assimilation of high resolution satellite imagery into the 3d-cmcc forest ecosystem model

Assimilation of high resolution satellite imagery into the

3D-CMCC forestecosystem model

S. Natali (1), A. Collalti (2,3), A. Candini (4), A. Della Vecchia (5), R. Valentini (2,3)

(1) SISTEMA GmbH, Vienna, Austria

(2) CMCC-EuroMediterranean Centre for Climate Changes-IAFENT division, Lecce, Italy

(3) DIBAF Institute, University of Tuscia, Viterbo, Italy

(4) MEEO Srl, Ferrara, Italy

(5) European Space Agency - ESRIN, Frascati, Italy

summary

• Context• Proposed approach• Conclusions

25.04.2012 BG2.8 - EGU 2012, Vienna, Austria 2

Contexts

• Why the project has been carried out• End users critical requirements, and proposed

solution


Context ConclusionsApproach


The ESA KLAUS Project

This work has been carried out in the framework of the ESA KLAUS Project.

A core activity of the project is the demonstration of the usability of the KEO environment by end users, by the development of applications derived from user requirements. Besides requirements definition, users are involved in the applications validation

Moreover, ESA wants to increase the use of satellite data in specific thematic areas:

KDA1 Forest biomass estimation

KDA2 Hydrogeological Risk

KDA3 Fires and burned areas detection

KDA4 Solar Irradiance monitoring


Motivations User Requirements


End users, requirements, and proposed solution

Driver Need of carbon stock estimation for public / private entities (reporting, carbon credit market)

Critical User Requirements Estimation of BIOMASS changes on a middle and large scale (Regional and National scale) Use of as less as possible on ground surveys Based especially on satellites surveys Provision Images at a spatial resolution of 10 metres or less Provision of seasonal estimation of biomass

State of the Art limited modeling capability / limited use of satellite data

Proposed Solution Forest Ecosystem Model (Multi-layer, Multi-age, Multi-species, forest management

simulation) provided/developed by University of Viterbo, department of Forest and Ecology, and CMCC euroMediterranean center for Climate Changes integrated with satellite data


Motivations User Requirements

Approach

• Selected forest ecosystem model• Selected integration environment (satellite data

assimilation schema)• 3D-CMCC-SAT application




The 3D-CMCC Model

Context ConclusionsApproachForest Model ApplicationAssimilation

(0,0,0)

(1,0,0)

(0,0,1)

(0,0,2)

3D-CMCC Forest Model (Collalti et al, in prep.) is a light use efficiency model (LUE) that permits to simulate in “natural” forests composed by variable number of species, layers and cohorts :

• CO2 fluxes (GPP) • Biomass production (NPP)• Carbon stock dynamic• Forest structure dynamic• Natural renovation• Mortality• Light and Water competition• Mean annual volume increment• Current annual volume increment• …

Multi-layer (tridimensional)Multi-species

Multi-ageDynamic

Hybrid (HMs)Monthly time-step

Spatially explicit / implicitRegional scale (cell size: 100m x 100m)


The 3D-CMCC Model


Input : Forest information (species, age, phenotype, management, number of trees per

ha, diameter, biomass values) Meteo-climatological information Domain data (borders, soil type, …) Species parameters

Output: Carbon sequestration estimation maps Biomass growth (Foliage, stem, root) Forest growth evolution Forest mortality estimation Seeds production estimation


Data Assimilation Approach


Use of satellite data vegetation indexes maps, high resolution (10m)

Substitution of the internally – computed LAI with the satellite-

estimated one

Increase of the model resolution from 100m x 100m to 10m x 10m

Model automatic spatialization

3D-CMCC executi

on (single point)

Single point

output manageme

nt

Input interface (1

point information extraction)

Static layers (1D/ 2D)

LAI multitemporal

maps (3D)

Climatological multitemporal

maps (3D)

