Short Introduction to Remote Sensing and it‘s Usability for CEUBIOM

1. CEUBIOM Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations Short Introduction to Remote Sensing and it‘s Usability for CEUBIOM Uwe Ballhorn RSS GmbH Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

2. Structure • Basic Introduction Remote Sensing • Remote Sensing and it’s Usability for CEUBIOM • International and European Standards and Recommendations Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

3. Basic Introduction Remote Sensing Remote sensing is the science and the art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation Lillesand & Kiefer, 3rd Edt.; 1994 Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

4. Sun Basic Introduction Remote Sensing Sensor Data processing Data Analysis Geo-Information Maps Monitoring Action/planning Object Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

5. Basic Introduction Remote Sensing 4 Resolutions of Importance • geometric: usually understood as “resolution”. The smallest detail resolved at the ground also called spatial resolution. • spectral: number, wavelength region and width of spectral bands. • radiometric: the photon count sensitivity of the sensor. Describes thecapability to differentiate “intensity” or “brightness” classes. • temporal: the repeat frequency of revisiting the same ground segment. Dependent on the registration platform, the ground resolution and the orbit parameter. Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

6. temporal geometric radiometric spectral Basic Introduction Remote Sensing Interdependence of the Different Resolutions increase in spectral resolution increase in geometric resolution increase in radiometric resolution increase in temporal resolution reduction of reflected photons per area reduction of reflected photons per area enlarged dynamic range more data per season • Conclusions: • tuning of one of the "technical" resolution parameter • (spectral, geometric, radiometric) affects the other two • only improvements of the photon capture capability of • the sensor (radiometric r.) led to substantial • improvements of the system as a whole Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

7. Basic Introduction Remote Sensing • Data Restoration • geometric correction • atmospheric correction • resampling • etc. • Image Enhancement • visualisation • contrast streching • filter operations • etc. Digital Image Processing • Image Analysis • Advantages versus image interpretation • Faster, cheaper and more objective • Possible Problems • Heterogenous image material and variation of objects • Expert knowledge difficult to integrate • Wrong class assignments due to mixed pixel Accuracy Assessment Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

8. Remote Sensing and it’s Usability for CEUBIOM Satellite sensors- multispectral ~ daily coverage ~ weekly coverage ~ 2 weeks coverage Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

9. Remote Sensing and it’s Usability for CEUBIOM Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

10. Remote Sensing and it’s Usability for CEUBIOM Direct biomass assessment methodologies Statistical – empiricalmodelling • Based on statisticalanalysisof experimental data – groundtruthdata, officialstatistics – andthe remote sensingsignal • Remote sensingsignalareoftentransferredinto VI -highlightingimportantvegetationcharacteristicsandtryingtominimizesoilandatmoshpereinfluence Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

11. Remote Sensing and it’s Usability for CEUBIOM Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

12. Remote Sensing and it’s Usability for CEUBIOM Direct biomass assessment methodologies • Statistical – empirical modelling • NDVI1 – Normalized Difference Vegetation Index • EVI2 – Enhanced Vegetation Index • SAVI3 – Soil Adjusted Vegetation Index 1 Rouse et al., 1973 2 Holben et al., 1981 3 Heute et al., 1988 Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

13. Remote Sensing and it’s Usability for CEUBIOM Direct biomass assessment methodologies Statistical – empiricalmodelling • linkedtothe variable ofinterestby simple or multiple regression, e.g. usingstepwise multiple regression • More sophicsticatedstatisticalapproachesare, e.g. artificialneuralnetworksordecisiontrees Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

14. Remote Sensing and it’s Usability for CEUBIOM Direct biomass assessment methodologies Semi – empiricalmodelling • based on physicalmodels, but forinversionempiricalrelationshipsareused • multispectral „CleversLeaf Area Index byReflectance“ (CLAIR)1 model, based on theWeigthedDifference Vegetation Index (WDVI)‏ Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

15. Remote Sensing and it’s Usability for CEUBIOM Direct biomass assessment methodologies Deterministic – physical modelling • complex deterministic models are based on inversion of canopy reflectance models • simulate the interactions between solar radiation and the elements constituting the canopy using physical laws • examples: Scattering Arbitrary Inclined Leaves (SAIL), PROpertiesSPECTra (PROSPECT), Michigan Microwave Canopy Scattering Model (MIMICS)‏ Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

