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Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
About High Temporal Resolution
Jordi Inglada
CNES/CESBIO
09-11-2010
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 1 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Outline
Introduction
Physical models
Multi-temporal simulator
Land-cover change
Conclusion
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 2 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
New sensors
I VenusI Sentinel (1,2)I LDCM
I New applications . . .. . . which require to closely monitor the temporaltrajectory of the characteristics of land surfaces.
I real time classificationI evolving nomenclatures
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 3 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Challenges
I Global coverage every few daysI Expectations for land cover change monitoringI Real-time: update the land-cover maps for everynew acquisition
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 4 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Approaches
I It's not about methods but aboutneeds/applications
I spatio-temporal trajectories of clusters in akernelized feature space are cool . . .
I but a hard threshold on NDVI can sometimes work
I Many scientists have developed models for thephysical processes
I Some are easy to use; some are complexI Some can be spatialized; some can'tI Many are Open Source (more on this later)
I Expert knowledgeI i.e. agricultural practices
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 5 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Outline
Introduction
Physical models
Multi-temporal simulator
Land-cover change
Conclusion
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 6 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Essential Climate VariablesI For climate change assessment, mitigation andadaptation:
I River discharge,I Water use,I Groundwater,I Lakes,I Snow cover,I Glaciers and ice caps,I Permafrost,I Albedo,I Land cover (including vegetation type),I Fraction of absorbed photosynthetically activeradiation (FAPAR),
I Leaf area index (LAI),I Above-ground biomass,I Fire disturbance
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 7 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Models
I Areas of interest:I hydrology, agriculture, forestry,
I Media:I Aerial, terrestrial, aquatic, mixed
I How to find the good balanceI complexity,I number of input parameters and variables,I computational cost
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 8 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Models
I They describe the physical realityI Their assumptions/simplifications are clearI Naturally use/need ancillary data (meteo, groundmeasures)
I They can be multi-sensor or better . . .. . . Sensor Agnostic
I benefit from the synergy between sensorsI increase temporal sampling!
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 9 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Open source models - some examplesI Prospect: optical model for estimating leaf-levelreflectance and transmittance
I Sail: canopy reflectance modelI Daisy: mechanistic simulation model of the physicaland biological processes in an agricultural field
I 6s: a basic RT code used for calculation oflook-up tables in the MODIS atmosphericcorrection algorithm
I Arts: radiative transfer model for the millimeterand sub-millimeter spectral range.
I etc.I have a look at ecobas.org
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 10 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Outline
Introduction
Physical models
Multi-temporal simulator
Land-cover change
Conclusion
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 11 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Purpose
Which is the best sensor to recognize these:
60
40
20
0
0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 2,6
sol nu sec
végétation
eau
visible proche infrarouge moyen infrarouge
Longueur d'onde (µm)
Ré
fle
cta
nce
(%
)
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 12 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Purpose
I Or these
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 13 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Principle
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 14 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Architecture
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 15 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results
0
0.2
0.4
0.6
0.8
1
Vegetation
Soils
Man-m
ade
Minerals
Acc
urac
y
Spot 5QuickbirdPleiades
Landsat TMIkonos
FormosatMeris
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 16 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results
0
0.2
0.4
0.6
0.8
1
RoadConcretes
Constructions
RoofIgneous
Metam
orphic
Sedimentary
Alfisol
Aridisol
Entisol
Inceptisol
Mollisol
Acc
urac
y
Spot 5QuickbirdPleiades
Landsat TMIkonos
FormosatMeris
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 17 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results
0
0.2
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1
brownc omps hr f
dkg ray
c omps hr f
ltg ray
c omps hr f
orangg ray
c omps hr f
grayg ravelr f
redg ravelr f
brownm etalr f
ltg ray
m etalr f
ltg ray
a sphaltlr f
redt iler f
browng ray
t iler f
tarr f
woods hingle
r f
greenv eg
npvbare
s oil
openw ater
swimp ool
lta sphalt
r d
dka sphalt
r d
concreter d
gravelr d
parkingl ot
railroadt rack
tennisc ourt
reds port
t artan
Acc
urac
y
Spot 5QuickbirdPleiades
Landsat TMIkonos
FormosatMeris
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 18 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results
0
0.2
0.4
0.6
0.8
1
Man-m
ade
Igneous
Metam
orphic
Sedimentary
Soils
Acc
urac
y
Spot 5Pleiades
Pleiades+MIRSpot5-MIR
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 19 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
But we said HTR . . .
I How to simulate a multi-t mission?I Venus, Sentinel-2
I Realistic temporal evolutionsI Use existing image time series
I Formosat-2I 8 m., 4 bands (B,V,R,NIR), 3 days
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 20 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Spectral bands
500 1000 1500 2000wavelength
0.0
0.2
0.4
0.6
0.8
1.0Fo
rmos
at-2
Relative Spectral Responses
500 1000 1500 2000wavelength
0.0
0.2
0.4
0.6
0.8
1.0
Venu
s
500 1000 1500 2000wavelength
0.0
0.2
0.4
0.6
0.8
1.0
Sent
inel
-2
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 21 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
March 14, 2006
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 22 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
July 17, 2006
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 23 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of series
November 2, 2006
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 24 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Available data
I 49 images in 2006I Orthorectification OKI Radiometric corrections OK
I TOC and aerosol corrections
I Cloud screening
I Land-cover map availableI Leaf pigments data base for several vegetationtypes (LOPEX'93)
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 25 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Simulator architecture
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 26 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Example of application
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 27 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Outline
Introduction
Physical models
Multi-temporal simulator
Land-cover change
Conclusion
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 28 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Soil work
I Main goal: improve real-time crop classification; soilwork can give hints on the type of crop
I Soil map: is also interesting in itself as a product
Inter-crop Stubble disking Deep ploughing
Harrowing Sowing preparation Emergence
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 29 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
ApproachI Radiometry only: only the reflectances andcombinations of them (indexes) are used; notexture, statistics, nor object-based features.
Index Formula
NDVI NIR−RNIR+R
Color R−BR
Brightness√G 2 + R2 + NIR2
Shape 2R−G−BG−B
Redness R−VR+V
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 30 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Approach
I Statistical analysis: the temporal evolution of thereflectances and the indexes (globally and perclass) are studied.
I 2 kinds of analysis:I Identification of the soil state: classificationI Identification of the transitions between states:change detection
I SVM classification: both used as separabilitymeasure and as classification tool
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 31 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Results for change detection
Transition D→H H→SP H→E SP→EAccuracy (%) 97.0 88.74 87.91 96.76
I The number of transitions is very low for somecases (between 12 and 50 plots; or between 1kand 10k pixels)
I Many transitions between states can't bedetected accurately
I However, some changes are well detected (about90% and more)
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 32 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Outline
Introduction
Physical models
Multi-temporal simulator
Land-cover change
Conclusion
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 33 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
What we've got
I Source code available for many simulatorsI Ongoing work for
I Prospect, Sail & Daisy integrationI new hyper/multi- spectral/temporal algorithmintegration
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 34 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
What we need
Engineering - Development
I Improve image simulation: MTF, realistic landscapesI Hide physical models under common interfaces
Research
I Learn to select the best model set for a givenproblem
I Incorporate domain expert knowledge
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 35 / 36
Introduction Physical models Multi-temporal simulator Land-cover change Conclusion
Creative Commons Attribution-ShareAlike 3.0 Unported License
Jordi Inglada (CNES/CESBIO) About High Temporal Resolution 09-11-2010 36 / 36