Earth System Science RelatedImaging Spectroscopy
Michael SchaepmanRemote Sensing of the EnvironmentJanuary 15, 2009
Outline
BackgroundEarth System ApproachThe Promise of Remote SensingContiguous spectral coverage
BaselineIncreased complexity, scales, and heterogeneityModel inversion based approaches and improvements
OutlookInstrumented approachesChallenges
‘The Dutch Period’
Complexity of Climate Models
“Climate science today is an interdisciplinary synthesis of countless tested and proven physical processes …”
Le Treut, H., R. Somerville, U. Cubasch, Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson and M. Prather, 2007: Historical Overview of Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Scales (Geographic resolution)
“…, many of the key processes that control climate sensitivity or abrupt climate changes (e.g., clouds, vegetation, oceanic convection) depend on very small spatial scales. They cannot be represented in full detail in the context of global models, and scientific understanding of them is still notably incomplete.”
1990
1996
2001
2007
Le Treut, H., R. Somerville, U. Cubasch, Y. Ding, C. Mauritzen, A. Mokssit, T. Peterson and M. Prather, 2007: Historical Overview of Climate Change. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Uncertainties in Carbon Cycle Feedbacks
“The sensitivity of Net Primary Production to climate change is especially uncertain …”
Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da Silva Dias, S.C. Wofsy and X. Zhang, 2007: Couplings Between Changes in the Climate System and Biogeochemistry. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Earth Explorer Missions for the Earth Sciences
Simon, P.C., Hollingsworth, A., Carli, B., Källen, E., Rott, H., Partington, K., Moreno, J., Schaepman, M.E., Mauser, W., Flemming, N.C., Visbeck, M., Vermeersen, B.L.A., Van Dam, T., Reigber, C., Grassl, H., Bougeault, P., England, P., Friis-Christensen, F., Johannessen, J., Kelder, H., Kosuth, P., Pinardi, N., Quegan, S., & Sobrino, J.A. (2006). The Changing Earth. In (ed B. Battrick), Vol. SP-1304, pp. 83. ESA, Noordwijk (NL).
ESA – Call for Ideas ESA – Science Review ESA – Strategies
Schaepman, M.E., Grassl, H., Johannessen, J., Kelder, H., Koike, T., Kosuth, P., Llewellyn-Jones, D., Quegan, S., Rex, M., Sandwell, D., Scholes, B., & Sobrino, J. (2005). ESA-EOEP Science Review Report - Assessment of the Science Benefits derived from the ESA Earth Observation Envelope Programme (EOEP). In (p. 37). Wageningen: Wageningen UR
Trends
Ground based and regional scale (‘airborne’) remote sensing currently undergoes a revival (…necessary for a complete observing system…)
Areas undergoing multiple pressures must be simultaneously addressed using a coherent instrumented approach
Transitional zones (and Ecotones) where pressure between blue (water) –green (vegetation) – red (urbanization) is highest, are where most short to medium term changes are taking place (often induced through human activities)
Increasing awareness of the international agendas on these issues (GEO, EU FP programmes, NRC, etc.)
The Promise of Remote SensingRemote Sensing
Data Acquisition
Spectral / Multi angular / …
Calibration; atmospheric compensation;standardization; etc. resulting product:
BHR (Bi-hemispherical Reflectance)
(spectral Albedo, ‘blue-sky’ Albedo;
BHRiso,λ - white-sky Albedo; DHRλ - black-sky Albedo)
Combinations of inverted canopy/leaf reflectance models; feature fitting based on
physical/empirical models; optimized vegetation indices & RT models; artificial neural networks trained with model runs;
linear/non-linear SMA; spectral matching; etc.
Variables(biochemical, structure, etc.)
LAI, leaf Chlorophyll content; leaf or canopy water content; Bio-indicators: canopy chemistry,
pigment ratio; fractional cover, fAPAR, biomass; etc.
(Selected) Products
Plant functional types, fractional snow cover, gross primary production, yield forecast, vegetation
spatial/temporal growth variability maps; vegetation stress maps (nitrogen deficiency, insect, disease,
dehydration, senescence); vegetation inventory (e.g., forest area, forest type, fragmentation, stem volume);
carbon products (reforestation, afforestation, deforestation); vegetation condition (e.g., health, water
stress, fuel type), etc.
