Module 3 False ideas and other theoretical issues

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1 FORmation of Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes Module 3 False ideas and other theoretical issues FORmation of Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes Meteorological satellites systems: only for meteorology NOAA-AVHRR Meteosat GOES Satellite platform EOS (since 1999) NOAA (since 1979) Meteosat (since 1977) MSG (since 003) FORmation Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes The trade-off between spatial and temporal resolution. Sensor MODIS AMSR-E AVHRR AMSU Spectral Range Optical Microwave Optical, Microwave Spatial Resolution (max-min nadir view) 50 mt -1km 5 km 1km 5 km Temporal Resolution (the wors 1 hours 6 hours MVIRI Optical,5 5 km 30 min SEVIRI Optical 1 4 km 15 min for timely detection of signal variations Polar Satellites NOAA, EOS, etc Satellite systems for early warning Geostationary Satellites Meteosat, GOES, MetSat, etc. The shortest revisitation time (<6 hours, up to 15 minutes) Global coverage (by polar satellites or geostationary constallations) Long term missions (past & future) Low, sustainable costs Main meteorological satellites systems spatial resolution VIS-PAN 0,5 km 1 km (nadir view) IR 0,5 4km (nadir view) to low it depends Example 1 Forest fire monitoring on the relation between: sensor spatial resolution (i.e. minimum ground resolution cell) and minimum detectable fire extension within it

2 Looking for fires in the MIR region lack body radiation Main meteorological satellites systems Sensors Spatial Resolution Temporal Repetition Platform Data Availability FIRES - LAVA FLOWS TEMPERATURE AVHRR (global coverage) 1,1 Km 6 h MODIS (global coverage) 0,5 1 Km 1 h NOAA EOS SEVIRI (Europe-Africa) 1-3 Km 15 min. MSG 003 d dt ( T ) Planck 5 e 5 e 1 hc 1 k e hc e 5 k T e e 1 1 T 1 T T hc 1 1 k e T d dt T d dt T d ln d ln T d ln ln T d ln T ln T cost T + ln cost ( T ) Planck 5 e 1 hc 1 1 e β with β hc k β ( 1 e ) T Dependence of on spectral regions of observation Dependence of on spectral regions of observation hc 1 1 e k T T FIRES 1000 K Sun 5900 K Es. Τ300 K Spectral Irradiance MIR 3-4 µ Earth 300 K T IR 11-1 µ Wavelength (µ) Β (Τ) T 1 4 T At the shorter wavelengths the warmer portion of the pixel gives a relatively higher contribution to the radiance

3 example 1 (5%) Fire (500 K) affecting a quarter of pixel area example (1%) Fire (500 K) affecting 1/100 of pixel area MIR 4µm T 1 (1++3+4)/4 (3x )/4 300 K 500 K TIR11 µm T 4.4 (1++3+4)/4 (3x ,4 )/4 MIR 4µm T 1 ( )/100 (99x )/ K 500 K TIR11 µm T 4.4 ( )/100 (99x )/100 T~ 1/1 T 446 K AVHRR saturation SEVIRI saturation 300 K 300 K T 4µ -T 1µ > 58 K T ~ 1/4.4 T 388 K AVHRR saturation SEVIRI saturation T 1/1 T 346 K AVHRR saturation SEVIRI saturation 300 K 300 K T 4µ -T 1µ > 40 K T 1/4.4 T 306 K T(4 µm) - T(11 µm) example 3 (1/ ) Fire (800 K) affecting 1/ of pixel area saturation region reduces fire vs background contrast but sensor saturation easily occurs for fire affected pixel fractions > 0,001 depending on fire temperature MIR 4µm T 1 ( )/ (9999x )/ T 1/1 300 K 800 K 300 K 300 K TIR11 µm T 4.4 ( )/ (9999x )/ T 1/4.4 T 31 K T 4µ -T 1µ > 0 K T 300 K example 4 (1/ ) Fire (1000 K) affecting 1/ of pixel area Why don t work MIR 4µm T K TIR11 µm T 4.4 ( )/ (99999x )/ K ( )/ (99999x )/ T 1/1 300 K 300 K T 1/4.4 T 38 K T 4µ -T 1µ > 8 K T 300 K Example 1 Detecting fires Polar Satellites NOAA/AVHRR

