Thermal monitoring of volcanoes from space

Thermal monitoring of volcanoes from spaceMatthew PatrickMichigan Tech UniversityTungurahua volcanoNov 11, 2006ASTER Bands 9-3-2Many thanks to Rob Wright (Univ. Hawaii) for sharing material from his 2004 Cordoba workshop presentation

What can satellite thermal monitoring provide?2) Identifying precursory activity: Santa Ana, El Salvador, 2005MODVOLC (MODIS) anomaliesCourtesy of SNET and Smithsonian Inst.• Only 3 MODVOLC anomalies have ever been observed at Santa Ana: July 27, 2005; Aug 27, 2005; Aug 30,2005. Major eruption occurred on Oct 1, 2005.• Anomalies corresponded with RSAM peak and anomalous ground observations (incandescent fumaroles)

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

Basic principles of thermal monitoring1) Radiant heat output: determined by Planck equationPlanck equation:Hotter objects have peak emission at shorter wavelengthsM =Ml = spectral radiant exitance (W m -2 µm -1 )C1 = 3.74151 × 10 8 W m -2 µm -4C2 = 1.43879 × 10 4 µm Kλ= wavelength (µm)C 1λ 5 [exp(C 2 /λT)-1]

Basic principles of thermal monitoring2) Radiant heat output: determined by Planck equation• Planck’s Blackbody Radiation Law: spectral distribution of energy radiated by a blackbody as a function oftemperatureWien’s Law: turning point of the Planck functionStefan’s Law: integral of the Planck functionCourtesy R. Wright

Basic principles of thermal monitoring3) Stefan-Boltzmann and Wein’s displacement lawStefan-Boltzmann law: to calculate total emitted radiant heat flux (from integrating Planck function)M T = T M T = W m -2 = 5.669 × 10 -8 W m -2 K -4Wein’s displacement law: to calculate wavelength of peak emission (from derivative of Planckfunction set to zero)λ m = b/Tλ m = µmb = 2898 µm KWhat is peak emission wavelength of:• Earth (15 o C)?• Active lava (1000 o C)?• Sun (5500 o C)?Courtesy R. Wright

Basic principles of thermal monitoring4) Electromagnetic spectrumFor monitoring temperatures (of lava and background Earth), we areinterested in wavelengths from 1 to 14 µm

Basic principles of thermal monitoring5) Satellites use narrow-band detectorsExample: Landsat ETM+

Basic principles of thermal monitoring6) Bands are chosen based on atmospheric transmissionExample: ASTER and Landsat ETM+ satellite bandsτ

Basic principles of thermal monitoring7) Calculating apparent temperature from measured radiance• Invert modified Planck function to obtain temperature from spectral radianceT =C 2λln[1+ C 1 /(λ 5 L )]• λ is the band’s central wavelength (a reasonable approx. for narrow bands)• L λ is spectral radiance, as measured by the satelliteSo far, so good, but…….1) Planck’s blackbody radiation law describes the spectral emissive power of a blackbody2) Formula does not account for radiance absorbed by atmosphereCourtesy R. Wright

Basic principles of thermal monitoring8) Real surfaces are not blackbodies (but can be pretty close)• Blackbody: Emits energy at all wavelengths and in all directions at maximum rate possiblefor given temperature – Ideal radiator (emissivity=1)• Emissivity: ratio of emitted energy to that of a blackbody at the same kinetic temperatureCourtesy R. Wright

Basic principles of thermal monitoring9) Calculating real (kinetic) surface temperature from measured radiance• Inversion of Planck function only gives Apparent Radiant Temperature• Apparent Radiant Temperature < True Kinetic Temperature• Must account for emissivity AND the imperfect transmission of radiance by theatmosphereT kin =c 2ln[1+ c 1 /( 5 L )]This provides kinetic temperature for pixel as a whole – i.e. assumesuniform temperature distribution within pixelCourtesy R. Wright

Basic principles of thermal monitoring10) Mixed pixel problem• Volcanic surfaces rarely thermally homogenous• Measured L l integrated over all radiators presentwithin the pixel at the time of samplingnL n (λ) = f i L(λ, T i )i=1• A single temperature will fail to describe the actualsub-pixel temperature distribution• Simple and more complicated methods exist for unmixingthe mixed thermal emission spectrum – however,all make major assumptionsIn operational monitoring, unmixing pixels is not practical – thus, focus istypically on relative changes in “apparent” (i.e. whole pixel) temperaturesCourtesy R. Wright

