Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation Conclusions

Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation Conclusions

Use remote sensing to measure: –Reflectance related to chemical, physical properties of surface –Emittance brightness temperature (IR part of spectrum) –Backscatter From active sensor (RADAR or LiDAR – light detection and ranging) Related to structure and physical properties of objects on surface

reflectance spectrum of a green leaf upper epidermis palisade layer spongy tissue lower epidermis image credit: Govaerts. Pigments 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 content uses Radiative transfer in the canopy B) Canopy structure, leaf properties allow classification of land cover

Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation 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) we ’ll see Questions : Answers :

JanDecJul Aug earlier spring delaye d fall JanDecJul Aug Increase changes in growing season duration changes in greenness magnitude In 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 NDVI Use 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<0.05) 12.4%/18 yrs (p<0.05) 11.9 days/18 yrs (p<0.05) 17.5 days/18 yrs (p<0.05) Analysis of GIMMS (v1) ndvi data for the period 1981 to 1999 A larger increase a longer active growing season are observed in Eurasia relative to North America From Zhou et al., (JGR, 106(D17): , 2001) NDVI averaged over boreal growing season months increased by about 10%, the timing of spring green-up advanced by about 6 days.

From Zhou et al., (JGR, 106(D17): , 2001) Analyses of pixel-based persistence indices from GIMMS (v1) NDVI data for the period 1981 to 1999 About 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 infestations fire suppression and forest re-growth changing harvests Changes in silvicultureforest expansion and re-growth

Mognard Do we detect snow (only) ? TRENDS IN SNOW MELT from SMMR AND SSM/I

From Zhou et al., (JGR, 106(D17): , 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

Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation Conclusions

Classification and land use change Landsat image

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 cycle Bases of remote sensing of vegetation canopies The Greening trend Land use, land use change Satellite and Models Estimations of GPP Assimilation Conclusions

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

GPP= LUE.*fPAR*PAR LUE is estimated with experimental values NDVI temporal and spatial variability APAR (mol.m -2 d -1 ) ERA40 Photosynthesis from remote sensing and weather data

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

Data assimilation Satellite data are radiative only Biophysical parameters (fPAR) are derived from inverse techniques e.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 !!!

Model Fluxes before and after assimilation of AVHRR data Cayrol 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 !

Don’t forget there’s a bonfire tonight. Hopefully, it will not be large enough to be detected ! Celebrate Hevelius famous polish astronomer … and famous « beer-maker » too !