2
Rui Zhang
(Supervised by John Grace Mathew Williams)
School of Geosciences, University of Edinburgh
Carbon is one of the most fundamental elements which constitute the life on the earth, making up about 50% of the dry mass of all organisms. About 120 billion tonnes of carbon as carbon dioxide are transferred between the atmosphere and planetary surface each year in a cyclic process. As carbon dioxide is among several important greenhouse gases that absorb infra-red radiation emitted by earth surface and because of its absorbing ability and rising concentration in atmosphere, the carbon cycle has become an intense research subject in environmental science. The carbon equilibrium in the atmosphere has been disturbed due to the anthropogenic carbon emissions since the industrial revolution and this has caused the additional global warming in modern times. Therefore, most governments support the aim “: to achieve stabilisation of greenhouse gas concentrations at a level that would prevent dangerous anthropogenic interference with the climate system .
”(The Framework Convention on Climate
Change)
In the early 1990’s, it has been pointed out that the amount of carbon dioxide related to human activities exceeds the sum of annual increase measured in the atmosphere and that the ocean sink is not strong enough to account for the difference.
Accordingly, it is postulated that there must be some “carbon sink” in the terrestrial system, very likely in forests (Kroecker et al 1979).
My project objectives are:
 to develop a methodology, using flux data, models and remote sensing, to be able to estimate the biotic carbon sink of entire countries
 to apply the methodology to UK, Europe and elsewhere
The scheme below outlines my research project to be achieved in three years of PhD study.
1

Carbon flux data
Weather data
Models
Land cover
data
Geographical distribution of fluxes and country totals
Existing models will be parameterized and modified where necessary using flux data for different types of land covers. The models will be run using weather data which exist already as freely available data and the results will enable us to estimate the spatial distribution of CO
2
fluxes and the country totals.
These objectives can be reached by combining flux data, flux modelling, remote sensing of land cover. The following sections outline the state-of-the-art in these three areas:
2.1 Flux data
Eddy covariance can offer direct and continuous measurement of net ecosystem productivity (NEP) between the biosphere and the atmosphere (Valentini et al, 1996).
In essence, the flux systems comprise a three-axis sonic anemometer, a closed-path infrared gas analyser (IRGA) and a suite of analysis software for real-time and postprocessing analysis (Aubinet et al 1996; Moncrieff et al 1997). The sonic anemometer, which measures wind velocity in three directions, is normally mounted on the top of tower above the vegetation. In the close-path analyser, a tube, which is connected pump, draws air from above the canopy to the IRGA. Eddies are sampled for their vertical velocity and concentration of water and carbon dioxide and then the sample are ducted down a sample tube to the IRGA, where water vapour and CO
2 concentration are measured. In an open-path analyser, there is no tube. The infra-red beam is sent across the air mass adjacent to the sonic anemometer. Raw data requires calibration and correction, applied by a real-time computer program.
The EUROFLUX project, sponsored by the Fourth Framework Programme of the
European Commission was established to make continuous and long-term of carbon and water exchange between European forests and the atmosphere, and also provide information about the role of terrestrial biosphere in the climate system .
2

2.2 Flux models
2.2.1 SVATs
Energy and mass transfer among soil, vegetation and atmosphere is fundamental to understanding how the vegetation influences the atmosphere. Understanding the interactions enables formulation of models which can provide accurate simulation on what is going to happen. Soil-Vegetation-Atmosphere transfer models (SVATs) are used to describe these interactions and more recently link to global and regional climate models.
Cowan (1964) was one of the first to develop SVATs. He explored the dynamic aspects of water flows in the soil-vegetation-atmosphere system. Cowan simplified the water transport process by categorizing and linking all the variables that have an effect on water potential into two functions: potential transpiration rate, which depends on environmental factors; and supply function, which has related to water content, soil hydraulic conductivity. The effect of these two functions in determining the rate of water loss is affected by the chemical potential of water in leaves and the soil.
In his formulation, water transport in the plant is considered as a catenary process and its rate is directly proportional to the water potential difference across successive components and inversely proportional to the impedance or resistance along the path of flow. The resistance depends on the microstructure of the soil and plants as water is drawn through the system.
The general equation is as follows:
In the equation, three conditions must be satisfied: i. the system is isothermal; ii. the net flow of solutions through the system should be small or should take place through a mechanism independent of that of water transport. iii. osmotic potential always exists even there is no dissolved solutes in the water. Essentially there are two alternative pathways of predominantly vertical water movement-directly through the soil and atmosphere on the one hand, and through the xylem vessels of the primary roots and of the stems on the other-which are interconnected by subsidiary parallel pathway(Cowan 1964). Water transport in the soil-plant-atmosphere is assumed to be steady flow. The movement of soil water towards plant roots cannot strictly be described in terms of a steady state model unless water is withdrawn from a water table continuously replenished by lateral flow (Wind 1955). Cowan considers the soil as a “reservoir ” and water is depleted as a results of extraction by the roots.
