about hight temporal resolution

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


Outline

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



Challenges

I Global coverage every few daysI Expectations for land cover change monitoringI Real-time: update the land-cover maps for everynew acquisition



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



Outline

Introduction

Physical models


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



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



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!



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



Outline

Introduction

Physical models


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

(%

)



Purpose

I Or these



Principle



Architecture



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



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


Landsat TMIkonos

FormosatMeris



Results

0

0.2

0.4

0.6

0.8

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


Landsat TMIkonos

FormosatMeris



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



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



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



Example of series

March 14, 2006



Example of series

July 17, 2006



Example of series

November 2, 2006



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)



Simulator architecture



Example of application



Outline

Introduction

Physical models


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



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



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



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)



Outline

Introduction

Physical models


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



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



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about hight temporal resolution

Documents

temporal trajectory

temporal sampling

landcover maps

snow cover

physical processes

physical reality

climate change assessment

optical model