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Assimilation of high resolution satellite imagery into the 3D-CMCC forest ecosystem model

Assimilation of high resolution satellite imagery into the 3D-CMCC forest ecosystem model. S. Natali (1) , A. Collalti (2,3), A. Candini (4), A. Della Vecchia (5), R. Valentini (2,3) (1) SISTEMA GmbH, Vienna, Austria

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Assimilation of high resolution satellite imagery into the 3D-CMCC forest ecosystem model

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  1. 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

  2. summary • Context • Proposed approach • Conclusions BG2.8 - EGU 2012, Vienna, Austria

  3. Context Approach Conclusions Contexts • Why the project has been carried out • End users critical requirements, and proposed solution BG2.8 - EGU 2012, Vienna, Austria

  4. Context Approach Conclusions Motivations User Requirements The ESA KLAUS Project BG2.8 - EGU 2012, Vienna, Austria 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

  5. Context Approach Conclusions Motivations UserRequirements End users, requirements, and proposed solution BG2.8 - EGU 2012, Vienna, Austria 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 euroMediterraneancenter for Climate Changes integrated with satellite data

  6. Context Approach Conclusions Approach • Selected forest ecosystem model • Selected integration environment (satellite data assimilation schema) • 3D-CMCC-SAT application BG2.8 - EGU 2012, Vienna, Austria

  7. Context Approach Conclusions ForestModel Assimilation Application The 3D-CMCC Model 3D-CMCC ForestModel (Collaltiet al, in prep.) is a light useefficiencymodel (LUE) thatpermitsto simulate in “natural” forestscomposedbyvariablenumberofspecies, layers and cohorts : • CO2fluxes (GPP) • Biomass production (NPP) • Carbon stock dynamic • Foreststructuredynamic • Naturalrenovation • Mortality • Light and Water competition • Meanannual volume increment • Currentannual volume increment • … (0,0,2) (0,0,1) (0,0,0) Multi-layer (tridimensional) Multi-species Multi-age Dynamic Hybrid (HMs) Monthlytime-step Spatiallyexplicit / implicit Regional scale (cellsize: 100m x 100m) (1,0,0) BG2.8 - EGU 2012, Vienna, Austria

  8. Context Approach Conclusions ForestModel Assimilation Application 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, …) • Speciesparameters Output: • Carbon sequestration estimation maps • Biomass growth (Foliage, stem, root) • Forest growth evolution • Forest mortality estimation • Seeds production estimation BG2.8 - EGU 2012, Vienna, Austria

  9. Context Approach Conclusions Forest Model Assimilation Application Data Assimilation Approach LAI multitemporal maps (3D) • 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 Climatological multitemporal maps (3D) Staticlayers (1D/ 2D) Input interface (1 point information extraction) 3D-CMCCexecution (single point) Single point output management 2D – 3D output maps BG2.8 - EGU 2012, Vienna, Austria

  10. Context Approach Conclusions Forest Model Assimilation Application Data Assimilation Approach Spatial resolution 100m x 100m Spatial resolution 10m x 10m High disomogeneity low accuracy Low disomogeneity high accuracy BG2.8 - EGU 2012, Vienna, Austria

  11. Context Approach Conclusions Forest Model Assimilation Application Data Assimilation Approach seasonal variation not considered seasonal variation considered With the use of satellite images it is possible to consider at least 3 LAI variation during the growing season instead of 1 LAI simulated value BG2.8 - EGU 2012, Vienna, Austria

  12. Context Approach Conclusions Forest Model Assimilation Application Parco Nazionale dei Monti Sibillini Application Site • 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 Site of simulation • 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 BG2.8 - EGU 2012, Vienna, Austria Site: Parco Nazionale dei Monti Sibillini, Central Italy

  13. Context Approach Conclusions Forest Model Assimilation Application Input Specifications – Satellite data • 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 BG2.8 - EGU 2012, Vienna, Austria

  14. Context Approach Conclusions Forest Model Assimilation Application Application Site – meteo climatological data • Meteo-climatological data averagemontlhyvalues per gridpoint • 1 file per year [xsize_domain, ysize_domain, 12] • CumulatedPrecipitation • Average Temperature • Global SolarRadiation • VapourPressure Deficit (VPD) • Meteo-climatological data retrievedfrom the ISPRA site and interpolated (linear) over the domain BG2.8 - EGU 2012, Vienna, Austria

  15. Context Approach Conclusions Forest Model Assimilation Application Application Site – Forest Structure information • ForestStructure file (max 5 vegetationtypes per gridpoint) • 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 providedby the localadmininstration (data tobeextrctedfor the calibrationvalidationdataset 2010) BG2.8 - EGU 2012, Vienna, Austria

  16. Context Approach Conclusions Forest Model Assimilation Application 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) BG2.8 - EGU 2012, Vienna, Austria

  17. Context Approach Conclusions Forest Model Assimilation Application 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 BG2.8 - EGU 2012, Vienna, Austria

  18. Context Approach Conclusions Forest Model Assimilation Application Application Site – Calibration • Model sensitivity analysis • Model calibration (30 points) based on the most sensible parameters (excluded from validation) BG2.8 - EGU 2012, Vienna, Austria

  19. Context Approach Conclusions Forest Model Assimilation Application Application Site – Simulation results and statistical analysis • WS Understimation trend • Wroverstimation trend • In bothcases, high correlationbetweenmeasured and simulated data Average error Relative mean absolute error Coefficients of model efficiency The root mean square error BG2.8 - EGU 2012, Vienna, Austria

  20. Context Approach Conclusions Forest Model Assimilation Application Application Site – Simulation results and statistical analysis BG2.8 - EGU 2012, Vienna, Austria

  21. Context Approach Conclusions Forest Model Assimilation Application Application Site – Simulation results and statistical analysis • NPP and GPP values are quite in accordancewithliterature (e.g. ScarasciaMugnozza G., Ecologia strutturale e funzionale di faggete italiane. Hoepli, 2001) • MAI and CAI values are realisticfor a relativelyyoungforest (41 yo) BG2.8 - EGU 2012, Vienna, Austria

  22. Context Approach Conclusions Conclusions • Assessment of the impact of the study with respect to the state of the art • New developments to improve the present study BG2.8 - EGU 2012, Vienna, Austria

  23. Context Approach Conclusions Conclusions Future Activities Application Site – Conclusions • 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 BG2.8 - EGU 2012, Vienna, Austria

  24. Context Approach Conclusions Conclusions Future Activities Application Site – Future Activities • 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 BG2.8 - EGU 2012, Vienna, Austria

  25. KEO Demonstrator with Models for Land Use Management– KLAUS • http://deepenandlearn.esa.int/tiki-index.php?page=KLAUS+Project • BiomassApplication: http://www.sistema.at/forest.html • Contact Point: Stefano Natali (SISTEMA)Tel: +43 (0)1 2367289 7403 Fax: +43 (0)1 2533033 7427 natali@sistema.at

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