3

2.2.2 SiB
Fig1 The general scheme for SiB, represents the transfer of water and CO
2
through the air to the leaf and their control by the stomatal resistance.
Following the formulation of SVATs, the next step was to add the flux of CO
2
.
Generally speaking, the main aim of modelling the land-surface and atmosphere interactions is to identify all the small scale physical process that could possibly happen and integrate them into one model. Besides, the extent of the relationship between environment and organism, from microscopical level (such as photosynthesis and respiration at molecule level) to macroscopical level (such as water transportation within the ecosystem) are essential parts of modelling.
Based on the strategy, one of these most successful models was the Simple Biosphere Model (SiB). Simple
Biosphere Model (SiB) has been developed by Seller(1986) to calculate the transferred energy, mass and momentum between the atmosphere and the vegetation on the surface of earth. Charney et al (1977) and subsequent researchers showed that changing the land surface albedo can produce significant changes in the large-scale atmospheric circulation and rainfall. Walker and Rowntree(1977), Shukla and
Mintz(1982) and others have demonstrated that changing the available soil moisture may have large feedback effect on the continental climate. In almost all the existing global circulation models, the fluxes of radiation, heat and momentum across the lower boundary of the atmosphere are treated as independent processes. Usually, the surface flux is made to depend on independent environmental-related factors. For example, the dependence of evapotranspiration on soil moisture has been generally conceptualized as a “bucket” in which the level of water goes down when evaporation exceeds precipitation, and is raised when precipitation is abundant, and runoff occurs
4

at the point where the “bucket” overflows. The water lever varies from model to model, the rate of evaporation is taken as equal or nearly equal to that from a freely evaporating surface, and is reduced only when the water level is low. However, instead of passive sponge-like structure as implied by the “bucket” model, the real plant has evolved into complex physiological structure to make water and nutrients transport through the system with the maximum prospect of survival. Understanding the interactions between earth surface and atmosphere should be taken into consideration during modelling.
The features of SiB are as follows: i. Carbon assimilation uses the biochemical model of photosynthesis by Farquhar et al (1980)
Photosynthesis is the minimum of the rate under two conditions: Rubisco (Ribulose
Bisphosphate Carboxylase/Oxygenase) limited and electron-transport limited.
Under the Rubisco limited condition, we have:
A v
=V max
(
C c
-I
K c
(1+pO
2
*
/K o
)+C c
)-R c, where V max is the maximum photosynthetic rate, pO
2
is the ambient partial pressure of oxygen, K c and K o
are the Michaelis-Menten constants for carboxylation and oxygenation by Rubisco, respectively, C c
is the partial pressure of CO
2
in the chloroplast, I * is the CO
2
compensation partial pressure in the absence of dark respiration, and R c
is the rate of dark respiration by the canopy.
Under the electron-transport limited condition, we have:
J
A
J
= ( )-R c,
4
C c
-I
*
C c
+2I
* where J is the rate of electron transport, which is related to the maximum (lightsaturated) rate of electron transport, C c
is the CO
2
partial pressure in the chloroplast, I
* is the CO
2
compensation partial pressure in the absence of dark respiration, and R c
is the rate of dark respiration by the canopy. The actual rate of photosynthesis is the minimum of these two formulation.
This model has been used directly to model the whole ecosystem as a “big leaf”
(Lloyd et al 1995). ii. Stomatal control the inward diffusion of CO
2
as well as the outward diffusion of water
The stomatal resistance of individual leaf, r s
(=1/g s
) was proposed by the empirical model of Jarvis(1976)
r s
= [ a/(b+Fs) +c] f(ψ
1
) -1 f(T) -1 f(T a
,e a
) -1
5

where a,b and c are constants determined by leaf maximum and minimum stomatal conductance; Fs is photosynthetic active radiation (PAR) flux incident on leaf surface; f(ψ
1
), f(T) and f(T a
,e a
) are adjustment factors for the influence of leaf water potential, temperature and atmospheric water vapour pressure deficit.
However, this model was formulated on the coniferous trees and thus required a great number of parameters on vegetation types.