2D – 3D output maps




Low disomogeneity high accuracy

Spatial resolution 10m x 10m

2,6

3,4

2,8 3

0

1

2

3

4

1 2 3 4

LAI

High disomogeneity low accuracy

Spatial resolution 100m x 100m

2

3,22,6

5,1

4,3

2

1,3

5,2

4,34,1

2,1

3,13,6

4,2

5,1

2,63

4,54,64,1

0,5

3,6

0,90,8

2,9

3,9

5,7

4,9

4,1

2,82,2

4,3

1,2

5,1

2,83,4

2

2,8

3,6

4,75,1

3,8

2,9

4,3

1,9

0

1

2

3

4

5

6

LAI




0 0 0 0

4,5 4,5 4,5 4,5 4,5 4,5

0 0

-0,5

0,5

1,5

2,5

3,5

4,5

5,5

1 2 3 4 5 6 7 8 9 10 11 12

LAI simulated

0 0 0

0,8

2,4

4,5 4,8 4,8

2,11,5

0,9

00

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

LAI measured

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

LAI from satellite data vs LAI simulated

LAI from satellite data LAI simulated

seasonal variation not considered seasonal variation considered

With the use of satellite images it is possible to consider at least 3 LAI variation duringthe growing season instead of 1 LAI simulated value


Application Site


Site: Parco Nazionale dei Monti Sibillini, Central Italy

• Area = 71.437 ha

• Latitude = 42x\.901

• Latitude = 13.205

• Altitude = 2476 m to 370 m (a.s.l.)

• Average precipitation = 1000 mm year

• Topography = disomogenous morphology

Parco Nazionale dei Monti Sibillini

• Fagus sylvatica L. forest

• Area = 5850 ha (584953 cells at resolution 10m x 10m)

• Altitude = 950 to 1850 m (a.s.l.)

• Average temperature = 7-9 C°

• Soil type = sandy-calcareous

• Growing season = 120-150 days per year

• Stand density = 2800 trees/ha

• Years of simulation = 4 (2007 to 2010)

• Points of validation = 30

Site of simulation

• Satellite data: LAI Value per grid point per month– Images have been:

• collected in L1B format (ESA C1P proposal)• Orthorectified• Radiometrically calibrated• Remapped onto a Earth Fixed Grid• Fused spatially / temporarily 1 file per year [xsize_domain, ysize_domain, 12]

– 4 seasons identified:• No growth / no leaves (Dec, Jan, Feb): same value for each month• Growing Season (March, April, May, June): different values• Summer Season (July, August, Sept): same value for each month• Falling season (Oct, Nov ): different values


Input Specifications – Satellite data



Application Site – meteo climatological data


• Meteo-climatological data average montlhy values per grid point– 1 file per year [xsize_domain, ysize_domain, 12]

• Cumulated Precipitation• Average Temperature• Global Solar Radiation• Vapour Pressure Deficit (VPD)

• Meteo-climatological data retrieved from the ISPRA site and interpolated (linear) over the domain


Application Site – Forest Structure information


• Forest Structure file (max 5 vegetation types per grid point)– 1 file per parameter [xsize_domain, ysize_domain, 5]

• Age (Class Age)• Species• Phenotype• Management • N (Number of trees)• AvDBH (Diametric Class)• Height (Height Class) • Wf • Wr• Ws

• Information provided by the local admininstration (data to be extrcted for the calibration validation dataset 2010)


Application Site – Species and site information


• Species characterization file (one for each involved specie [text file])– Canopy Quantum Efficiency– Assimilation use Efficiency– Max Age– Optimum growth temperature– etc

• Site parameters (not mandatory)


Application Site – Output Parameters


• Net primary productivity (NPP) – monthly/yearly

• Gross Primary Productivity (GPP) – monthly/yearly

• Above Ground Biomass (AGB) –yearly• Belowground Biomass (UGB) - yearly• Mean Annual Volume Increment (MAI) –yearly• Current Annual Volume Increment (CAI) –yearly


Application Site – Calibration


• Model sensitivity analysis• Model calibration (30 points) based on the

most sensible parameters (excluded from validation)


Application Site – Simulation results and statistical analysis


2006 2007 2008 2009 2010

Wr simulated 0,175 0,186 0,199 0,210 0,222

Ws measured 0,175 0,186 0,197 0,208 0,219

0,160

0,180

0,200

0,220

0,240

Wr

tDM

/a y

ear

Wr trend (2007-2010)

