Imaging Spectroscopy in Ecology and the Carbon Cycle - Heading Towards New Frontiers Michael Schaepman, Wageningen University, NL With contributions from Han van Dobben, Sander Mücher, Wieger Wamelink, Alterra, NL Manuel Gloor, Princeton Univ., USA Gabriela Schaepman-Strub, WU, NL

Content Introduction Imaging spectroscopy based approaches to ecology Challenges Outlook Discussion

1. Introduction Introduction

Good ecologists dont wear contact lenses and always a raincoat

State and Temporal Dynamics of Ecosystems Thematic classification Wildlife habitat, successional stage, forest fragmentation, invasive species, biodiversity Biophysics LAI, specific leaf area, biomass, canopy moisture content, canopy cover Temporal dynamics Greening, senescence, forest change detection, windfall Spatial patterns Spatial metrics, patches, shapes for disturbed forests Large area mapping Stratifying forest at national level, GlobCover program Cohen, W.B., and Goward, S.N. (2004). Landsats Role in Ecological Applications of Remote Sensing. BioScience, 54(6):

Integration with Ecological Models Physiological models Integration of multitemporal data in to physiologically based process models (surface energy balance, evapotranspiration, productivity, Light Use Efficiency (LUE) to derive NPP) Accounting models Carbon accounting models (estimation of biomass change over time using RS) Habitat assessments Habitat characterization and mapping Socioeconomic studies Landscape ownership patterns, visualization of ecological consequences Cohen, W.B., and Goward, S.N. (2004). Landsats Role in Ecological Applications of Remote Sensing. BioScience, 54(6):

2. Imaging Spectroscopy Based Approaches Imaging spectroscopy based approaches to ecology Classification Examples

Imaging Spectroscopy Approaches Continuous fields Quantitative, physical methods Improvement path clear, long development time Quantitative, statistical methods Effective, fast, no cause-effect relationship Categorical variables Classification based approaches Discrete classes Base maps Orientation and visualisation

Example I Continuous Fields – Quantitative Physical Based Method Product: NASA MODIS Product No. 15 (MOD15) Leaf Area Index (LAI) Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Radiative transfer based approach derived from reflectances and ancillary data (land cover type, background, etc.)

Continuous Fields – Physical Based

Example II Continuous Fields – Quantitative Statistical Based Method Product: (Effective) Leaf Area Index (LAI) derived from HyMAp data Correlation based approach, where a Perpendicular Vegetation Index (PVI) is correlated with LAI (R 2 =0.77, RMSE=0.54 m 2 /m 2 )

Continuous Fields – Statistical Based Schlerf, M, C. Atzberger and J. Hill (2005), Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment, Vol. 95(2), p

Example III Categorical Variables – Classification Based Product: 18 land use/cover classes derived from EO-1 ALI and Hyperion Two level classification scheme starting with 10 classes in first level and 18 classes in second level using segmented linear discriminant analysis (segLDA).

Categorical Variables – Classification Based Xu, B., and Gong, P. (2002). Land use/cover classification with multispectral and hyperspectral EO-1 data: a comparison.

Example IV Categorical Variables – Discrete Classes Product: Invasive specie map of iceplant (Carpobrotus edulis) expressed in different density levels. Continuum removal based approach defining presence/absence of Iceplant in combination with MNF. Discretization of results is based on land managers requirements for detection of invasion fronts.

Categorical Variables – Discrete Classes Underwood, E., Ustin, S., and DiPietro, D. (2003). Mapping nonnative plants using hyperspectral imagery, Remote Sensing of Environment, Vol. 86(2), p

Example V Base Map Product: WTC debris analysis by mapping levels of Chrysotile (asbestiform minerals, potential Asbestos indicators) AVIRIS data recorded on September 16, 18, 22, over the WTC area. Base maps used for identification of affected sites and determination of ground sampling analysis. Subsequent SUM approach of AVIRIS data.