Collatz et al. 1991 combined his photosynthetic model with the Ball (1988) semiempirical model for leaf stomatal conductance, in which stomatal conductance is linearly related to photosynthesis: g s
An
Cs s p + b , where An is the leaf photosynthetic rate; m and b are coefficient parameters; h s is the relative humidity at leaf surface, while c s
and p are CO
2
partial pressure at leaf surface and atmospheric pressure respectively. In the formulation, photosynthesis is controlled by three limitations ( Farquhar and Berry ):
An=min(A c
, A l
, A s
)-Rd, where A c
, A l
and A s
represent Enzyme kinetics, light and starch respectively. iii.Hydraulic conductivity in soil and leaf
SiB assumes that canopy resistance is equal to the effect of all the canopy leaves stomata function when the surface of canopy is dry; the below canopy resistance is the combination of groundcover and soil diffusion resistance. Following van der
Honert(1948), the leaf water potential is calculated by using a catenary model of water transfer pathway from root zone to leaf:
ψ l
=ψ r
-z
T
-E d
/ρ w
(r plant
+r soil
), where z
T is the height of transpiration source; E d
is transpiration rate. Within the equqtion, r soil is the area-averaged resistance to flow of water from the soil to root, which is described by a depth-averaged form of relationship proposed by
Federer(1979): r soil
=(R/D d
+α f
/K r
)/z d
, where K r
is the soil hydraulic conductivity in the root zone, which is determined by soil water potential and soil type. The direct evaporation from the soil surface depends on the difference in canopy air vapour pressure and soil surface. The gradient of vapour pressure varies greatly near the soil surface, a surface resistance is used to relate the surface vapour pressure and in-depth soil vapour pressure.
6

iv. Leaf water potential in controlling the opening and closure of stomata
Water evaporates from the mesophyll cell into the air space of leaf, and then diffuse out of leaf through stomata, which surround mesophyll cells. The water loss cause a decrease in water potential in the mesophyll cell, which therefore results in a water potential difference between the adjacent mesophyll cells. It is the difference that makes water flow consecutively from high water potential to low water potential. The physiological control of evapotranspiration by plants is an evolved optimization mechanism that seeks to maximize carbon fixed by photosynthesis (by drawing CO
2 into leaves through stomatal pores) and yet reduce water loss from the plant (through stomata). v. Parameterization
In SiB, the World’s vegetation is classified into two morphological groups: trees or shrubs, which constitute the upper story canopy vegetation; and the ground cover, which consists of grasses and other herbaceous plants. As for the root and soil structure, the upper story vegetation consists of roots with a fixed depth root up to the bottom of soil layer 2, while the ground cover may have a time-varying root which doesn’t exceed soil layer 2. Soil layer1 is the upper thin soil, from which water evaporates into the air and soil layer 3 is beneath the root zone, where transfer of water is governed by gravitation and hydraulic diffusion. vi. incorporate micrometeorological information
Five atmospheric boundary condition variables, air temperature, vapour pressure, wind speed, solar radiation and precipitation are all provided by Global Circulation
Model(GCM).
The strategy of formulating SiB is to model the vegetation itself and to let the vegetation determine the way in which the surface interacts with the atmosphere.
7

2.3 SPA
SOIL PLANT ATMOSPHERE MODEL
 ln
BIOLOGICAL COMPONENT
H
2
O CO
2
Layer
C. Boundary
Layer
Windspeed
E n
( g sn
)
1
PHYSICAL COMPONENT
B. Radiation A.
Canopy Structure
PAR NIR [N] LAI n
R pn
C n
10
R s n
Sun & shade
D. Soil Water Potential & Soil-Root Hydraulic Conductivity
 s
Fig 3. Soil-Plant-Atmosphere model (SPA, Williams et al 1996 ) simulates the water and CO
2 transfer between vegetation and atmosphere under the control of stomata conductance. The physical components of the SPA model describes the canopy structure, interactions with solar radiation, canopy boundary layer and soil hydraulic conductivity; the biological components determines the inward of CO
2
and outward of water through stomata on leaf level.
SPA(Soil-Plant-Atmosphere) was proposed by Mathew Williams et al 1996. SPA has many features like SiB, but is simpler and has a new way to control the stomata. The mechanism of CO
2
and water vapour diffusion through the stomata passage has evolved to optimize carbon uptake and water loss. When water transpires into the atmosphere, the tension inside xylem keeps the continuous steady-flow. If leaf water potential drops to a threshold, stomata will close to prevent the onset of cavitation— rupture in the water flow in the xylem.
A process-based Soil-Plant-Vegetation model (SPA, Williams et al 1996) has been developed to simulate ecosystem photosynthesis and water balance at fine temporal and spatial scales (30 minutes time-step, ten canopy layers and twenty soil layers).
SPA model is made up of a physical component and a biological component. The physical component describes the underlying principles that specify the structure of canopy, the absorbtion, reflection and diffusion of photosynthetically active radiation(PAR), near infrared(NIR) and shortwave energy; calculation of leaf boundary layer, soil hydraulic conductivity; the biological components determine how leaf water potential varies with transpiration, the variation of leaf biochemical parameters with foliar N contents, irradiance and leaf temperature, and the diurnal
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course of stomatal conductance’s control on water loss and CO
2 uptake. The opening and closure of stomata combines the process of both CO
2
assimilation and water loss.
