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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.

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Presentation on theme: "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."— Presentation transcript:

1 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 (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, Austria2

3 Contexts Why the project has been carried out End users critical requirements, and proposed solution BG2.8 - EGU 2012, Vienna, Austria3 Context ConclusionsApproach

4 BG2.8 - EGU 2012, Vienna, Austria4 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 Context ConclusionsApproach Motivations User Requirements

5 BG2.8 - EGU 2012, Vienna, Austria5 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 Context ConclusionsApproach Motivations User Requirements

6 Approach Selected forest ecosystem model Selected integration environment (satellite data assimilation schema) 3D-CMCC-SAT application BG2.8 - EGU 2012, Vienna, Austria6 ContextConclusions Approach

7 BG2.8 - EGU 2012, Vienna, Austria7 The 3D-CMCC Model ContextConclusions Approach Forest 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 : CO 2 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-age Dynamic Hybrid (HMs) Monthly time-step Spatially explicit / implicit Regional scale (cell size: 100m x 100m) Multi-layer (tridimensional) Multi-species Multi-age Dynamic Hybrid (HMs) Monthly time-step Spatially explicit / implicit Regional scale (cell size: 100m x 100m)

8 BG2.8 - EGU 2012, Vienna, Austria8 The 3D-CMCC Model ContextConclusions Approach Forest Model ApplicationAssimilation 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

9 BG2.8 - EGU 2012, Vienna, Austria9 Data Assimilation Approach ContextConclusions Approach Forest ModelApplication Assimilation  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

10 BG2.8 - EGU 2012, Vienna, Austria10 Data Assimilation Approach ContextConclusions Approach Forest ModelApplication Assimilation Low disomogeneity high accuracy Spatial resolution 10m x 10m High disomogeneity low accuracy Spatial resolution 100m x 100m

11 BG2.8 - EGU 2012, Vienna, Austria11 Data Assimilation Approach ContextConclusions Approach Forest ModelApplication Assimilation 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

12 BG2.8 - EGU 2012, Vienna, Austria12 Application Site ContextConclusions Approach Forest Model Application Assimilation Site: Parco Nazionale dei Monti Sibillini, Central Italy Area = ha Latitude = 42x\.901 Latitude = 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 ( 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 = days per year Stand density = 2800 trees/ha Years of simulation = 4 (2007 to 2010) Points of validation = 30 Site of simulation

13 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, Austria13 Input Specifications – Satellite data ContextConclusions Approach Forest Model Application Assimilation

14 BG2.8 - EGU 2012, Vienna, Austria14 Application Site – meteo climatological data ContextConclusions Approach Forest Model Application Assimilation 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

15 BG2.8 - EGU 2012, Vienna, Austria15 Application Site – Forest Structure information ContextConclusions Approach Forest Model Application Assimilation 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)

16 BG2.8 - EGU 2012, Vienna, Austria16 Application Site – Species and site information ContextConclusions Approach Forest Model Application Assimilation 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)

17 BG2.8 - EGU 2012, Vienna, Austria17 Application Site – Output Parameters ContextConclusions Approach Forest Model Application Assimilation 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

18 BG2.8 - EGU 2012, Vienna, Austria18 Application Site – Calibration ContextConclusions Approach Forest Model Application Assimilation Model sensitivity analysis Model calibration (30 points) based on the most sensible parameters (excluded from validation)

19 BG2.8 - EGU 2012, Vienna, Austria19 Application Site – Simulation results and statistical analysis ContextConclusions Approach Forest Model Application Assimilation p-Valuee%MAE%ECEF RMSE (tDM/a) R2R2 p< p-Valuee%MAE%ECEF RMSE (tDM/a) R2R2 p< 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

20 BG2.8 - EGU 2012, Vienna, Austria20 Application Site – Simulation results and statistical analysis ContextConclusions Approach Forest Model Application Assimilation

21 BG2.8 - EGU 2012, Vienna, Austria21 Application Site – Simulation results and statistical analysis ContextConclusions Approach Forest Model Application Assimilation 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)

22 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, Austria22 ContextConclusions Approach

23 BG2.8 - EGU 2012, Vienna, Austria23 Application Site – Conclusions Context Conclusions Approach Future Activities 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

24 BG2.8 - EGU 2012, Vienna, Austria24 Application Site – Future Activities Context Conclusions Approach Future Activities Conclusions 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

25 KEO Demonstrator with Models for Land Use Management– KLAUS Biomass Application: Contact Point: Stefano Natali (SISTEMA) Tel: +43 (0) Fax: +43 (0)


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