4 Single image fixed threshold algorithms Fixed threshold on single bands (Muirhead and Cracknell 1985; Malingreau and Tucker 1988; Setzer and Pereira 1991b, 199; etc.) NOAA-AVHRR (Kaufman et al. 1990) South Italy - SUMMER Single image fixed threshold algorithms Fixed thresholds multi-bands (Kaufman et al., 1990; Arino et al., 1993; Kennedy et al., 1993; Li et al., 000; etc.) Contextuals (Justice et al., 1996; Flasse e Ceccato, 1996, Lee et al., 000, Elvidge et al., 1997, Giglio et al., 1999; etc.) AVHRR 5th july :00 GMT FALSE Actually occurred Detected Fixed threshold on band combinations (Kaufman et al., 1990; Arino et al., 1993; Kennedy et al., 1993; Li et al., 000; etc.) FALSE FALSE Kaufman et al. (1990) Flasse &Ceccato (1996) Single image fixed threshold Contesxtual : (Justice et al., 1996; Flasse e Ceccato, 1996, Lee et al., 000, Elvidge et al., 1997, Giglio et al., 1999; etc.) Main natural radiation sources for Earth Observation from satellite night-time Multi-temporal EOS-MODIS (MOD14, (PozoGiglio et al., 1999) 1997, Cuomo EOS-MODIS et (MOD14, al., 001; Giglio Lasaponara et al., 1999) North-Italy WINTER (15 Feb 005) North-Italy WINTER (17 Feb 005) et al., 003) erzo Demo FIRES 1000 K Varaz ze Trino (Vercelli) MISSED Voltri (GE) MISSED Montespertoli (Fi) Fontia (Carrara) Voltri (Ge) 8d46 E Erli 44d7 N Seborga Casanova Lerrone Carpasio MISSED Spectral Irradiance MIR 3-4 µ Earth 300 K T IR 11-1 µ Wavelength N Main natural radiation sources for Earth Observation from satellite day-time False fire alarms generated by highly reflecting surfaces (during daytime) SOR SOR Reflected Solar Radiation FIRES 1000 K Reflected Solar Radiation FIRES 1000 K Spectral Irradiance Earth 300 K Spectral Irradiance Earth 300 K MIR 3-4 µ T IR 11-1 µ Wavelength MIR 3-4 µ T IR 11-1 µ Wavelength

5 False alarms generated by highly reflecting surfaces (during daytime) Why don t work Solar reflected radiation I R ρ I I I I θ I ( T ) ε ( T ) Thermally emitted radiation Example Detecting s (Kuwai Example : Oil Spill Detection Single image processing Fixed threshold (Cross, 1991) 3-30 January 1991 (Gulf War): release of crude oil on Kuwait and Saudi Arabia coasts (Kuwai Example : Oil Spill Detection Single image processing Fixed threshold (Cross, 1991) if T 4 > 89.8 K 3-30 January 1991 (Gulf War): release of crude oil on Kuwait and Saudi Arabia coasts (Saudi Arabia) sea (Saudi FALSE Arabia) sea FALSE RIGHTNESS TEMPERATURE (K) Cross threshold Cross s threshold sea LOCATION 4th January 1991 (NOAA/AVHRR TIR channel 4 10:38 GMT) 4th January 1991 (NOAA/AVHRR TIR channel 4 10:38 GMT) Why don t work NOAA-16 July,, GMT Example 3: Volcanic Asloud detection (Single image Fixed threshold ) e.g. Prata, 1989 Example 3 Detecting volcanic aslouds T 11µm - T 1µm < 0 A A T 11µm - T 1µm [K] 3,5 3,0,5,0 1,5 1,0 0,5 0,0-0,5 A A Prata s threshold 0 1 x1 0 4 x x x 10 4 Distance [m ] NO DETECTIONS!!

6 Why don t work NOAA-16, July, GMT Example 4: Volcanic Lava flow Detection (Single image Fixed threshold ) e.g. Harris et al Summer Example 4 Monitoring lava flows T3µm -T10µm > 10K A clouds problem : - High reflectivity of clouds in MIR band (during daytime) - Low Ts values of clouds in the TIR (during both day and nigh FALSE DETECTIONS!! NOAA-1, January, GMT T3µm -T10µm > 10K Example 4: Volcanic Lava flow Detection (Single image Fixed threshold ) e.g. Harris et al Winter ( again a clouds problem ) Signal Fixed threshold tests Change detection by traditional fixed threshold approaches anomalies FALSE DETECTIONS!! time or space to reduce thresholds to increase sensitivity. or to increase thresholds in order to reduce false alarms Signal anomalies false alarms Signal anomalies missed alarms Fixed threshold tests Fixed threshold tests time or space Tempo o distanza