The SPA model can be parameterized with flux data, by adjusting the foliar N content, the leaf area index, and the soil respiration rate. SPA is run with high resolution time and space climatological data which are available from a weather station around the flux site or by interpolation from global databases. Parameterization and upscaling are two key challenges in running the SPA model.
2.4 DGVMs
Models like SiB and SPA do not simulate the changes that occur in vegetation associated with processes such as succession and response to climate change.
Dynamic global vegetation models (DGVMs), combing vegetation dynamics and biogeochemical processes, have been development to simulate transient terrestrial ecosystem responses under rapid climate change. The representation of physiological , biophysical and biogeochemical processes in DGVMs include more or less mechanistic representations of photosynthesis , respiration and canopy energy balance, the controls of stomatal conductance and canopy boundary layer conductance, and the allocation of carbon and nitrogen within the plant (Cramer et al 2000). All of them treat vegetation cover as a fractional representation consisting of different types.
Canopy phenology includes the seasonal timing of budburst, senescence and leaf abscission in response to temperature and drought. Vegetation dynamics are based on annual net primary productivity (NPP) and biomass growth. Some of biomass in vegetation falls on the ground as litter, which becomes substrate for microbial decomposition.
Plant function types (PFTs) are central to DGMs because there are too many species to be able to model each species separately. They are assigned different parameterisations with respect to ecosystem processes. Plant functional types reduce the complexity of species diversity in ecological function to a few key plant types.
Many land models classify vegetation by biome. Albedo, roughness length, rooting depth, and stomatal physiology are usually used to describe the vegetation characteristics. However, photosynthesis and the carbon cycle pose a problem with the biome-based land classification, that is, how to standardize leaf physiology and carbon biomass distribution within the biome especially for mixed life-form biomes.
Rooting depths also vary greatly between different life-forms. One solution is to recognize that biomes consist of individual species or plant functional types (PFTs) that do have measurable leaf physiology and carbon allocation. Representing the landscape as patches of PFTs is a common approach that can link climate and ecosystem models. It provides direct linkage to leaf-level ecophysiological measurements and ecological theory. Bonan et al.(2002) describes 7 primary PFTs, which are needleleaf evergreen or deciduous tree, broadleaf evergreen or deciduous tree, shrub, grass, crop. PFTs are inferred from 1-km satellite data. Oleson and Bonan
(2000) describe this methodology for a region of the boreal forest. Bonan et al. (2002) describes the global implementation.
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Ultimately, the development of DGVMs is expected to lead to better tools for the assessment of impacts on multiple ecosystem functions beyond carbon storage, such as water resources, conservation values and forest productivity (Cramer et al 2000).
2.5 Remote Sensing for land cover
2.5.1 Fundamentals of remote sensing
Remote sensing is defined as acquisition of information about the earth surface without being in contact with it, by the sensing and recording of reflected or emitted energy and the processing, analyzing, and application of that information. There are two types of remote sensing systems: passive sensors and active sensors. Passive sensors refer to the systems that measure the reflected or reemitted energy available naturally, ususally from the sun. One disadvantage of most passive sensors is that they can only be used under cloudless condition; on the other hand, active sensor can direct radiation toward the target of interest and measure the reflectance. Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Some examples of active sensors are the laser fluorosensor and synthetic aperture radar (SAR).
2.5.2 Remote sensing for land cover
The initial application of remote sensing into imaging the earth’s surface began with the development of meteorological satellite TIROS-I in the early of 1960. The series of manned space programmes provided more accurate and more exciting photographs in 1960’s. The US department of Interior launched a conceptual study of the feasibility of a series of Earth Resource Technology Satellite Satellites (ERTS) in
1967. This program resulted in a planned sequence of six satellites ERTS_1,2,3,4,5 and 6. On January 22,1975, NASA officially renamed the ERTS program the
“Landsat” program.
Landsat 1 and 2 were launched onboard a three-channel return beam vidicon (RBV) system and a four-channel multispectral scanner (MSS) system.
The RBV system consists of three television-like cameras which view the 185 km by
185 km of ground area simultaneously. The nominal ground resolution of the camera was 80 m and the spectral sensitivity of each camera was 0.475-0.575 μm (green),
0.580-0.680 μm(red) and 0.690-0.830 μm (reflected infrared), which were designated as channel 1,2,3.
Many remote sensing systems contain MSS. The MSS covers a 185 km swath width in four wavelength bands: 0.5-0.6 μm(green), 0.6-0.7 μm(red), both 0.7-0.8 μm and
0.8-1.1 μm are reflected infrared. These bands are designated as channel 4,5,6,7. The
MSS operating system instantaneous field of view(IFOV) is a square and thus resulted in the ground resolution of 79 m. The arrangement required four arrays of six detectors each. The analog signal from each detector was converted to digital form by converters and then further scaled to other ranges be the subsequent ground-based processing.
