Satellite observations of terrestrial ecosystems and links to climate and carbon cycleBases of remote sensing of vegetation canopies

The Greening trend

Land use, land use change

Satellite and Models Estimations of GPPAssimilation

Conclusions

Satellite observations of terrestrial ecosystems and links to climate and carbon cycleBases of remote sensing of vegetation canopies

The Greening trend

Land use, land use change

Satellite and Models Estimations of GPPAssimilation

Conclusions

Use remote sensing to measure:Reflectancerelated to chemical, physical properties of surfaceEmittancebrightness temperature (IR part of spectrum)BackscatterFrom active sensor (RADAR or LiDAR light detection and ranging)Related to structure and physical properties of objects on surface

reflectance spectrum of a green leafPigments in green leaves (notably chlorophyll) absorb strongly at red and blue wavelengths. Lack of such absorption at near-infrared wavelengths results in strong scatter from leaves.

Reflectance Ratio is VERY convenient... NDVI = (NIR-RED)/(NIR+RED) is related to greeness.Satellite view of a forest.

MODIS

A) links between satellite reflectance and vegetation parameter

greeness, Leaf Area Index, chlorophyll content, N contentuses Radiative transfer in the canopyB) Canopy structure, leaf properties allow classification of land cover

Satellite observations of terrestrial ecosystems and links to climate and carbon cycleBases of remote sensing of vegetation canopies

The Greening trend

Land use, land use change

Satellite and Models Estimations of GPPAssimilation

Conclusions

1) Can we detect climate change impact on ecosystems ?

2) From space ? At large scale ?1) yes. For example, birds, plants, insect phenology has changed. Spring is earlier by a few days.

2) well seeQuestions :Answers :

Increasechanges in growing season durationchanges in greenness magnitudeIn the north, where vegetation growth is seasonal, the cumulative growing season greenness, which is the area under the NDVI curve, can change either due to a longer photosynthetically active growing season or due to increased greenness magnitude, or both.Assess changes in peak seasonal greenness from July and August average NDVIUse NDVI threshold to assess changes in dates of spring green-up and autumn green- down (assess sensitivity to threshold value)R. Myneni

8.4%/18 yrs (p

From Zhou et al., (JGR, 106(D17):20069-20083, 2001)Analyses of pixel-based persistence indices from GIMMS (v1) NDVI data for the period 1981 to 1999About 61% of the total vegetated area between 40N-70N in Eurasia shows a persistent increase in growing season NDVI over a broad contiguous swath of land from Central Europe through Siberia to the Aldan plateau, where almost 58% (7.3 million km2) is forests and woodlands.North America, in comparison, shows a fragmented pattern of change, notable only in the forests of the southeast and grasslands of the upper Midwest.

longer growing seasons from warming in the northern latitudes possibly explain some of the changes, with a role also for :increased incidences of fires and infestationsfire suppression and forest re-growthchanging harvestsChanges in silviculture forest expansion and re-growth

MognardDo we detect snow (only) ?TRENDS IN SNOW MELT from 1979-1997 SMMR AND SSM/I

From Zhou et al., (JGR, 106(D17):20069-20083, 2001)The temporal changes and continental differences in NDVI are consistent with ground based measurements of temperature, an important determinant of biological activity in the north

Chart7

-1.8783981089-1.3235049496

-1.5404992004-0.2245801165

-1.0892720573-0.4402955525

-0.626225405-1.0806972008

0.4222345825-0.463636622

0.7260654941.1076227916

-0.20489466730.956777341

0.25050406730.6058087434

0.34784120140.5004708

1.12653827440.8177918818

-1.1156921366-1.6485369204

-0.4878676215-0.5680841188

0.14621428070.8951470658

0.4236251130.0014228201

-0.3641104081-1.0576782077

0.7865535702-0.4889859328

1.81415560042.2269521148

1.26281026210.1840212192

NDVI

TEMPERATURE

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fig

820.35359-0.484030.354060.29397820.3806070.014383-1.87839810890.2145490.527825-1.32350494960.3733750.016061-1.20260257770.4251480.395952-0.3312977331

830.358450.096010.352630.35301830.3806070.014383-1.54049920040.2145490.527825-0.22458011650.3733750.016061-1.29163812960.4251480.395952-0.1821887502

