imaging spectroscopy in ecology and the carbon cycle - heading towards new frontiers michael...
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Imaging Spectroscopy in Ecology and the Carbon Cycle - Heading Towards New FrontiersMichael Schaepman, Wageningen University, NL
With contributions from Han van Dobben, Sander Mücher, Wieger Wamelink, Alterra, NLManuel Gloor, Princeton Univ., USAGabriela Schaepman-Strub, WU, NL
Content
1. Introduction2. Imaging spectroscopy based approaches
to ecology3. Challenges4. Outlook5. Discussion
1. Introduction
Introduction
Good ecologists don’t wearcontact 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). Landsat’s Role in Ecological Applications of Remote Sensing. BioScience, 54(6): 535-545.
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). Landsat’s Role in Ecological Applications of Remote Sensing. BioScience, 54(6): 535-545.
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
http://earthobservatory.nasa.gov/Newsroom/NasaNews/2001/200112206806.html
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 (R2=0.77, RMSE=0.54 m2/m2)
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. 177-194
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. http://www.cnr.berkely.edu/~bingxu/pub/hyper_101002.pdf
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. 150-161.
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, 23. 2001 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
http://pubs.usgs.gov/of/2001/ofr-01-0429/results.html
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
CO2 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), 197-201.
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, http://www.maweb.org
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 EuropePercentage of transect area changed between 1950-1990
-3,00%
-2,00%
-1,00%
0,00%
1,00%
2,00%
3,00%
Artificial Surfaces Agricultural areas Forest and seminatural area
Weltands Water bodies
ch
ang
ed
are
a p
er d
ec
ade
Increase
Decrease
Netto
Mucher, S. (2005), BIOPRESS – Linking Pan-European Land Cover Change to Pressures on Biodiversity
Land Cover Changes in EuropePercentage of transect area changed between 1990-2000
-3,00%
-2,00%
-1,00%
0,00%
1,00%
2,00%
3,00%
Artificial Surfaces Agricultural areas Forest and seminatural area
Weltands Water bodies
ch
an
ge
d a
rea
pe
r d
eca
de
Increase
Decrease
Netto
Mucher, S. (2005), BIOPRESS – Linking Pan-European Land Cover Change to Pressures on Biodiversity
Land-Biosphere Models
Land-Biosphere ModelsWhat processes are / should be mapped by land biosphere models ? Carbon Engine
carbon fixed per unit time = F(CO2, 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
CASA BETHY PnET LM3 SMART/SUMO
Carbon Engine
Light use efficiency, PAR,
fPAR
(Farquhar, Ball, Berry) or LUE
Pmax=a+b*N, where N is foliar
Nitrogen
Farquhar, Ball, Berry
C-ass = f(light, N,P,water
availability, temp)
Phenology fPAR ? Predicted fPAR Not relevant (timestep = 1y)
Allocation Globally fixed ratios; leaf, litter,
roots
? Simple allocation rules for tissue
types
Allometries Ratios (root, shoot, leaf) fixed per
vegetation type
Remineralisation
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
Hydrology Bucket type Bucket type One soil layer Bucket type Supplied by external
hydrological model (WATBAL, SWAP)
Discretization PFT’s PFT’s 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
Demography None None None Core of model None (but tree mortality included)
Reference Potter et al., Global Biogeochem. Cycles, 7(4): 811-841, 1993
Knorr, Global Ecology & Biogeography, 9:225-252, 2000
Aber, Oecologica, 92(4): 463-474, 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 ModelsVariable Instrument CASA Model ED Model SMART/
SUMO
fPAR Optical NPP - NPP
LAI Optical - Eval. predicted LAI Eval. predicted LAI
Albedo Optical/Thermal/IS
- Eval. predicted Albedo -
fCover Optical - Eval. predicted fCover -
fLiving/Dead Biomass
Optical - Eval. predicted mortality -
Leaf Chlorophyll
Optical/IS - - -
Leaf Water Optical/IS - ? -
Leaf Dry Matter
Optical/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 Nitrogen IS - - 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): http://geo.arc.nasa.gov/sge/ecocast/data/carbon.html
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 hrsMeasurement is reflectance plus reflected transmittance
Undisturbedleaf
Maximal Spectral Resolution
900850800750700650600550500450400Wavelength [nm]
0.20
0.19
0.18
0.17
0.16
0.15
0.14
0.13
0.12
0.11
0.10
0.09
0.08
0.07
0.06
TO
A a
t sen
sor
radi
ance
[W/(
m2 s
r nm
)]
1.4
1.2
1.0
0.8
0.6
Rat
io []
MERIS TOA bands (incl. FWHM) MERIS TOA radiance (± 1 stdev) ASD ground reflectance modelled to TOA radiance using MODTRAN sun ASD ground reflectance modelled to TOA radiance using Thuillier 2002 sun Ratio of MODTRAN sun to Thuillier 2002 sun
MERIS data:MER_FR__1PNIPA20020822_180221_000000872008_00442_02500_0031.N1
MODTRAN standard solar irradiance vs. Thuillier 2002 solar irradiance
Mean deviation (400 nm - 900 nm): 4.66%
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. 614-623. 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 …!
Thank you for your attention!
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