Combining remote sensing and ancillary data to monitor the gross

productivity of water-limited forest ecosystems*

Maselli F.1, Papale D.2, Puletti N.3, Chirici G.4, Corona P.2

1 CNR-IBIMET, Firenze, Italy2 University of Tuscia, Viterbo, Italy3University of Firenze, Italy4 University of Molise, Isernia, Italy

*Remote Sensing of Environment 113(2009), 657-667

- - Starting from a Starting from a straightforward forest productivity straightforward forest productivity modelmodel ( (C-fixC-fix, by Veroustraete et al. 1994, 2002, 2004), , by Veroustraete et al. 1994, 2002, 2004), the aim was to develop a procedure of the aim was to develop a procedure of simplesimple implementation (implementation (based on widely available EO databased on widely available EO data) ) and characterized by and characterized by easy repeatabilityeasy repeatability in the in the perspective of perspective of quick monitoring on a national levelquick monitoring on a national level

- - The The C-fixC-fix approach is between approach is between complex process complex process modelsmodels based on a large amount of field based on a large amount of field measurements and measurements and very simple empirical modelsvery simple empirical models merely based on remotely sensed datamerely based on remotely sensed data

Methodology

The work is based on C-fix model, modified

z = number of months = 12i = number of forest and other wooded land categories = 12GPP = Gross Primary Production (gC m-2 year-1)ε = radiation use efficienty (gC/MJ) Tcor= temperature correction factorfAPAR = fraction of the Absorbed Photosyntetic Active RadiationGpar = incoming photosynthetically active radiation (MJ m-2 year-1)

12

1zzparzizcori GfAPARTGPP

Maselli, F., Barbati, A., Chiesi, M., Chirici, G., Corona, P., 2006. Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sensing of Environment 100, 563-575.Chirici, G., Barbati, A., Maselli, F., 2007. Modelling of Italian forest net primary productivity by the integration of remotely sensed and GIS data. Forest Ecology and Management 246, 285-295.

NEP = 35.2 M t C y-1

Gpar*fAPAR*Cws*Tcor*GPP

Water stress indexCws = 0.5 + 0.5 AET/ PET

Where AET and PET are actual and potential evapotranspiration, respectively.For simplicity, AET can be assumed to equal precipitation when this is lower than PET.Consequently, Cws can vary between 0.5 (when strong water shortage reduces photosynthesis to half of its potential value) to 1 (when there is no water shortage and photosynthesis reduction).

08.0025.02450 aveTRs

PET

PET = monthly mean of daily potential evapotranspiration (mm/day);Rs = monthly mean of daily global (total) solar radiation (kJ/m2/day);Ta = monthly mean of daily air temperature (°C).Monthly temperature and radiation maps were used to produce monthly PET maps for the whole study period (1999-2006).

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 1

GP

P/G

PP

Max

RealC-Fix

C-Fix modified

Strain Damage Increasing Water Stress (Time)

Estimation of Gpar

1 - Estimation of theoretical radiation (G0) based on astronomic formulas of Iqbal (1983) using latitude, slope and aspect from DEM

2 - Conversion from theoretical to real radiation to take into account radiation reduction due to cloud coverage (Benincasa et al. 1991)

3 – Comparison to real incoming solar radiation

4 – Conversion of total incoming solar radiation into Gpar

))/(*exp(6735.00

zmzMz

z

TTPAG

G

n = 120 y = 0.982 x r2 = 0.980

0

100

200

300

400

500

0 100 200 300 400 500

Measured Radiation (MJ/m 2/year)

Est

imat

ed R

adia

tio

n (

MJ/

m2/y

ear)

Estimation of fAPAR (fraction of the Absorbed Photosynthetically Active Radiation) for each

month and for each forest category

fAPAR is basically estimated linearly from NDVI (Myneni e Williams 1994, modified by Veroustraete et al. 2002)

fAPARzi = 0.8642 NDVIzi – 0.0814

The relationship is theoretically valid only for pure pixels

To estimate the NDVI value of each forest category the locally calibrated end-member estimation method of Maselli (2001) was applied

For each forest category and for each month a value of fAPAR was mapped

SPOT-VEGETATION NDVI

- Downloading from VITO dekad MVCFrom 1999 to march 2006

- Low-pass filtering

- Monthly maximum value compositing

- Resampling to the project geographic system (UTM 32 N)

Selected forest categoriesMapped in a national development of CORINE database to increase the thematic resolution for forest categories

Mixed categories for the present project were aggregated to pure ones

Original vector data were converted in fuzzy raster format

Fir and spruce

Dec. oaks

Mount. pinesMed. pines

(High) Maquis Evergreen oaksMeso. broadleaves

Higro broadleaves Exotic broadleaves

Beech

Exotic coniferous

Chestnut

Forest monthly GPP images of Italy obtained by the original C-Fix (a), by the modified C-Fix (b), and difference image (c) for August 2003

Forest annual GPP images of Italy obtained by the original C-Fix (a), by the modified C-Fix (b), and difference image (c) for 2002

Monthly GPP measured (full circles) and estimated by C-Fix (asterisks) and modified C-Fix (empty circles) at Castelporziano