EUFAR - European Facility for Airborne Research CNR IMAA airborne facilities ODS3F – Observation and Detection Systems For Forest Fire Monitoring Rome, 15th May 2014 Stefano Pignatti

Instrument Type Instrument name Serial type Operator Measured parameter ++ Range Incident flow vector probe AIMMS-20-ARIARIAirspeed; Incidence angle; Turbulence Radar Radio-echo-sounderAWIReflectivityPenetrates ice up to 4000m thick GPS Trimble-4000SSIAWIAircraft position, velocity and attitude Other LaCoste-Romberg- Gravimeter AWI Gravity field Other Scintrex-MagnetometerAWI Magnetic field: nT Laser Altimeter Riegl-LD90-AWIAWIAircraft height above surface m (if reflectivity > 0.8) Laser scannerRiegl-LMS-Q280AWIsurface maps VIS/NIR spectrometer OceanOpticsCNR-IBIMETRadiance Incident flow vector probe CNR-Mobile-Flux-PlatformCNR-IBIMETAirspeed; Incidence angle; Turbulence Imaging Spectrometer TASI-600 S/N 5506CNR-IMAARadiance SpectraLWIR 8-12micron CO2 and H20 by IR absorption Licor7500-CNRCNR-ISAFoMCO2, H2OCO2: ppm. H2O: ppm. Incident flow vector probe BAT-ARACNR-ISAFoMAirspeed; Incidence angle; Turbulence Dew/Frost-point hygrometer Edgetech-Dewtrak200CNR-ISAFoMDew Point °C. Operating principle: thermo-electric BBR Licor-Quantum-PARCNR-ISAFoMHemispheric broadband radiance nm Laser Altimeter Riegl- LD90-CNRCNR-ISAFoMAircraft height above surface <500m BBR Cambell-Q7.1CNR-ISAFoMHemispheric broadband radiance µm BBR Everest ZLCNR-ISAFoMHemispheric broadband radiance InfraRed ( °C)

Partenavia P68 Observer2 Technical information: - Typ. speed: 52 m/s - Ceyling height: ft - Typ. operating height: ft - Empty weight: 1420 Kg - Max. take-off weight: 2084 Kg - Max payload: 660 Kg TASI-600 installation - Total electrical power: KW - Electrical power available: 1 KW at 27.5V +/- 0.5V - Usual range during measurements flight: 1620 Km - Aircraft can flight in non iceing low operat. costs - Avionics is is equipped with Garmin GNS 430W - Take-off runway lenght: 630 m - in ISA – MTOM - 0 Wind - Helicopter-like visibility through the plexiglas cockpit Research Infrastructures CNR IMAA’s property in terms of scientific equipment is now estimated at more than 12 million Euros. The main instrumental facilities operating at the IMAA laboratories are: CIAO-CNR-IMAA Atmospheric Observatory which is one of the 12 worldwide sites within the GRUAN network for the study of the high atmosphere; a system used for receiving, processing and storing satellite images (NOAA, MSG, EOS-AQUA, EOS-TERRA), which is capable of processing online more than 120 Tbyte of data; a Hydrogeosite Experimental test field at the the Marsico Nuovo Centre, which is the first full-scale laboratory in Italy for the investigation of hydrogeophysical processes; mobile laboratories consisting of a Lidar system, a system for interferometric and radiometric measurements, a system for non-invasive physico-chemical and geophysical measurements, a system for geochemical and mineralogical measurements and a mobile vehicle equipped with systems of satellite data reception and transmission as well as sensors for ground-based RS data acquisition. Research Topics Development and Integration od Lidar, Radiometric and Microwave; Tecniques for the 4D Characterization of Atmosphere; Satellite Remote Sensing for Clouds and Precipitations; OT Multi-platform Methods and Techniques for Surface Process Characterization and Natural and Anthropic; Risk NRT Monitoring; Earth Observation Integrated Techniques for Environmental and Archeological Research - “ARGON”; Micro and Biominerals in Environmental and Human Health Issues; Integrated Methodologies for the Study of Soil and Subsoil; Integrated Modelling for Energy-Environmental Sustainability. Networks and International Working Teams NEREUS, Network of European Regions Using Space Technologies; Copernicus Regional Contact Office (RCO) Network ; IGOS-Geohazard Core Team; EGU Core Team; Working group on Satellite data-driven detection, tracking and modeling of volcanic hotspots; ISIS - Working Group. Staff: more than 100 researchers involved in several international and national research projects.