Base Map

3. Challenges Climate scenarios vs. vegetation scenarios Global Land Use/Cover Monitoring Land-Biosphere Models Validation Biochemistry There are many more! Integration of multiple sensors Scaling issues etc.

Climate Scenarios vs. Vegetation Scenarios IPCC climate change predictions are only driven by climate scenarios: Currently DGVMs do not consider (at all/well enough) the inter-/intra-year variability of vegetation Vegetation can change at much faster rates due to disturbance, rather than due to climate change To better explain the uncertainty in the land sink/source domain, vegetation scenarios (analog to climate scenarios) should be developed accounting for disturbance

CO 2 and Climate: Model Divergence Source: M. Rast, Ed., SPECTRA – Surface Processes and Ecosystem Changes Through Response Analysis, ESA SP-1279(2), 2004, pp. 66; Data: IPCC (2001) Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., 881pp.

Global Net Carbon Balance Source: M. Rast, Ed., SPECTRA – Surface Processes and Ecosystem Changes Through Response Analysis, ESA SP-1279(2), 2004, pp. 66; Data: Joos, F., G.-K. Plattner, T.F. Stocker, A. Körtzinger, and D.W.R. Wallace, 2003: Trends in Marine Dissolved Oxygen: Implications for Ocean Circulation Changes and the Carbon Budget, EOS, 84 (21), Bars indicate a decade each Uncertainties are in black

Biomass Divergence due to Management c.f. contribution of Schmidt, et al. (2005), and Kooistra et al. (2005) for details (also this EARSeL workshop)

Global Land Use/Cover Monitoring

Millennium Ecosystem Assessment Conclusions: What are the most important uncertainties hindering decision making concerning ecosystems? There are major gaps in global and national monitoring systems that result in the absence of well-documented, comparable, time- series information for many ecosystem features and that pose significant barriers in assessing conditions and trends in ecosystem services. Moreover, in a number of cases, including hydrological systems, the condition of the monitoring systems that do exist is declining. Although for 30 years remote sensing capacity has been available that could enable rigorous global monitoring of land cover change, financial resources have not been available to process this information, and thus accurate measurements of land cover change are only available on a case study basis. MA Board (2005). Millennium Ecosystem Assessment Synthesis Report, Reid, W.V., (ed.), p. 219,

Existing and Planned Land Cover Maps Mucher, S., and Schaepman, M. (2005). Inventory of regional, national and global land cover maps produced using satellite data. Unpublished data.

Land Cover Changes in Europe Mucher, S. (2005), BIOPRESS – Linking Pan-European Land Cover Change to Pressures on Biodiversity

Land Cover Changes in Europe Mucher, S. (2005), BIOPRESS – Linking Pan-European Land Cover Change to Pressures on Biodiversity

Land-Biosphere Models

What processes are / should be mapped by land biosphere models ? Carbon Engine carbon fixed per unit time = F(CO 2, light, water availability, temperature, nutrients) Carbon allocation distribution of C fixed to different living tissue (e.g. stems or roots) = F(geometry, physiology,plant functional type, species) Remineralisation carbon flow to nonliving forms and decomposition (fast and slow soil pools) = F(plant functional type, physiology, microbiology, molecular structure (e.g. lignin vs. waxes or cellulose)) Hydrology soil water balance, (root depths) Population dynamics early versus late successional species through competition for resources (light, nutrients, water) = F(stand height, stand age, physiology) disturbance: humans (land use), fire, windfall, insects = F(climate, humans) Gloor, M. (2005), SPECTRA Simulator – Final Report, ESA