Instruments:Increased accuracy
through advancements in technology
Pre-processing:Increased accuracy
through advancements in terminology, standardization,
calibration, physical models
Analysis Paradigms:Increased accuracy
through remote sensing science innovations
(e.g., models, computational efforts,
etc.)
Variables:Increased accuracy through improved models (empirical,
physical), use of expert systems, and sum of
the instrumented efforts
Products:Increased accuracy
through Earth Observation science
innovation.Increased awareness
and commodity through inter- and
multidisciplinary cooperation,
international programs, and stakeholder
involvement
Imaging Spectrometer Variables/Products
L: HyMap Millingerwaard (NL)R: Unsupervised Classification
Classical Indices
L: Photochemical Reflectance Index (Gamon, 1998)M: MSAVI (Huete, 1988)R: NDVIgreen (Gitelson, 1996)
Moderate Level Products
L: Hydrocarbon Index (1705/1729 nm)M: Clay-Sand-agricultural soil SUMR: Leaf Area Index (based on RSR) (Chen, 2002)
Advanced Products
L: NPP (Ruimy, 1994)M: fAPAR (Gobron, 2002)R: LUE (Nichol, 2002)
Imaging Spectrometers in the Earth Science
True imaging spectrometers image spatially and spectrally in a contiguous fashion Many products can be retrieved using imaging spectrometers simultaneously, while still generating independent productsNew missions concepts based on spectrometers can offer backward compatibility and advanced products at the same time (eg Full Spectral Landsat)
Spectral Absorption Features
Nitrogen/lignin/cellulose all affect the 2.1 and 2.3 μm feature
Cellulose
Lignin
Data: Raymond Kokaly, pers. communication, 2007
‘Senescence’ of a Ficus benjamina L. leaf
Schaepman, M. (2007) SpectrodirectionalRemote Sensing: From Pixels to Processes. International Journal of Applied Earth Observation and Geoinformation, 9, 204-223.
Each time step is 10 mins., total duration 8 hrsMeasurement is reflectance plus reflected transmittance
Undisturbedleaf
Blue-shift
Increase in
Carotenoids
Leaf water loss
Lignin
Cellulose
WaterLeaf and canopy structure
Chlorophyll andother pigments
Protein
‘Killer Application’?
Schläpfer, D., & Schaepman, M.E. (2002). Modeling the noise equivalent radiance requirements of imaging spectrometers based on scientific applications. Applied Optics, 41, 5691-5701
Scales in Remote Sensing
Land surface interactions
Mem
bran
e ar
chite
ctur
e
Cellu
lar
ultr
astr
uctu
re
Com
pone
nt p
ool s
izes
C, N
, P b
udge
ts
Leaf
are
aRe
prod
uctiv
e at
trib
utes
PAR
prof
ileCa
nopy
arc
hite
ctur
e
Habi
tat s
tate
Energy transfer - Photochemistry
Electron transportBiochemistry
Metabolic regulationCO2, O2, H2O exchange
PartitioningPhenology productivity
Vegetation dynamicsCO2 fluxes
Biogeochemicalcycles
Up-/Down-Scaling
Dim
ensi
ons
[µm
]
10-6 –
10-3 –
100 –
103 –
106 –
109 –
1012 –
Leaf RT Model:link leaf (ρ,τ) to absorption of pigments, water, etc.
Canopy RT Model:link BRF to LAI, fCover, 3D structure, biochemistry, background, non-vegetative surfaces, etc.