References: - R. Casa, F. Castaldi, S. Pascucci, A. Palombo, S. Pignatti (2013). “A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing”. Geoderma 01/ S. Pascucci, C. Belviso, R. M. Cavalli, A. Palombo, S. Pignatti, F. Santini (2012). “Using imaging spectroscopy to map red mud dust waste: The Podgorica Aluminum Complex case study”. Remote Sensing of Environment, Volume 123, pp R. Casa, F. Baret, S. Buis, R. Lopez-Lozano, S. Pascucci, A. Palombo, H. G. Jones (2012). “Estimation of maize canopy properties from remote sensing by inversion of 1-D and 4-D models”. Precis. Agric. 04/2012;11(4): S. Pignatti, R.M. Cavalli, V.Cuomo, L.Fusilli, S. Pascucci, M.Poscolieri, F.Santini. “Evaluation of Hyperion capability for land covers mapping in a fragmented ecosystem: Pollino National Park (Italy) case study”. RSE, 113 (3) (2009) 622– S. Pascucci, C. Bassani, A. Palombo, M. Poscolieri, R.M. Cavalli (2008). “Road Asphalt Pavements Analyzed by Airborne Thermal Remote Sensing: Preliminary Results of the Venice Highway”. Sensors 2008, 8, C. Bassani, R.M. Cavalli, F. Cavalcante, V. Cuomo, A. Palombo, S. Pascucci, S. Pignatti (2007). “Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data”. Remote Sensing of Environment, 109, pp S. Pascucci, Fusilli L., Palombo A., Pergola N., Pignatti S., Santini F. (2013). «Karst water resources detection through airborne thermal data: MIVIS and TASI-600 im- agery”, in International Geoscience and Remote Sensing Symposium (IGARSS'13), July 2013, Melbourne, Australia.. - F. Santini, U. Amato, M. Daraio, S. Pignatti, A. Palombo, S. Pascucci, “Calibration is- sues and pre-processing chain of the TASI-600 airborne LWIR hyperspectral scan- ner”. WHISPERS 2013, June 2013, Gainesville, Florida, USA. - M. F. Carfora, A. Palombo, S. Pascucci, S. Pignatti and F. Santini. “ Land cover map- ping capability of multispectral thermal data: the TASI-600 case study“. WHISPERS 2013, June 2013, Gainesville, Florida, USA. - S. Pascucci, M. Daraio, A. Palombo, S. Pignatti, F.Santini, G. Laneve, ‘TASI-600 high resolution airborne thermal data for accurate materials detection in urban scenarios’. 33rd EARSeL 2013:’Thermal Remote Sensing’ session. 5-7 June Matera(Italy). - S. Pignatti, Lapenna V., Palombo A., Pascucci S, Pergola N., Cuomo V. (2011). “An advanced tool of the CNR IMAA EO facilities: Overview of the TASI-600 hyperspectral thermal spectrometer“, in 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Lisbon, Portugal, 6 -9 June Contacts: Director of CNR IMAA: Hyperspectral RS Lab and TASI facilities: Applications Karst water resources detection through TASI-600 imagery R:8383 nm; G:9697 nm; B:11230 nm TASI-600 urban materials map from emissivities (K coeff. > 0.90) TASI-600 sub-superficial pipeline monitoring

Strumentazione RS del CNR IMAA StrumentoTipoRange(μm)Risoluzione Ocean Optics 2000Spettrometro0.3÷0.83 nm Flir SC7000Camera3.0÷5.0integrata Flir SC900VLCamera8.0÷12.0integrata FT-IR D&P Model 102Interferometro2.0÷ μm Sensori a terra IMAA

Carta delle principali tipologie vegetazionali del Parco Nazionale del Pollino AEREO (MIVIS) SATELLITE (HYPERION) Classificazioni MIVIS e Hyperion ottenute applicando il classificatore supervisionato MD considerando 13 classi CORINE (fino al 4° livello). Fino al 4° livello CORINE MIVIS e Hyperion hanno prestazioni simili

150 metri Test area = 484 pixels Test area = 25 pixels Test area = m 2 Classificazione MIVIS Hyperion Classification Roccia Arbusti Praterie aride Faggi MIVIS Hyperion Ortofoto aerea Dimensione del pixel terra: 1.5 m Classificazione Hyperion Dimensione del pixel a terra: 7 m Dimensione del pixel a terra: 30 m Il confronto dei risultati con le percentuali abbondanze dei singoli endmembers a livello di subpixel è ottenute dal MIVIS. Errore definito tramite “Errore Residuale” (RE) Endmembers % unmixing HYPERION % MD MIVIS arbusti faggio Praterie aride RE% = 5.03 Analisi di un sistema naturale ad elevata frammentazione attraverso tecniche di unmixing. Studi per la missione PRISMA: analisi sub-pixel Parco Nazionale del Pollino

Campagna aerea VNIR per il progetto IOSMOS ( IOnian Sea water quality MOnitoring by Satellite data ) Applicati diversi Indici di Vegetazione per il retrieving dello stato di salute della vegetazione (rosso-ottimo; giallo/verde - buono; verde/blu - stressata)