Basic principles of thermal monitoring11) Reflection versus emission (nighttime versus daytime)The problem:Shortwave IR bands (2-4 microns) have highest sensitivity to emitted radiation fromhigh temperature volcanic surfaces (e.g. active lava)But...Those bands contain significant amounts of reflected radiation from sunSolution:In daytime imagery: Recognize potential impact of reflected radiation, and decreasedsensitivity to volcanic signal (due to higher ‘noise’ levels)Or use nighttime imagery: no reflected radiation at night, obviouslyLongwave IR bands (8-12 microns) are not sensitive to reflected radiation, but theyare less sensitive to small areas of high temperature volcanic surfaces

Basic principles of thermal monitoring12) Other practical limitationsa) Satellite repeat periodsHigh spatial resolution (tens of m) = low temporal resolution (tens of days)Low spatial resolution (1 km) = high temporal resolution (daily)b) Cloud cover: Dense clouds can completely block IR from even large, activelava flows• A limitation in Central America, varies by seasonc) Saturation/response-pixel – pixels over active lava surfaces often saturate(reach highest measurable value), which affects neighboring pixels

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

Current satellite sensors and data availableHigh resolution thermal dataASTERVisible (3 bands): 15 mShortwave (6 bands): 30 mThermal (5 bands): 90 mLandsat ETM+Visible (1 band): 15 mVisible (3 bands): 30 mShortwave (4 bands): 30 mThermal (1 band): 60 mSanta AnaSanta AnaFeb 3, 2001 May 3, 2005Repeat period: on demand (sporadic)Cost: 80 USD/sceneRepeat period: 14 daysCost: 250 USD/sceneData after May 30, 2003 have scanline corrector problem

Current satellite sensors and data availableLandsat Thematic Mapper• 7 bandpasses (3 VIS, 1 NIR, 2 SWIR, 1 TIR)• LEO• 30-m IFOV (TIR = 120-m)• 16 day repeat; 185 km swath• 8-bit data• Large archive of volcano data• Very, very few night-time data sets• Still going strongCourtesy R. Wright

Current satellite sensors and data availableLandsat Enhanced Thematic Mapper Plus• ETM+: like TM but enhanced• LEO• 15-m PAN band• 60-m TIR band (high & low gain)• Scanning mechanism malfunctioned inMay 03Courtesy R. Wright

Current satellite sensors and data availableAdvanced Spaceborne Thermal Emission andReflection Radiometer• ASTER: “Landsat-class” instrument• LEO• 14 bandpasses (2 VIS, 1 NIR, 6 SWIR, 5TIR)• 30-m IFOV (VIS & NIR); 30-m (SWIR);90-m (TIR)• Stereoscopic capability (NIR)• 60 km swath• VIS/NIR/SWIR = 8-bit; TIR=12-bit• Limited duty cycle but a lot of volcanodata including night-time scenes• Not experimental, but no replacementplannedCourtesy R. Wright

Current satellite sensors and data availableEarth Observing-1 Hyperion• Only commercially available space-based hyperspectralimager• LEO• 242 contiguous wavebands between 0.357 – 2.479µm (196 unique calibrated wavebands)• 30-m IFOV• 7.5 km swath• 12-bit data• Very low duty cycle (experimental satellite)• Regular night-time data takes over activevolcanoes

Current satellite sensors and data availableLow resolution dataAVHRR MODIS GOES• Polar orbiting• 1 km pixel size• 5 bands• Multiple operatingsatellites (>1 scene/day)• Polar orbiting• Visible: 250 m pixel size• Thermal: 1 km pixel size• 36 bands• Two operating satellites(Terra and Aqua)• Geostationary• Visible: 1 km pixel size• Thermal: 4 km pixel size• 5 bands• Image every 15-30 min.• Only westernhemisphere

Current satellite sensors and data availableAdvanced Very High Resolution Radiometer• LEO• Meteorological/environmental monitoring sensor• 5 bandpasses (NOAA 12+). 1 VIS, 1 NIR, 1 SWIR,2 TIR• IFOV = 1.1 km at nadir (much large off-nadir)• 10-bit dataCourtesy R. Wright