Atmosphere RT Model:link spectral radiance to scattering & absorption effects of varying aerosols & atm. H2O
LinkedRadiative Transfer Models (RTM)
Time [s]
10-6
–
10-3
–
100
–
103
–
106
–
109
–
1012
–Ec
osys
tem
stat
e
Biom
est
ate
Species competitionSuccession
Processes
Stat
es Scales
Schaepman, M.E., Ustin, S.L., Plaza, A., Painter, T., Verrelst, J., & Liang, S. (2009 (in review)). Earth System Science Related Imaging Spectroscopy - An Assessment. Remote Sensing of Environment
Scaling Example – Three Gorges Region
2004-10-08
2004-10-08
2002-09-01
Spatial extend [m2] (not to scale)
QuickBird 0.6 m EO-1 Hyperion 30 m Landsat TM 30 m MODIS-09 500 m
Longmenhe Study Area
Scaling (Three Gorges Region)
Zeng, Y., Schaepman, M.E., Huang, H.A., De Bruin, S., & Clevers, J.G.P.W. (2008). Comparison of two canopy reflectance models inversion for mapping forest crown closure using imaging spectroscopy. Canadian Journal of Remote Sensing, 34, 235-244
Scaling Example – Three Gorges Region
Zeng, Y., Schaepman, M.E., Wu, B., Clevers, J.G.P.W., & Bregt, A. (2008). Scaling-based forest structural change monitoring using an inverted geometric-optical model in the Three Gorges region of China. Remote Sensing of Environment, 112, 4261-4271
ForestCanopies
Numbered inorder of difficulty:1 – 3 tackled4 – 5 underway
AgricultureCanopies
Closed canopiesNegligible background influence, only shadowing
Sparse canopiesBackground influence throughout, with spectral distinctiveness of background critical
Clumped canopiesBackground influence between crown,with spectral distinctiveness ofbackground critical
2 2 3
Canopy Heterogeneity1 5 4
Schaepman, M.E., Ustin, S.L., Plaza, A., Painter, T., Verrelst, J., & Liang, S. (2008 (in review)). Earth System Science Related Imaging Spectroscopy - An Assessment. Remote Sensing of Environment
Stand Distribution of Cab
Malenovsky, Z., Martin, E., Homolova, L., Gastellu-Etchegory, J.-P., Zurita-Milla, R., Schaepman, M.E., Pokorny, R., Clevers, J.G.P.W., & Cudlin, P. (2008). Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution. Remote Sensing of Environment, 112, 1-18
RGB = NIR,G,B
θv = 48° ϕv = 225°
DART simulated image (116 trees)
Model Inversion in Remote SensingLittle to no well-posed inversion problems exist in remote sensing
Mathematical inversion satisfying the Hadamard criteria lead to ‘non-physical’parameters (little use in remote sensing)Inversion for bio-/geophysical parameters is ill-posed and often ill-conditioned (eg inversion of SAIL/PROSPECT)Parameters retrieved using intermediate solutions (eg RPV inversion / Minnaert’s k) may be further assessed
Inversion quality using radiative transfer models has been improvedUsing higher quality measurements (instrumented approach)‘Optimization’ of the ill-conditioned part
• Recovering a limited number of parameters• Using Bayesian inference• Using expert systems (eg stratification)• Using advanced spectral libraries (estimates of probability density functions (PDF))
Improve representation of all relevant scatterers (eg PV vs NPV, woody elements)
Alternatively use direct radiance data assimilation in process models
Spectral fingerprinting temperate-zone hardwoods
Aim: Mapping multi-annual forest structural and biochemical changes caused by human activity in a complex environment
North-slope of Elburz mountain range (Iran) (topography, directional effects)Significant rainfall (up to 2000mm/yr) and cloud cover (> 90%) limits use of multi-temporal optical data
ApproachSpectral library of leaf/stack and NPV spectra of dominant tree species at various altitude gradients. Forward simulation of the forest.