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The newest in Landsat series of remote sensing satellites is Landsat 7. Launched on
15 April 1999, Landsat 7 has the new Enhanced Thematic Mapper Plus (ETM+) sensor. This sensor has the same 7 spectral bands as its predecessor, TM, but has an added panchromatic band with 15 metre resolution and a higher resolution thermal band of 60 metres. The ETM+ sensor also has a five percent absolute radiometric calibration
.
Landsat remains a very important programme for detecting land use change. Its channels cover the region where chlorophyll reflects strongly (green 0.5-0.6μm), and so it was used very early on to detect and monitor vegetation.
Many remote sensing systems record energy over several separate wavelength ranges at various spectral resolutions
.
This is called Multi-spectral sensors.
Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The finer the spectral resolution, the narrower the wavelength range for a particular channel or band
.
Accordingly
, hyperspectral sensor can detect hundreds of very narrow spectral bands throughout visible, near-infrared, mid-infrared spectrum.
Hyperspectral MODIS(Moderate Resolution Imaging Spectroradiometer), is the key instrument aboard the satellites Terra (EOS AM-1), launched on 18 December 1999 and Aqua (EOS PM-1), launched on 4 May 2002. MODIS views almost the entire surface of the Earth every day, acquiring data in 36 spectral bands over a 2330 km swath. It has different spatial resolutions for different spectral bands: 250m (band1-2),
500m(band3-7), 1000m(8-36). MODIS data will improve the understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is also playing a vital role in the development of validated, global, interactive Earth system models, able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.
SPOT4 is the fourth member of SPOT satellite family. It comes from European Space
Agency. Aimed to monitor the environmental change and update the land cover features, SPOT4 has two types of sensors onboard with different resolution: two
HRVIRs(High Resolution Visual Infra Red), each offering a 10m and 20m pixel size,
Vegetation has a 1km resolution at equator, but with high geolocation accuracy. The
Global vegetation Monitoring Unit use data from Vegetation sensor abroad SPOT4 and make a harmonized land cover FAO rule-based classification image in the world and different regions.
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2.5.3 Remote sensing for photosynthesis estimation
In addition to the application to the landscape feature and vegetation structure, remote sensing also can be used to detect photosynthetic capacity. Spectral vegetation index such as Normalized Difference Vegetation Index (NDVI) indicate the amount of greenness vegetation present in the pixel—higher NDVI values means more green vegetation. It is defined as: NDVI=(NIR-Red)/(NIR+Red). NDVI has been used to estimate canopy photosynthesis capacity. However, it has been argued that NDVI is only suitable to indicate how lush one specific area of vegetation is when compared with other area, but unable to provide accurate information on how much vegetation in that specific area.
The reflectance of leaves may contain a signal for photosynthetic efficiency and therefore provide a new method to detect changes in photosynthesis using remote sensing (Nichol et al 2000). Photosynthetic light-use-efficiency(LUE) is defined as the CO
2
assimilation rate divided by the incident photosynthetic photon flux density(PPFD). LUE was first thought to be more or less constant at least to first approximation. However, photosynthetic LUE has been found to decline in the high
PPFD as photosynthesis becomes light-saturated. Considerate work has been done to study the relationship between photochemical reflectance index(PRI) and photosynthetic LUE at leaf level and small plot level. PRI is defined as follows:
PRI=( R570-R531) / ( R570+R531), where R570 and R531 represent the leaf reflectance at 570 nm and 531nm respectively (Gamon et al. 1990, 1992, Penuelas et al. 1993, 1995).
Measurements on individual leaves have demonstrated that PRI, calculated from narrow waveband data, was closely related to LUE, calculated from gas exchange measurements in leaves from a wide range of species. (Penuelas et al.,1995). Fillela. et al and Gamon et al. (1997) have further presented evidence that PRI provided a widely applicable index of leaf LUE across species, functional types and nutrient levels. Nichol et al. (2000) use the measurement of reflectance by remote sensing in these spectral regions to infer canopy-scale LUE over contrasting canopy types suggest that PRI can serve as an index of photosynthetic LUE over heterogeneous forest canopies.
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My project aimed to estimate the carbon dioxide uptake by European forest area. My approach is, to define a forest area first, then apply a simple model to detect their capacity to absorb CO
2
, use the climatology form the Climate Research Unit of the
University of East Anglia; adopt an integration based on a spatial resolution of 0.5
0 latitude and longitude.