Current satellite sensors and data availableGeostationary Operational Environmental Satellite• GOES: meteorological satellite• Geostationary orbit• 5 bandpasses (GOES 8-10). 1 VIS, 1 SWIR, 1MIR, 2 TIR• 1-km IFOV (VIS); 4-km (SWIR, TIR); 8 km (MIR)• 10-bit data• Temporal resolution: 7.5-30 min.Courtesy R. Wright

Current satellite sensors and data availableModerate Resolution Imaging Spectroradiometer• MODIS: environmental monitoring sensor• LEO• 36 bandpasses: 11 VIS; 5 NIR; 10 SWIR; 10 TIR)• 250-m IFOV (bands 1 & 2); 500-m (bands 3-7);1000-m (bands 8-36)• 12-bit data• 48-hour repeat frequency (sort of….)• Two MODIS sensors currently in orbitCourtesy R. Wright

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

High-resolution versus low-resolution dataHigh resolution thermal dataStrength 1: Gives an idea of eruption style (not just presence of activity)Example: Mount Belinda (Montagu Island) eruption, South Sandwich IslandsMODIS (1 km) ASTER thermal-visible composite (15-30 m)Appears to be erupting...butstyle unknown90 m wide lava flow sourced fromsummit crater extending 3.5 km toreach the sea

High-resolution versus low-resolution dataHigh resolution thermal dataStrength 2: Provides view of passive heat emission (e.g. fumaroles)Hightemperaturefumaroles inSanta AnacraterSanta AnaLowtemperaturefumaroles onIzalco summitIzalcoASTER nighttime thermal IR image: 16 Jan 2006

High-resolution versus low-resolution dataHigh resolution thermal dataWeakness 1: Temporal resolution is poor (tens of days – if cloud free)Weakness 2: Costly (80-250 USD per scene)Example: Search for ASTER data over Fuego volcano over last 7 months – just 4 scenes

High-resolution versus low-resolution dataLow resolution thermal dataStrength 1: Excellent temporal resolutionExample: 36 MODIS images for Fuego over two weeks (May 20 and June 3, 2007)Strength 2: Often free (AVHRR and MODIS)

High-resolution versus low-resolution dataWeakness 1: Low resolution!Low resolution thermal dataExample: Mount Belinda (Montagu Island) eruption, South Sandwich IslandsMODIS (1 km) ASTER thermal-visible composite (15-30 m)Appears to be erupting...butstyle unknown90 m wide lava flow erupted fromsummit crater, extending 3.5 km toreach the sea

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

Thermal monitoring approachesMethods:Manual:1) Detection by eye and recordingAutomated:2) Simple threshold3) Band differential (shortwave IR minus longwave IR)4) Comparison with average background (RATS)

Thermal monitoring approaches‘High’ versus ‘low’ spatial resolution data• Many space-based resources available that acquiredata in the important 4 and 12 µm bandpasses• High spatial resolution data can detect smaller, lessintense thermal anomalies, but…..ATSR – 1 km pixels• Their low temporal resolution, low duty cycle, datavolume make them (largely) impractical as volcanomonitoring tools (but possible OK as a volcano“surveying tool”; see work of Rick Wessels)• Low spatial, high temporal resolutionenvironmental/meteorological satellites are the bestbetLandsat TM – 30 m pixelsCourtesy R. Wright

Thermal monitoring approachesMethod 1: Brute force• Acquire, enhance and manually inspect the imagesMODIS band 22 (3.959 µm)Courtesy R. Wright

Thermal monitoring approachesMethod 2: Simple thresholding of the 4 mm radiance signal• Pixels with a 4 µm radiance > pre-determined threshold are classified as hot-spots•Totally insensitive to variations in ambient background temperature (season, geography…)• We need methods that account for variation of non-volcanic sources of scene radianceCourtesy R. Wright

Thermal monitoring approachesMethod 3: The Spectral Comparison MethodBT λ =C 2λln[1+ C 1 /(λ 5 L )]• Calculate T for each pixel• Automatically accounts for variance in ambient background• Flag pixel as a ‘hot-spot’ pixel if T > chosen threshold• Detects sub-pixel temperature ‘contrasts’, BUT….• Needs to include more checks to avoid returning ‘false positives’caused by cloud edges, non-uniform surface emissivity,atmospheric transmissivity….)Courtesy R. Wright