CASABETHYPnETLM3SMART/SUMO Carbon EngineLight use efficiency, PAR, fPAR (Farquhar, Ball, Berry) or LUE Pmax=a+b*N, where N is foliar Nitrogen Farquhar, Ball, BerryC-ass = f(light, N,P,water availability, temp) PhenologyfPAR?PredictedfPARNot relevant (timestep = 1y) AllocationGlobally fixed ratios; leaf, litter, roots ?Simple allocation rules for tissue types AllometriesRatios (root, shoot, leaf) fixed per vegetation type Remineralisatio n 5 litter, 2 organic pools, first order decay ?No soil carbon component Fast and slow pools of C and N Litter + 2 organic pools, fixed ratio + 1st order decay HydrologyBucket type One soil layerBucket typeSupplied by external hydrological model (WATBAL, SWAP) DiscretizationPFTs Biomass produced only by tissue type (foliage) Defined by mortality and fecundity functions (species build a continuum) 5 FT's that compete for light, N, P, water DemographyNone Core of modelNone (but tree mortality included) ReferencePotter et al., Global Biogeochem. Cycles, 7(4): , 1993 Knorr, Global Ecology & Biogeography, 9: , 2000 Aber, Oecologica, 92(4): , 1992 Carbon Mitigation Initiative, Princeton Univ., 2005 Wamelink & al. in prep., 2005 Comparison of 5 Different Land-Biosphere Models Gloor, M. (2005), van Dobben, H. (2005), pers. comm.

RS Derived Variables and Models VariableInstrumentCASA ModelED ModelSMART/SUMO fPAROpticalNPP- LAIOptical-Eval. predicted LAI AlbedoOptical/Thermal/I S -Eval. predicted Albedo- fCoverOptical-Eval. predicted fCover- fLiving/Dead Biomass Optical-Eval. predicted mortality- Leaf ChlorophyllOptical/IS--- Leaf WaterOptical/IS-?- Leaf Dry MatterOptical/IS-Eval. leaf dry matter- Foliage Temperature Thermal-Important for eval. of water loss during photosynthesis Eval. potential evapotransp. Soil Temperature Thermal-Water stress eval.Water stress eval Leaf NitrogenIS--Eval predicted leaf N Gloor, M. (2005), van Dobben, H. (2005), pers. comm.

NPP Controls Geographic Variation in Climatic Controls of Terrestrial Net Primary Production (Nemani, 2003)

NPP Change Nemani (2003): Trends in global net primary production (NPP) anomalies from 1981 through 1999, computed from historical AVHRR-NDVI data

Ecocasting Ecological forecasting (or 'ecocasting') is the prediction of ecosystem parameters. NASA Ames is developing advanced computing technologies for converting massive streams of satellite remote sensing data into ecocasts that are easy to read and use. Nemani, R. (2005):

Validation Geospatial interpolation Validation of products Representativeness of sites

Geospatial Interpolation for Assimilation

BELMANIP Baret, F. et al. (2005), Validation and inter-comparison of medium resolution LAI and fPAR products derived from MODIS, MERIS and Vegetation, EGU, Geophys. Res. Abstracts, Vol. 7, 01765

Biochemistry The biochemistry has gotten a bit lost over the past few years – it must come back in the future!

Biochemicals Present in Vegetation Spectra Source: Schaepman, M. (2005): Spectrodirectional Remote Sensing – From Pixels to Processes, Inaugural Address, Wageningen.

Decay of a Ficus benjamina L. Leaf Source: Bartholomeus, H., and Schaepman M. (2004) Decay of Ficus benjamina L. in 10 minutes steps over 8 hrs, unpublished Each time step is 10 mins., total duration 8 hrs Measurement is reflectance plus reflected transmittance Undisturbed leaf

Maximal Spectral Resolution Source: Kneubühler, M., Schaepman, M.E., Thome, K.J., & Schläpfer, D.R. (2003) MERIS/ENVISAT vicarious calibration over land. In Sensors, Systems, and Next-Generation Satellites VII, Vol. 5234, pp SPIE, Barcelona.

4. Outlook Imaging spectroscopy has deepened the physical understanding of the interaction of photons with matter Certain links between remote sensing and ecology have been significantly advanced because of advances in imaging spectroscopy Imaging spectroscopy will never solve the challenges alone – integrated solutions will be the future trend Nevertheless, we need to make sure imaging spectroscopy will soon be a commodity and not remain a fancy technology in the shade of …!

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