PV: 102(trees)*2(sunlit/shade)*2(leaf/stack)*2(ad-/abaxial)*3(repeats) = 2448
*50(measurements)= 122’400 spectra
NPV: 102(trees)*2(branch/twig)*6(repeats)= 1224
*50(measurements)= 61’200 spectra
Tree species(n=5)
Sunlit canopy
Shaded canopy
NPV(n=102)
Stack level
(n=102)
Stack level
(n=102)
Leaf level
(n=102)
Abaxial(n=3)
Adaxial(n=3)
Abaxial(n=3)
Adaxial(n=3)
Abaxial(n=3)
Adaxial(n=3)
Abaxial(n=3)
Adaxial(n=3)
Twig(n=6)
Branch(n=6)
Leaf level
(n=102)
Chl analysisN analysisBiomassPositionAltitude
Spectral Fingerprints of Dominant Tree Species
Leaf/Stack Reflectance
Abbasi, M., Schaepman, M.E., Darvishsefat, A.A., Kooistra, L., Marvi Mohajer, M.R., & Sobhani, H. (2009 (in preparation)). Spectral fingerprinting dominant tree species in the Elburz mountain forests of Iran. Remote Sensing of Environment
Minnaert’s k – Wavelength and fCoverFractional cover: 20-30% Fractional cover: 40-50%
Fractional cover: 70-80% Fractional cover: 80-90%
Verrelst, J., Schaepman, M.E., Clevers, J. (2009) Spectrodirectional Minnaert’s k retrieval using CHRIS/PROBA data. EARsel SIG-IS
Remote Sensing and Other Field Work
Remote Sensors performing Field Work
Vegetation Classification
Left: Indicator species mapping supported by DGPSMiddle: Spectral unmixing and fractional abundances (Rubus)Right: Supervised classification (14 classes)
Schaepman, M.E., Wamelink, G.W.W., van Dobben, H., Gloor, M., Schaepman-Strub, G., Kooistra, L., Clevers, J.G.P.W., Schmidt, A., & Berendse, F. (2007). River Floodplain Vegetation Scenario Development using Imaging Spectroscopy and Ecosystem Models. Photogrammetric Engineering and Remote Sensing, 73, 1179-1188
Vegetation mapping data courtesy Karle Sykora, WUR
Instrumented Approach – Laboratory
Plant FacilityLaboratory Facility for Assessing the Plant (Non-) Pigment SystemControlled environment (climate chamber)Solar illuminatorImaging (thermal) and non-imaging (laser, spectroradiometer) measurementsAll measurements multi-angular
Schaepman, M., van Kooten, O., Jalink, H., Schaepman-Strub, G., Snel, J., Clevers, J., Verhoef, W., and Jia, L. (2008) Laboratory Facility for Assessing the Plant (Non-) Pigment System. Accepted proposal, Wageningen University and Research Centre
Instrumented Approach – Field/LaboratoryLAGOS
Laboratory Goniometer SystemSolar Illuminator2 ASD FieldSpec Pro SpectroradiometerChla/Chlb LasersImaging Laser
FIGOSField Goniometer SystemSolar Illumination2 ASD FieldSpec Pro (directional incoming & reflected radiance)Sun-photometer (hemispherical diffuse and direct irradiance)
Dangel, S., Verstraete, M., Schopfer, J., Kneubühler, M., Schaepman, M.E., & Itten, K.I. (2005). Toward a Direct Comparison of Field and Laboratory Goniometer Measurements. IEEE Transactions on Geoscienceand Remote Sensing, 43, 2666-2675
Instrumented Approach – Airborne
APEXAirborne Prism ExperimentAdvanced, programmable and fully calibrated imaging spectrometer (400-2500 nm)Potential future additions
• Directional multispectral photogrammetric camera (Leica ADS-40)• Full waveform LIDAR (Riegl LMS-Q680)
FIRST TEST RESULTS OF THE AIRBORNE DISPERSIVE PUSHBROOM IMAGING SPECTROMETER APEX, K.I. Itten, K. Meuleman, M. Schaepman, E.A. Alberti, B. Bomans, F. Dell'Endice, P. D'Odorico, A. Hueni, J. Nieke, D. Schläpfer, EARSeL, Tel Aviv, 2009
Instrumented Approach – Spaceborne
FLEXFluorescence ExplorerEarth Explorer Mission proposal for the science and research element of ESA’sLiving Planet ProgrammeQuantification of spatial and temporal variations of photosynthetic Carbon uptake of terrestrial vegetationVery high resolution imaging spectrometer (0.1 nm), measuring fluorescence within two oxygen bands, second spectrometer to derive atmosphere and vegetation parameter, and a thermal infrared radiometer for vegetation temperature
FLEX Spectral Coverage
Challenges
Remote sensing products have become expert systemsUsing full dimensionality of long time series (spectral, spatial and temporal)Independence of parameter estimates must be secured
Improvement of radiative transfer based approaches Ongoing discussion of representation of scatterers (eg twigs, bark, understorey)Scattering component separation (PV vs NPV; lignin-cellulose-soil organic carbonates)Physical soil models are still not existing in optical remote sensing
Emerging quality of ecosystem understanding by assessingIntegration of leaf optical properties with photosynthesis modelsSimultaneous Pigment retrieval (Chl a/b, Xantophyll, etc.) and other relevant leaf molecules (Cellulose, Lignin, etc.)Plant Functional Types (many definitions, not only successional stages but pigment based)
Solid coupling of atmospheric chemistry and dynamic vegetationWater masking effectsSurface reflectance (‘where is top-of-canopy?’)
