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