3.1 Global Landcover
The Global Vegetation Monitoring Unit (GVM) completed the development stage of
Global Land Cover 2000 Project(GLC 2000) in January, 2004. The co-ordination of the Global Land Cover 2000 project has been carried out under the Fifth Framework
Programme 1999-2002 for Research of the European Commission. The GLC2000 project was carried out to provide information to define the boundaries of the different ecosystems such as forest, grassland, and cultivated systems. It is the product of GVM in collaboration with a network of partners around the world. Taking advantage of the dataset from the sensor Vegetation onboard the Spot 4 Satellite, GLC 2000 Project provides a global as well as regional land cover map based on the land cover classification system from FAO.
Global Product from GLC 2000 Project
Fig 4 Global land cover classification using datasets from Vegetation abroad SPOT4, with 23 land features, under the coordination of European Commission Joint Research Centre.
ENVI and ArcGIS software was used to deal with the European map. ENVI is a powerful tool to visualize, analyze, and present of all types of digital imagery. ArcGIS package is ideal for management, analysis and display of geographical information.
After displaying the European map in the ENVI environment, an overlay of different country boundaries vector file was needed to define each country as Region Of
Interest (ROI). The Digital Chart of World, from Pennsylvania State of University
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provide updated country’s boundary in ASCII format or ARC/INFO interchange format. Loaded into ArcMap, the ASCII file could be converted into a shapefile which contains the geographical information on the country boundary. When combined with classification image, the ROI function in ENVI can take out the area within the country layer described in the shapefile. Through the class statistics function of ENVI, the number of pixels in each of classification can be calculated.
Since the pixel size is 1 km at the equator, we can calculate exactly the area of pixels elsewhere by squaring the cosine of the latitude of the centre of the pixel. Then by multiplying the number of pixels and the area each pixel stands for, we can obtain area for each country and find out the forest area in each.
European classification map
Fig5. forest distribution from GLC 2000 product
14

United Kingdom boundary layer
F ig6. UK country boundary from digital chart of world
United Kingdom Land Cover Map
Fig 7 Outcome of classification in combination with country boundary file through ENVI
15

Fig 8 UK woodlands distribution from forest commission.
In order to test the reliability of the GLC 2000 dataset, two other dataset were compared. One is the 2003 report of FAO, the other is from Karjalainen(2000).
In table 1 results from GLC 2000 are compared with the FAO report and the forest inventory data. There are differences (sometimes surprisingly high) between these data sources. Possible reasons for the differences are several. One is, different definitions of forest may have been used. GLC 2000 uses FAO land cover classification system .
A second reason, perhaps the most important one, is the problems of mixed pixels. The pixel size from GLC 2000 over Europe is from 0.03 to
0.75, patches of forest are usually smaller than the actual pixel. Thus one pixel represents at least two types of land cover features, which make our calculation confused. Under this condition, GLC defines the predominant land cover type as the content of these pixels. Thirdly, some regional or country products from GLC legend include parts of a neighbouring area, such as France. As one of the joint organisers for the Vegetation Programme, France is the only country in Europe, which has a separate classification image. In addition, both Karjalainen(2000) and GLC 2000 indicate the state of world forest in year 2000, while FAO report was released in 2003. As mentioned by FAO, forest area in each country increases or decreases over years due to afforest, reforestation, deforestation. Although the GLC 2000 image has a spatial resolution of 1 km at equator, which is relatively low resolution, geographical information is claimed to have high quality of estimation accuracy. Maybe that is the key point why people from GVM insist GLC 2000 are more reliable than others despite of the existing big difference.
In order to further testify the accuracy of GLC 2000 data, we choose some other countries or regions we have interest in to make estimation and compare with FAO report. We find that, generally speaking, our calculations from GLC are underestimated comparing to FAO and European inventory data especially for those countries with relatively small forest coverage. For Spain, Sweden and Germany who are famous for their large forest resource, the results are quite a good fit.