Outlook
Effective coupling of Earth System Science approachesDynamic vegetation and atmospheric chemistry (eg Wet and dry nitrogen deposition)Radiative transfer in coastal zones (water masking effects (atmosphere, ocean, algae, vegetation)
Combined down- and upscalingDominant species distribution changes assessed using potential vegetation maps and continuous fields of actual vegetationThematic disaggregation (regionalization of spatial information)
Vertical and horizontal integration of observational systemsGround observations, SensorNets, close sensing, airborne, spaceborneobservationsPhysical products including uncertainties (eg BRDF and Albedo with associated uncertainties)
The Dutch Period (2003 – 2009)
Traditions I
A typical dialogue at Univ. of Zurich campus:A: “I will be working in a project lead by Alterra in Wageningen.”B: “Excellent, a project at Wageningen University, you can’t decline such an opportunity!”A: “Well, it’s Alterra, not Wageningen University!”B: “Oh, …, I understand, [silence] … Alterra…, is there also an Alterra in Wageningen? Are you sure this is not the University?”
Traditions II
A typical dialogue at WUR campusA: “I am leaving to work for the University of Zurich”B: “Excellent, a job at the ETH, you can’t decline such an opportunity!”A: “Well, it’s the University of Zurich, not ETH”B: “Oh, …, I understand, … [silence] University…, is there also a University in Zurich? Are you sure you want to leave?”
y p p yHendriks Kees Slingerland Kees van Diepen Kees van’t Klooster Klaas Jan Beek Lammert Kooistra Laurens GanseveldLena Elings Leo Stroosnijder Li Jia Lijbert Brussaard Lucia Yanez Lucie Homolova Lukas Grus Maarten Krol Marcel Dicke
Martien Molenaar Martin Kropff Massimo Menenti Mozhgan Abbasi Nikee Groot Olaf van Kooten Paul Opdam Pavel Kabat Peter de Ruiter Phan Minh Tuh Philip Wenting Pim Brascamp Promovendi Ramon Hanssen RaulZurita Raymond Sluiter Rik Leemans Rob Jongman Rogier de Jong Roland van Zoest Rolf de Groot Ron van
Lammeren Ronald Hutjes Sander Mucher Sanne Heijting Silvia Huber Steven de Jong Sytze de Bruin Theo JettenTitia Mulder Tom Veldkamp Truus van de Hoef Tuur Mol Valerie Laurent Vincent van Engelen Wall•e
Hoogendoorn Wies Vullings Willem Takken Willy ten Haaf Wim Cofino Wim van Driel Wout Verhoef Xiaomei Jin Yuan Zeng Zbynek Malenovsky Zhanguo Bai Aldo Bergsma Alfred Stein Allard de Wit Andrew Skidmore Anne Schmidt Antoinette Stoffers Arend Ligtenberg Arnold Bregt Auke de Bruin Ben Gorte Bert Holtslag Bob Su Claudius van
der Vijver David Dent Eddy Moors Frank Berendse Frans Rip Frits Mohren Gabriela Schaepman-StrubGerd Weitkamp Godert van Lynden Harm Bartholomeus Heidi Hamers Hein van Holstijn Hendrik Boogaard Henk JalinkHenk Siepel Herbert Prins Jan Clevers Jan Snel Jeremy Harbinson Jochem Verrelst Joep Crompvoets John Stuiver
