SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Application

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006

HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions

Application of Hyperspectral Data

Bio-sciences

Lammert Kooistra and Michael Schaepman Wageningen University



Introduction



Introduction

• Bio-science applications originating from imaging spectrometer data products are in most cases indirectly derived and require the use of ‘models’ (e.g., radiances – PRI – LUE – DVM – Biodiversity).

• Directly derived bio-science applications from imaging spectrometer data are sparse (e.g., LUCC) or often site specific.

• Wageningen UR (CGI) is currently focusing on the integration of imaging spectrometer data derived products into dynamic vegetation models.



Applications I

• Variables– Mostly being used as input for a model that needs to

produce a spatially explicit output• Parameters

– Mostly being used to constrain a model or other parameters (not too much relevance for the bio-domain)

• Applications– Higher level product the involves the use of

{statistical, physical} models• Products

– Can be any of the above



Applications II

• Relevant Bio-Science Variables– At-sensor radiance– Surface reflectance– (Spectral) Albedo– fAPAR– fCover / gap fraction– LAI– Leaf/canopy pigments (Chlorophyll, Xantophyll, Cellulose, etc.)– Leaf/canopy water– Leaf/canopy dry matter– Foliage temperature– Soil temperature– fLiving/fDead biomass (litter) / SOC



Applications III

• Relevant Bio-Science Products– Albedo– Efficiency (Light Use, Water Use, Rain Use)– LUCC, VCC– GPP, aNPP, NPP– Biodiversity– Ecosystem, habitat, species distribution– Crop growth and yield estimation – Plant stress (nitrogen; water)– Forest inventories (e.g., forest area, forest type, fragmentation, biomass, stem

volume, crown diameter)– Carbon sequestration (reforestation, afforestation, deforestation)– Ecosystem resilience– Ecosystem services– Fire (health, water stress, fuel type, activation energy)



Application IV

• Imaging spectroscopy measures radiance (with spatial (2D), spectral, temporal, directional (2D), and polarization dependencies)

• However the reflectance of a canopy is a function of its geometry, structure, biochemistry, and geochemistry.

• We employ mainly quantitative statistical or physical models (or a combination of both) to bridge the gap that imaging spectroscopy cannot measure any of the canopy parameters directly (sometimes this is (erroneously) referred to as being the ill-posed problem)



Example I

fAPAR PRI ANPP (LUE approach)MJm-2day-1

Regional estimates of aboveground Net Primary Productivity (aNPP) for a river floodplain

Issues:- Regional scale ecosystem modeling- DVM initialisation, calibration and validation- Scenario development including human impact Aduaka, U. et al., 2006



Example IISpecies abundance maps

fAPAR PRI

Issues:- Level of detail increases with increasing spatial

resolution- Many RTM’s are sensitive to the shadow fraction- Parameterizations of models need to account

horizontal competition

Spatial abundance map for Rubus caesius based on combined approach of SMA and radiative transfer modelling

PFT1: Grazed Grassland PFT2: mixed herb

Liras, E. et al., 2005



Example III Field / Laboratory Observations

DART 3D spruce „mock-up“

Laboratory measurement of the needle optical properties

Radiative Transfer Modelling (DART)

Spatial (3D) measurement of the tree structural parameters

RGB = NIR,G,B

RGB = NIR,G,B

v = 48° v = 225°

NADIR simulated forest

OFF-NADIR simulated forest stand

Malenovsky, Z. et al., 2006



Example IV

Three Gorges Region,China

EO-1 Hyperion data

Zheng, Y. et al., 2006

Issues:-Bridging scaling gaps from local to regional-Combined physical and statistical model calibration-Assessing ecosystem services



Example V

l

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Seasonal Patterns - Fused images

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0.3

0.4

0.5

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0.8

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J an March April May J une J uly Aug Sept Nov Dec

Time of the year

ND

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valu

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cerrado

eucalyptus

pasture

"new" cerrado

Acerbi, F. et al., 2006



Example VI Sub-pixel Land Cover mapping with

MERIS

Identification of

endmembers

Sub-pixel accuracy

Reference dataset (LGN)

Zurita-Milla, R. et al., 2006

Issues:-Requirement to map LUCC at high spatial resolution-Vegetation Cover Conversion (state vectors)-Phenology



Developments I

• Bridging scaling gaps will be come more relevant (genetics – molecules – leaves – plants – canopies – ecosystems)





Developments II

• In-situ networks (SensorWeb), data assimilation and applied optimal estimation methods will further constrain degrees of freedom





Developments III

• In coupled human-environment systems monitoring of transitional zones (ecotones – habitat, ecosystem boundaries) deserve more attention





Developments IV

• Holistic views striving to describe the Earth System better in all relevant aspects will result in more detailed spectroscopic analysis







Developments V

• 3-D radiative transfer approaches in partly cloudy atmospheres





Developments VI

• Biochemical applications concentrate on the retrieval of moisture content, C, N, and (potentially) P cycles





Developments VII

• Coupled systems (soil-vegetation-atmosphere transfer (SVAT)) must emphasize on the soil component



Discussion Points

• Imaging spectroscopy of vegetation is one of the most challenging applications in remote sensing due to the multitude of simultaneously influencing factors and that none of the measurements is a direct measurement

• Semantic interoperability is the (unexplored) link between remote sensing and vegetation research (PFT, Albedo, reflectance, etc.)

• In characterizing the SVAT (soil-vegetation-atmosphere-transfer) scheme, the S remains the least explored so far (no parametric soil model avaiable)



Discussion Points

• Spectral band redundancy discussions should be replaced with full spectral coverage discussions, making use of the contiguity criterion of spectral measurements

• Spectroscopy has most significantly advanced the understanding of interactions of photons with vegetation. We are looking forward for photon-matter interactions.

• Spectroscopy alone will not be able to solve current issues to the full extend: we need phenology (time series), ground measurements (data assimilation), and other technologies (fluorescence, SAR, LIDAR, etc.) to complement spectroscopy

Documents

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Application