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Unit: 1000 hector
Country
Belgium
Denmark
Finland
France
Germany
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United Kingdom
Austria
Greece
Bleguim+Luxembourg
Poland
Slovenia
Lithuania
Cyprus
Estonia
Hungary
Latvia
Slovakia
Czech Republic
Forest area(Karjalainen) Forest area(GLC) Forest area(FAO)
531 404 not available
442
19919
13300
9905
326
17569
15389
10509
455
21935
15341
10740
344
5757
71
304
1508
13980
22219
250
7777
91
154
3746
13052
22078
659
10003 not available
375
3666
14370
27134
1898
2942 not available
602 not available not available not available not available not available not available not available not available not available
1366
3984
2419
496
6039
980
1007
156
1497
998
216
1641
1984
2794
3886
3599
728
9047
1107
1994
172
2060
1840
2923
2177
2632
Table1. The area of European forests obtained from these sources: Karjalainen(2000), FAO(2003) and the present method based on GLC 2000 classification( European Space Agency)
24
22
20
18
16
14
12
10
8
6
4
2
0
0 co m p ariso n tab le y = 0.9587x + 0.4897
R
2
= 0.975
2 4 6 8 10 12 14 16 18 20 22 24
K arjalain en (2000)
Fig9. Goodness of fit line for European
Forestry Institute and GLC2000 comparison table
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0 2 4 y = 1.1502x + 0.2911
R
2
= 0.9813
6 8 10 12 14 16 18 20 22 24
FAO
Fig 10. Goodness of fit line for FAO and GLC 2000
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3.2 Climatological Data
Climatological data were obtained from Climate Research Unit(CRU), University of
East Anglia. The construction of terrestrial high-resolution (0.5
0 × 0.5
0 ) climatology has been based on the weather station data. There are all together nine variables available: precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapour pressure, sunshine, cloud cover, ground frost frequency and wind speed. The strategy of the climatology is, separating the time and space component to construct a high resolution mean dataset and subsequently derive the gridded monthly anomalies according to the period for which the mean data are defined. The mean dataset and the anomaly are combined to determine the real data at the time series.
The high-resolution mean climatology is constructed from a number of station sources.
As many as possible stations data are used including the World Meteorological
Organisation(WMO) global standard normals. All the data have been subject to comprehensive quality control.
High resolution 0.5
0 
0.5
0 global average monthly temperature data is read out by a
Fortran program in gridded format(latitude, longitude,temperature) and processed through Access. Average monthly temperature for each country is interpolated from the half degree dataset by way of gridded area distribution.
Fig11 Gridded temperature map by ArcGIS
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Temperature
Filled Contours
1.00 - 3.40
3.40 - 5.80
5.80 - 8.20
8.20 - 10.60
10.60 - 13.00
13.00 - 15.40
15.40 - 17.79
17.79 - 20.20
20.20 - 22.59
22.59 - 25.00
Fig 12. Temperature contour map
3.3. Simple Linear Model of carbon uptake
We use a linear relation between Net Ecosystem Productivity (NEP) and temperature, which has been found from analysis of eddy covariance data at forest site throughout
Europe. These sites are chronosequences, so the data should tell us what happens over the life cycle of planting and harvestings. The data come from CARBO-AGE, an
EU project within CARBOEUROPE(Grace et al 2004). From henceforth, we will call the fitted model(Fig10), the Simple Linear Model(SLM).
The SLM contains additional data from N.America, Siberia and the tropical rain forest. All these non-European data points fall on the same straight line, indicating that SLM may be rather generally applicable to forests everywhere. (Grace 2004).
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C-flux from CARBO-AGE
Fig 13. Mean annual temperature is a linear function on mean NEP(R 2 =0.97, slope=0.1997). The data were obtained by integrating NEP across a chronosequence. The filled circle are Europe data; The filled rectangular is for rain forest; The blank rectangular represents forests in N. timellca; the filled triangle stands for forest in Siberia( Law et al 2003, Wirth et al 2003).
The relationship between mean temperature and mean NEP falls on the simple linear equation: NEP=0.1997*Temperature+0.93. This is the basic idea with which we are trying to estimate the European forest carbon sink capacity. That is, if we know the mean temperature and area of one area we have interest in, we can calculate the mean net carbon uptake within this area. Since we have already the temperature and forest area, all the remaining work is just simple computation.
Fig14 Calculated net carbon uptake by forest in each Europe country
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Unit: 10 12 C yr -1
Country
Belgium
Denmark
Finland
France
Germany
Ireland
Italy
Luxembourg
Netherlands
Portugal
Spain
Sweden
United Kingdom
Austria
Greece
Poland
Slovenia
Lithuania
Cyprus
Estonia
Hungary
Latvia
Slovakia
Czech Republic
NEP
1.163
0.802
22.955
47.626
27.848
0.703
28.301
0.248
0.434
14.903
47.253
30.588
3.600
8.885
9.694
15.031
2.641
4.735
0.716
2.906
2.878
4.396
3.733
4.793
Table2. The total forest carbon sink of European Union is 0.286Gt per year.
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4.1 Aims
(i) To develop a methodology, using flux data, models and remote sensing, to be able to estimate the biotic carbon sink of entire countries.
(ii) To apply the methodology to UK, Europe and elsewhere
4.2 Making the framework for running a big-leaf model, and/or SPA and/or
Sheffield DGVM
4.2.1 Computation efficiency
For the big leaf model and SPA, the time step for computation is 0.5 hours (possibly 1 or 2 hours). For SDGVM, the timescales of intrinsic processes are different. The vegetation physiology and biophysics module has a minutes-to-hours time step.