Just Vlak Jusuck Koh Karle Sykora Kees Hendriks Kees Slingerland Kees van Diepen Kees van’t Klooster Klaas Jan Beek Lammert Kooistra Laurens Ganseveld Lena Elings Leo Stroosnijder Li Jia Lijbert Brussaard Lucia Yanez LucieHomolova Lukas Grus Maarten Krol Marcel Dicke Martien Molenaar Martin Kropff Massimo Menenti Mozhgan Abbasi Nikee Groot Olaf van Kooten Paul Opdam Pavel Kabat Peter de Ruiter Phan Minh Tuh Philip Wenting Pim
Brascamp Promovendi Ramon Hanssen Raul Zurita Raymond Sluiter Rik Leemans Rob Jongman Rogier de Jong Roland van Zoest Rolf de Groot Ron van Lammeren Ronald Hutjes Sander Mucher Sanne Heijting Silvia HuberSteven de Jong Sytze de Bruin Theo Jetten Titia Mulder Tom Veldkamp Truus van de Hoef Tuur Mol Valerie
Laurent Vincent van Engelen Wall•e Hoogendoorn Wies Vullings Willem Takken Willy ten Haaf Wim Cofino Wim van Driel Wout Verhoef Xiaomei Jin Yuan Zeng Zbynek Malenovsky Zhanguo Bai Aldo Bergsma Alfred Stein Allard de Wit Andrew Skidmore Anne Schmidt Antoinette Stoffers Arend Ligtenberg Arnold Bregt Auke de Bruin Ben
Gorte Bert Holtslag Bob Su Claudius van der Vijver David Dent Eddy Moors Frank Berendse Frans Rip Frits MohrenGabriela Schaepman-Strub Gerd Weitkamp Godert van Lynden Harm Bartholomeus Heidi Hamers Hein van Holstijn Hendrik Boogaard Henk Jalink Henk Siepel Herbert Prins Jan Clevers Jan Snel Jeremy Harbinson Jochem
Verrelst Joep Crompvoets John Stuiver Just Vlak Jusuck Koh Karle Sykora Kees Hendriks Kees Slingerland Kees van Diepen Kees van’t Klooster Klaas Jan Beek Lammert Kooistra Laurens Ganseveld Lena Elings Leo Stroosnijder Li Jia
Lijbert Brussaard Lucia Yanez Lucie Homolova Lukas Grus Maarten Krol Marcel Dicke Martien Molenaar Martin Kropff Massimo Menenti Mozhgan Abbasi Nikee Groot Olaf van Kooten Paul Opdam Pavel Kabat Peter de Ruiter
Phan Minh Tuh Philip Wenting Pim Brascamp Promovendi Ramon Hanssen Raul Zurita Raymond Sluiter Rik Leemans Rob Jongman Rogier de Jong Roland van Zoest Rolf de Groot Ron van Lammeren Ronald Hutjes Sander Mucher Sanne Heijting Silvia Huber Steven de Jong Sytze de Bruin Theo Jetten Titia Mulder Tom Veldkamp
Truus van de Hoef Tuur Mol Valerie Laurent Vincent van Engelen Wall•e Hoogendoorn Wies Vullings Willem Takken Willy ten Haaf Wim Cofino Wim van Driel Wout Verhoef Xiaomei Jin Yuan Zeng Zbynek Malenovsky Zhanguo BaiAldo Bergsma Alfred Stein Allard de Wit Andrew Skidmore Anne Schmidt Antoinette Stoffers Arend Ligtenberg
Special Thanks
Board of Wageningen University and Research Centre, in particular Martin Kropff and Bert Speelman who gave me the unique opportunity working in WageningenESG, its (past) management team of scientific and operational managers, in particular Kees Slingerland and Wallie Hoogendoorn, and Frank, Peter, Wim, and TomCGI and its employees, in particular going back to field work in occasionally harsh environmentsAnd everyone else at WUR and in the NL, having helped us to make our stay in the Netherlands a pleasurable experience during the last 5½ years
Last but not least …
Thank you for your attention!
Wageningen, 2009