16. Remote Sensing and it’s Usability for CEUBIOM Indirect biomass assessment methodologies Classificationbased • unsupervisedclassifications, e.g. ISODATA • supervisedclassifications, Maximum LikelihoodClassification, DecisionTrees, Support Vector Machines • others: segment-based, fuzzyclassifications Current – historicalbiomassassessment • Land use maps derived from remotely sensed data can be linked with official statistical data, e.g. with data from forest inventories for biomass assessment. The area covered by the different vegetated classes in the land cover map is used to calculate the total biomass in a region. Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

17. Remote Sensing and it’s Usability for CEUBIOM Indirect biomass assessment methodologies Potential biomassassessment • Besides the assessment of the current or historical biomass status, the potential biomass may be estimated as well. To do this, auxiliary data in vector and raster format must be analyzed in a Geographic Information System (GIS). Important information which may be considered for inclusion are: precipitation, temperature, relief, or soil type. Economic data may also be included. Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

18. Remote Sensing and it’s Usability for CEUBIOM Indirect biomass assessment methodologies Future biomassassessment • Future scenarios based on existing data (statistics, land cover maps, land cover change maps, and so on) can be modeled assuming different disturbances ranging from complex climate change impacts, political regimes, or forest management strategies to the simple assumptions like the construction of new roads or business as usual assumptions. Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

19. Remote Sensing and it’s Usability for CEUBIOM Limitation of remotely sensed data and potential Like other monitoring methods, remote sensing methods have advantages and limitations. The overall advantage of the technique is the large amount of uniform data, providing large spatial coverage, collected from distance for less expense than field-based mapping. Constraints are the technical limits on feature discrimination, the costs, the required high level of technical expertise and the need for information to calibrate and verify the results (Tuner 2003). Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

20. International and European Standards and Recommendations CORINE Scale: Level 1 with 5 classes; 1:1.000.000 Level 2 with 15 classes; 1:500.000 Level 3 with 44 classes; 1:100.000 MMU: 25ha 32 European countries 1990, 2000, 2006 CORINE land cover 2000: data available for Europe • Usability for CEUBIOM: • harmonized way throughout Europe • +/- classes and the MMU are maybe too coarse Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

21. International and European Standards and Recommendations LUCAS – Land Use / Cover Area frame statistical Survey Scale: 3 Levels ~ 1*1km² Grid Available for 26 European countries 2001, 2003, 2006 • Usability for CEUBIOM: • for training or validation • - no wall-to-wall biomass mapping Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

22. International and European Standards and Recommendations JRC forest Cover Forest, non-forest, clouds & snow, no data MMU: 1ha All European countries 1990, 2000, 2006 • Usability for CEUBIOM: • harmonized way throughout Europe • independent of national boundaries • high spatial detail – better than CORINE Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

23. International and European Standards and Recommendations GMES Service Element – Forest Monitoring (GSE- FM)‏ MMU: 1ha Europa: 14 countries ~1990, 2000, 2008 GSE-FM products • Usability for CEUBIOM: • harmonized European-wide product (medium resolution)‏ • recent for 2008 Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

24. International and European Standards and Recommendations GOFC-GOLD Global Observation of Forest and Land Cover Dynamics proposed systematic program for coarse resolution (250 – 1000 m) land cover mapping on a five-year cycle combination with periodic mapping and monitoring of forested areas at fine resolution (~25 m) Usability for CEUBIOM: • important international activity well connected to main players and initiatives • could be a valuable input for the development of a harmonized approach • no harmonized data set available Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

25. International and European Standards and Recommendations FAO – Forest Resource Assessment 2010 (FRA2010) High resolution (i.e. 20 to 30 m pixel) sampling based on optical imagery forest area and area change statistics change matrices on other land uses • Usability for CEUBIOM: • FRA2005 is only available for the tropical areas • FRA2010 is not available for at least two more years • could be used for comparisons in a later stage (part of a follow-up activity) Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

26. International and European Standards and Recommendations Geoland I and II 23 classes (based on CORINE land cover class definitions) MMU: 1ha currently only 5 core areas every 5 years • Usability for CEUBIOM: • currently only 5 examples  not yet covering whole Europe • harmonized way throughout Europe • follow-up project GEOLAND II  more detailed results will be produced Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations

27. Additional Information http://ceubiom.org/deliverables/ Deliverable D.2.1: Methods compendium on current state-or-the-art in EO for biomass assessment Deliverable D.2.2: Study on SAR potential for direct biomass assessment Deliverable D.2.3: Recommendations on EO data for European users Classification of European Biomass Potential for Bioenergy Using Terrestrial and Earth Observations