840.36494-0.017850.34457-0.00421840.3806070.014383-1.08927205730.2145490.527825-0.44029555250.3733750.016061-1.7934748770.4251480.395952-1.0843688124

850.3716-0.355870.359820.09673850.3806070.014383-0.6262254050.2145490.527825-1.08069720080.3733750.016061-0.84396986490.4251480.395952-0.8294389219

860.38668-0.030170.362320.18676860.3806070.0143830.42223458250.2145490.527825-0.4636366220.3733750.016061-0.68831330550.4251480.395952-0.6020628763

870.391050.799180.35941-0.30561870.3806070.0143830.7260654940.2145490.5278251.10762279160.3733750.016061-0.86949754060.4251480.395952-1.8455721906

880.377660.719560.374610.70381880.3806070.014383-0.20489466730.2145490.5278250.9567773410.3733750.0160610.07689434030.4251480.3959520.7037772255

890.384210.534310.372940.53216890.3806070.0143830.25050406730.2145490.5278250.60580874340.3733750.016061-0.02708424130.4251480.3959520.2702650826

900.385610.478710.389770.76608900.3806070.0143830.34784120140.2145490.5278250.50047080.3733750.0160611.02079571630.4251480.3959520.8610437629

910.396810.64620.384220.86674910.3806070.0143831.12653827440.2145490.5278250.81779188180.3733750.0160610.67523815450.4251480.3959521.1152664969

920.36456-0.655590.36578-0.00698920.3806070.014383-1.11569213660.2145490.527825-1.64853692040.3733750.016061-0.47288462740.4251480.395952-1.0913646099

930.37359-0.08530.36538-0.01574930.3806070.014383-0.48786762150.2145490.527825-0.56808411880.3733750.016061-0.49778967690.4251480.395952-1.1134885037

940.382710.687030.384020.84224940.3806070.0143830.14621428070.2145490.5278250.89514706580.3733750.0160610.66278562980.4251480.3959521.0533903099

950.38670.21530.391280.91953950.3806070.0143830.4236251130.2145490.5278250.00142282010.3733750.0160611.11481227820.4251480.3959521.2485907383

960.37537-0.343720.380110.06683960.3806070.014383-0.36411040810.2145490.527825-1.05767820770.3733750.0160610.41933877090.4251480.395952-0.9049531256

970.39192-0.043550.398290.88085970.3806070.0143830.78655357020.2145490.527825-0.48898593280.3733750.0160611.55127327070.4251480.3959521.1509021296

980.40671.389990.386530.6203980.3806070.0143831.81415560040.2145490.5278252.22695211480.3733750.0160610.81906481540.4251480.3959520.4928678224

990.398770.311680.395010.85619990.3806070.0143831.26281026210.2145490.5278250.18402121920.3733750.0160611.34705186480.4251480.3959521.0886218532

0.38060666670.21454944440.3733750.4251477778

0.01438349590.52782485810.01606055240.3959523348

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Chart6

-1.2026025777-0.3312977331

-1.2916381296-0.1821887502

-1.793474877-1.0843688124

-0.8439698649-0.8294389219

-0.6883133055-0.6020628763

-0.8694975406-1.8455721906

0.07689434030.7037772255

-0.02708424130.2702650826

1.02079571630.8610437629

0.67523815451.1152664969

-0.4728846274-1.0913646099

-0.4977896769-1.1134885037

0.66278562981.0533903099

1.11481227821.2485907383

0.4193387709-0.9049531256

1.55127327071.1509021296

0.81906481540.4928678224

1.34705186481.0886218532

NDVI

TEMPERATURE

ANOMALY

EURASIA (40N~70N)

fig

820.35359-0.484030.354060.29397820.3806070.014383-1.87839810890.2145490.527825-1.32350494960.3733750.016061-1.20260257770.4251480.395952-0.3312977331

830.358450.096010.352630.35301830.3806070.014383-1.54049920040.2145490.527825-0.22458011650.3733750.016061-1.29163812960.4251480.395952-0.1821887502

840.36494-0.017850.34457-0.00421840.3806070.014383-1.08927205730.2145490.527825-0.44029555250.3733750.016061-1.7934748770.4251480.395952-1.0843688124

850.3716-0.355870.359820.09673850.3806070.014383-0.6262254050.2145490.527825-1.08069720080.3733750.016061-0.84396986490.4251480.395952-0.8294389219