7 WINTER COLLEGE ON OPTICS IN ENVIRONMENTAL SCIENCE Trieste 13 February 009 What anomaly means THEORETICAL ISSUES What anomaly means Signal at the sensor V ref (r) ±nσ(r) anomalies (common sense do not consider fixed thresholds) Time or space A different approach: Robust Satellite Techniques () 1. Select an historical data-set V(r, as homogeneous as possible: same time of the day and period of the year (Τ domain) in order to reduce natural/observational noise Y (formerly RAT: Robust AVHRR Techniques, V.Tramutoli, 1998) A different approach: Robust Satellite Techniques () 1. Select an historical data-set V(r, as homogeneous as possible: same time of the day and period of the year (Τ domain) in order to reduce natural/observational noise. Compute the unperturbed reference fields for the observable V(r, Y V REF (r) and σ V (r) X X t t Febr, 1-3 GMT Febr 1-3 GMT 3. Change - Detection at the time t by Y A different approach: Robust Satellite Techniques () 1. Select an historical data-set V(r, as homogeneous as possible: same time of the dayand period of the year (Τ domain) in order to reduce natural/observational noise. Computing the unperturbed reference fields for the observable V(r, t V ( x, y, VREF ( x, y) V ( x, y, σ ( x, y) V X V REF (r) and σ V (r) Febr, 1-3 GMT Febr 1-3 GMT A.L.I.C.E. (Absolutely Llocal Index of Change of the Environmen FORmation of Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes 10 years of Applications Using several satellite/packages NOAA (AVHRR, AMSU) METEOSAT, GMS, GOES, MSG (SEVIRI) from Visible to the Microwave spectral range

8 10 years of Applications For two main classes of environmental processes: FORmation of Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes short scale changes (relatively confined in the space and/or in time) to be detected mainly for damages mitigation purposes volcanic eruptions (Ann.of Geoph.,001, 004, Remote Sens. of Env., 004a, 004b) forest fires detection (Int. J. of Rem. Sens, 001) monitoring (IWA, 003, RSE-sub. 005) cloud-detection (Atmosph. Research., 004) rapid alert for security purposes (GMOSS) Improving reliability saving sensitivity medium, long scale, changes (in space and/or time) to be analysed in terms of relative trends or as precursor of short scale events air and water quality and pollution monitoring (e.g. IRS, 001) flood mapping and Soil Wetness Variation monitoring ( IJRS 008, RSE 005) seismic area monitoring (Ann. Geoph.,001, 004, Phy. Chem.Earth, 004, RSE 005, Tectonophysics, 007) desertification processes monitoring, etc... Example 1 Detecting fires: Example 1: Fire Detection Example 1: Fire Detection AVHRR 5 th july :00 GMT (South Italy) AVHRR 5 th july :00 GMT (South Italy) Fires detected Fires detected Kaufman et al. (1990) Kennedy et al.(1993) Actual fires Kaufman et al. (1990) Kennedy et al.(1993) Actual fires FALSE FALSE Flasse &Ceccato (1996) 3 (x,y, >5 NO FALSE Flasse &Ceccato (1996) 3 (x,y, >5 tunability: Thermal structure description 3 (x,y, >5 3 (x,y, >4 3 (x,y, >3 FORmation of Multi-disciplinary Approaches to Training in Earth Observation Erasmus Intensive Programmes Example 1: Fire Detection EOS-MODIS (MOD14-algorithm) North-Italy WINTER (17 Feb h) TMIR ( x, y, µ MIR( x, y) MIR ( x, y, σmir( x, y) (AVHRR at 1:00 GMT) Voltri(Ge) Improving reliability saving sensitivity Seborga N Voltri (Ge) Erli Casanova Lerrone Carpasio MISSED FIRES Carpasio Erli Casanova Lerrone Seborga fire started in the afternoon hours after NOAA- 3 (x,y, >3 AVHRR pass N NO MISSED NO FALSE FIRES Example Detecting s

9 Example : Oil Spill Detection 3-30 January 1991 (Gulf War): release of crude oil on Kuwait and Saudi Arabia coas Cross (1991) T 4 > 89.8 K T4 pipeline > 5 Oil Spill and Seepage Detection on the Caspian Sea T 4 > 3 T 4 > 4 T 4 > 5 r ' ' r ' T4(, t ) 4( r ) T (, t ) r µ 4 σ ( ) 4 FALSE FALSE sea (channel 4 AVHRR GMT) (channel 4 AVHRR GMT) AVHRR image of the , channel 4 ( µm) GMT Improving reliability saving sensitivity Example : Ash Cloud Detection Traditional methods (Prata, 1989) NOAA-16 July,, GMT July-August, 001 Etna eruption T11µm - T1µm < 0 T T 11µm - T 1µm < 0 K Example 3 Detecting volcanic aslouds e.g. Prata, 1989 Traditional fixed threshold methods NO DETECTION!! Example : Ash Cloud Detection NOAA-16 July,, GMT Improving reliability saving sensitivity T T T τ ) < 0 τ ) < 1 τ ) < July-August, 001 Etna eruption T [ T µ ( r) ] T T σ ( r) T( r, T11 T1 DETECTION AND TUNEAILITY! µ µ Example 4 Monitoring lava flows