Accordingly, in practice, averaging and interpolation will be used to reduce the computation load. The default timescale of SDGVM output is year, however it can be worked on daily or monthly. When combing the SPA and the SDGVM, efforts must have been made to unify the time scales of the two models. My programming would be running on the unit of high resolution gridded area (0.5
0 
0.5
0
). Therefore, the process of running on European countries and further the whole world would be timeconsuming on an ordinary PC. For example, the European region has over 4000 gridded boxes and the climatological data covers 30 years, which means that program would be running at least 4000 multiple 30 divided by the running time of the SPA times before the results come out. It is approximately estimated that the whole process will take around 75 days. So it might be feasible to choose some typical years (such as the year with the average temperature) instead of whole thirty years to run the SPA.
4.2.2
Prepare the weather generator
Hourly of climatological data are used to run process-based carbon flux models such
SPA and DGVM. However, the available climatological dataset is specified as monthly average. The term “Weather Generator” is applied to stochastic numerical model to generate daily or hourly weather series statistically identical to the observed.
It has two basic functions. One is to convert low resolution data into high resolution data and the other is to identify a method of extending simulations to unobserved regions. LARS-WG, a semi-parametric generator developed by Dr Mikhail Semenov from Rothamsted Research, generates maximum and minimal temperature, precipitation and solar radiation based on the dry/wet day distribution. It is freely available at: http://www.rothamsted.bbsrc.ac.uk/mas-models/larswg.html
. This kind of weather generator will be used to produce daily weather variables and then further convert the daily data into hourly data for SPA model
4.2.3
Calibrates SPA
The calibration of the big leaf model and SPA needs either CO
2
flux data or LAI &
Foliar nitrogen concentrations.
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4.3
Evaluate quality of the Global Land Cover 2000
4.3.1
Make a confusion matrix for UK
A confusion matrix (Kohavi and Provost, 1998) contains information about actual and predicted classifications done by a classification system. Performance of such systems in commonly evaluated using the data in the matrix. This method can be used to compare two kinds of image classifications such as GLC and LCM. The basic idea is, plotting a homogeneous area in one classified image, for example LCM, and then looking at the other image, GLC to see what is the percentage of fitness. Keeping track of all these possible outcomes, evaluations on the two class systems can be made.
4.3.2
Look at the radiance data and critically evaluate the GLC 2000 forest area
GLC classification is based on the FAO class systems. However, since the calculated forest area from GLC dataset is not satisfactory particularly for the UK, it might be possible to look at the raw radiance data. To this purpose, a visit to the European
Space Agency is planned for September. Issues to discuss during this trip are as follows:
(i) Why are there such big differences in the calculated forest areas between
GLC datasets and other data sources (underestimation from GLC)?
(ii) Who did the land cover classification for UK?
(iii) What vegetation index (NDVI, LAI) has been used to make the vegetation classification?
(iv) How to update the classification mapping under the condition of young forests and tree felling?
4.4
Modification of the big leaf model and SPA
4.4.1
Incorporate PRI information into the photosynthetic subroutine of models
Photochemical reflectance index (PRI, (R570-R531)/(R570+R531)) is an indirect indicator on plant photosynthetic performance. In the CTCD project, the seasonal and diurnal variation in PRI and LUE will be measured in spruce. It is expected to show strong seasonal, as well as some diurnal, variation in both PRI and LUE, associated with the effect on low temperature on the light-harvesting complex. Such processes haven’t been incorporated into models of photosynthesis except in an empirical It should be possible to see how LUE declines in stress conditions(drought, cold), and to incorporate this information into the photosynthetic subroutine of SPA and SDGVM.
4.4.2
Evaluate the soil respiration subroutine
Soil respiration subroutine in SPA model simulates the root respiration and microbial decomposition. Compared with the field measurement of soil respiration, the SPA soil functions can be evaluated. At the research site (Harwood forest), the soil respiration has been determined by Zerva(2004) and so it will be possible to parameterize the soil respiration of SPA.
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Loop to cover all cells
4.5 Make and apply models
Start
Cell I
Results of
Predefined
Area call
Big leaf
SPA
SDGVM
output
Result of Cell I
compare
Forest carbon sink
Monthly meteorological data
Daily meteorological data
Hourly
Meteorological
Data
Weather generator
Empirical
Relationship
Other Data Source
(inventory or atmospheric observation)
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The program should start with the first cell, which is 0 .5

0.5 gridded area because the climatological data freely available from CRU, university of East Anglia are half degree resolutions. The climatological monthly variables are converted into daily data through weather generator, and then the daily data further into hourly data through empirical model. These daily data is used to run the big leaf model and/or SPA and/or
SDGVM model. By calling subroutine of big leaf model, SPA and SDGVM, carbon exchange between vegetation and atmosphere on the first cell will be output. These procedure will be repeated until to the last cell. These results will be compared with other data source.
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