860.38668-0.030170.362320.18676860.3806070.0143830.42223458250.2145490.527825-0.4636366220.3733750.016061-0.68831330550.4251480.395952-0.6020628763

870.391050.799180.35941-0.30561870.3806070.0143830.7260654940.2145490.5278251.10762279160.3733750.016061-0.86949754060.4251480.395952-1.8455721906

880.377660.719560.374610.70381880.3806070.014383-0.20489466730.2145490.5278250.9567773410.3733750.0160610.07689434030.4251480.3959520.7037772255

890.384210.534310.372940.53216890.3806070.0143830.25050406730.2145490.5278250.60580874340.3733750.016061-0.02708424130.4251480.3959520.2702650826

900.385610.478710.389770.76608900.3806070.0143830.34784120140.2145490.5278250.50047080.3733750.0160611.02079571630.4251480.3959520.8610437629

910.396810.64620.384220.86674910.3806070.0143831.12653827440.2145490.5278250.81779188180.3733750.0160610.67523815450.4251480.3959521.1152664969

920.36456-0.655590.36578-0.00698920.3806070.014383-1.11569213660.2145490.527825-1.64853692040.3733750.016061-0.47288462740.4251480.395952-1.0913646099

930.37359-0.08530.36538-0.01574930.3806070.014383-0.48786762150.2145490.527825-0.56808411880.3733750.016061-0.49778967690.4251480.395952-1.1134885037

940.382710.687030.384020.84224940.3806070.0143830.14621428070.2145490.5278250.89514706580.3733750.0160610.66278562980.4251480.3959521.0533903099

950.38670.21530.391280.91953950.3806070.0143830.4236251130.2145490.5278250.00142282010.3733750.0160611.11481227820.4251480.3959521.2485907383

960.37537-0.343720.380110.06683960.3806070.014383-0.36411040810.2145490.527825-1.05767820770.3733750.0160610.41933877090.4251480.395952-0.9049531256

970.39192-0.043550.398290.88085970.3806070.0143830.78655357020.2145490.527825-0.48898593280.3733750.0160611.55127327070.4251480.3959521.1509021296

980.40671.389990.386530.6203980.3806070.0143831.81415560040.2145490.5278252.22695211480.3733750.0160610.81906481540.4251480.3959520.4928678224

990.398770.311680.395010.85619990.3806070.0143831.26281026210.2145490.5278250.18402121920.3733750.0160611.34705186480.4251480.3959521.0886218532

0.38060666670.21454944440.3733750.4251477778

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Satellite observations of terrestrial ecosystems and links to climate and carbon cycleBases of remote sensing of vegetation canopies

The Greening trend

Land use, land use change

Satellite and Models Estimations of GPPAssimilation

Conclusions

Classification and land use changeLandsatimage

1975, 1986, 1992 : deforestation, and some forest regrowth

Satellites bring constraints for the past 2 decades.

Satellite observations of terrestrial ecosystems and links to climate and carbon cycleBases of remote sensing of vegetation canopies

The Greening trend

Land use, land use change

Satellite and Models Estimations of GPPAssimilation

Conclusions

Modelling ecosystems function with satellite dataData as an input Data assimilation Estimation of photosynthesis from fPARAssimilation of AVHRR data in a vegetation/SVAT model

GPP= LUE.*fPAR*PARLUE is estimated with experimental values NDVItemporal and spatial variabilityAPAR (mol.m-2d-1) ERA40Photosynthesis from remote sensing and weather data

Trend in photosynthesis from 1982 to 1999R. Nemanni et al. 2003

Data assimilationSatellite data are radiative only

Biophysical parameters (fPAR) are derived from inverse techniquese.g. What Leaf Area Index gives such reflectances ?

Assimilation extends such inverse technique to the whole vegetation model.- Look for the best agreement between model and data by correcting errors (variable, parameters)- benefit from the model knowledge- requires good knowledge of errors !!!

ModelFluxes before and after assimilation of AVHRR dataCayrol et al. (2000)

Some simple concluding remarks

We are in a data rich period for Earth Observation

Archives are rare but already show signs of climate change global impact.

Some are free ! Use (with caution...)

Networks allow calibration/validation of satellite products

Lots to be done !

Dont forget theres a bonfire tonight.Hopefully, it will not be large enough to be detected !Celebrate Heveliusfamous polish astronomer and famous beer-maker too !