uk
Wills Memorial Building
0117 3315037
Queens Road Bristol BS8
1RJ
29
Helen
O-Brien
Nottingham
Trent
University
phd
helen.obrien@
ntu.ac.uk
School of ARES
44 01636 817000
Brackenhurst Southwell
Nottinghamshire NG25
0QF
30
Peter
Eliasson
Swedish
University of
Agric.
Sciences
phd
peter.eliasson
@eom.slu.se
Dept. for Ecology &
Environmental Research
SE 750 07
+46 (90) 672458
phd
a.wakefield@la
ncaster.ac.uk
IENS
07816166702 (mobile)
(SLU)
31
Angela
Wakefield
Lancaster
University/C
EH
Lancaster
Lancaster University
Lancaster
LA1 4YQ
66
Annex 4
32
Martin
De Kauwe
Edinburgh
University
Bruce
Thomson
Lancaster
University/C
EH
Lancaster
33
34
Kevin
Jones
University of
Edinburgh
phd
mdekauwe@g
mail.com
brth@ceh.ac.u
k
Hydrology, Oxford
phd
phd
Centre for Ecology and
s0197746@sms
.ed.ac.uk
The Univeristy of
Edinburgh
Crew Building
The Kings Buildings
EH9 3JN
67
+441865281684