2006 2007 2008 2009 2010

Ws simulated 0,64 0,67 0,71 0,74 0,78

Ws measured 0,64 0,68 0,72 0,76 0,80

0,50

0,55

0,60

0,65

0,70

0,75

0,80

0,85

Ws

tDM

/a y

ear

Ws trend (2007-2010)

p-Value e% MAE% EC EF RMSE

(tDM/a)

R2

p<0.001 -2.9009 -0.0966 0.9907 1.1329 0.0016 0.9439

p-Value e% MAE% EC EF RMSE

(tDM/a)

R2

p<0.001 1.3170 0.0439 0.9887 0.7780 0.0001 0.9263

• WS Understimation trend• Wr overstimation trend• In both cases, high correlation between measured and simulated data

Average error

Relative mean absolute error

Coefficients of model efficiency

The root mean square error

O

OP=e‰ 100

O

nOPΣ=MAE‰ ii

n=i /

100 1

21

211,0

OOΣ

OPΣ=MEEC

in=i

iin=i

2

1

21

2

11,0OOΣ

OPΣOOΣ=MEEF

in=i

iin=ii

n=i

n

OPΣ=RMSE ii

n=i

21




0

50

100

150

200

250

300

350

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10

GPP

gC/m

^2 m

onth

Monthly GPP trend

GPP Min

GPP Max

GPP Av

StDev

0

0,005

0,01

0,015

0,02

0,025

0,03

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10

NPP

tDM

/a m

onth

Monthly NPP trend

NPP Max

NPP Min

NPP Av

StDev

2007 2008 2009 2010

GPP simulated 935,49 1072,85 973,92 987,38

850

900

950

1000

1050

1100

GPP

gC/m

^2 ye

ar

Average Annual GPP

2007 2008 2009 2010

NPP simulated 0,054358 0,065228 0,061404 0,065081

0,045

0,05

0,055

0,06

0,065

0,07

NPP

tDM

/a y

ear

Average Annual NPP




2007 2008 2009 2010

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

Mea

n Ann

ual V

olum

e In

crem

ent (

m^3

/a ye

ar)

Mean Annual Volume Increment (MAI)

MAI Min

MAI Max

MAI Av

StDev

2007 2008 2009 2010

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

Curre

nt A

nnua

l Vol

ume

Incr

emen

t (m

^3/ a

year

)

Current Annual Volume Increment (CAI)

CAI Min

CAI Max

CAI Av

StDev

• NPP and GPP values are quite in accordance with literature (e.g. Scarascia Mugnozza G., Ecologia strutturale e funzionale di faggete italiane. Hoepli, 2001)

• MAI and CAI values are realistic for a relatively young forest (41 yo)

Conclusions

• Assessment of the impact of the study with respect to the state of the art

• New developments to improve the present study




Application Site – Conclusions


Future ActivitiesConclusions

• Results showed high correlation between observed and computed data hence the model can be deemed a good predictor both for high resolution (10 m x 10 m) and for short period of simulation.

• The coupling satellite data at high resolution and field information as input data have showed that these data can be used in the 3D-CMCC Forest Model run.

• The model can be also successfully used to simulate the main physiological processes at regional scale


Application Site – Future Activities


Future ActivitiesConclusions

• Future developments related to:

• Implementation / use of a more accurate vegetation index time series creation algorithm

• Evaluation of a further vegetation index assimilation schema

• extension of the system to Sentinel data (sentinel 2)

• Use of further satellite data for computation of climatological input data

• Optimization of the system / algorithm

• Validation with other species / more complex forest structures

KEO Demonstrator with Models for Land Use Management– KLAUShttp://deepenandlearn.esa.int/tiki-index.php?page=KLAUS+Project

Biomass Application: http://www.sistema.at/forest.html

Contact Point: Stefano Natali (SISTEMA)Tel: +43 (0)1 2367289 7403 Fax: +43 (0)1 2533033 7427

[email protected]

http://deepenandlearn.esa.int/tiki-index.php?page=KLAUS+Project

http://www.sistema.at/forest.html

mailto:[email protected]

assimilation of high resolution satellite imagery into the 3d-cmcc forest ecosystem model

Documents

requirements definition

cmcceuromediterranean

cmcc euromediterranean

dcmcc forestecosystem

department of forest

austria5end users

forest management simulation

university of viterbo