Thermal monitoring approachesMethod 4: A multi-temporal approach• Pergola et al. (2004) use a multi-temporal approach at Etna and StromboliT 4 (x,y,t) =T 4 (x,y,t) – T 4ref (x,y)σT 4 (x,y)• ‘Stack’ co-registered images of an area ofinterest• Characterise the thermal ‘behaviour’ ofeach pixel over an extended period oftime• Hot-spots identified when a pixel beginsto behave (thermally) ‘differently’ than ithas in the past• Great potential for detecting lowtemperature events Courtesy R. Wright

Thermal monitoring approachesWhat kind of activity can we detect?• Ability to detect the thermal emission associated with volcanic activity depends on:• The temperature of the lava/process• The area it covers• Its longevityEasierBasaltic lava flowsBasaltic lava lakesBlock lava flowsLava domesHarderStrombolian activityPhreatic activityPhreatomagmatic activityFumarolic activity

Thermal monitoring approachesCase study 1: Cycles of dome growth at Popocatepetl• Dome growth resumed at Popocatepetl in 1996• Satellite remote sensing only method useful for routine observations of the crater interior• GOES images summit crater once every 15 minutes• High temporal but low spatial resolution: what can we learn?Courtesy R. Wright

Thermal monitoring approachesCase study 1: Dome growth at Popo• 10 × 10 kernal centred at Popo’s summit• Record the peak radiance from the group (P r ) and the mean of the remainder (B r )• In the absence of any time-independent forcing mechanism, P r and B r should be well correlatedCourtesy R. WrightWright et al., 2002

Thermal monitoring approachesCase study 1: Dome growth at Popo• However, a volcanic radiance source, radiance from which is time-independent will cause P rand B r to de-couple• Use adjacent inactive volcano to normalise for environmental effects• Easy to identify volcanic activity in GOES radiance time-seriesCourtesy R. WrightWright et al., 2002

Thermal monitoring approachesCase study 1: Dome growth at Popo• Elevated GOES radiancecoincides temporally with periodsof heightened explosivity of thedome• Periods of heightened explosivityfollow substantial decreases inSO 2 flux• Restricted degassing =overpressure = explosionsCourtesy R. WrightWright et al., 2002

Thermal monitoring approachesCase study 2: Dome growth at LascarWooster and Rothery, 1997• Cyclic activity described in terms of generation of overpressure within the dome due todegassing induced decreases in permeability and compaction• Satellite measurements of radiance corroborate physical modelCourtesy R. Wright

Thermal monitoring approachesCase study 3: Estimating lava eruption ratesHarris et al, 1997• Very easy to detect lava flows• Spectral radiance from the flowsurface is related to the area oflava at a given temperaturewithin the field of view• In other words….the higher theeruption rate the greater arealava will be able to spread beforeit cools to a given temperature,and the higher the correspondingat-satellite radiance will be• Pieri and Baloga (1986)• Harris et al. (1997)• Wright et al. (2001)Courtesy R. Wright

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

MODVOLCWhat does it do? Detects high temperature pixels (from volcanoes, vegetationfires and industrial activity) in MODIS imagery. MODIS images every spot onthe Earth about twice a day.Who developed it? Rob Wright, Luke Flynn and others at Hawaii Institute ofGeophysics and Planetology, University of HawaiiWright R, Flynn LP, Garbeil H, Harris AJL, & Pilger E. 2004. MODVOLC: near-real-timethermal monitoring of global volcanism. Journal of Volcanology and GeothermalResearch, 135, 29-49.Where are the data?http://modis.higp.hawaii.edu

MODVOLCHow does it work? Uses a spectral comparison approach (shortwave IR minuslongwave IR) to isolate high temperature pixels. Spectral comparison result isnormalized to reduce false positives.Normalized temperature index:NTI =Band 22 – Band 32Band 22 + Band 32Band 22: 4 micronsBand 32: 12 micronsIf NTI is greater than -0.8, pixel is tagged as alert pixel

MODVOLC

MODVOLCStrengths:• Daily coverage of every volcano on Earth• Easy to use interface• Free, available over internetLimitations:• Threshold is high: can only detect significant eruptive activity• Threshold is high: can only detect significant eruptive activity• Typically does not detect ash-rich activity – better suited for effusive eruptions• As with all low-resolution sensors: Sometimes difficult to discriminate brush firesfrom volcanic activity• As with all infrared systems: clouds

MODVOLC: strengths – worldwide coverage, easy to use interface

MODVOLC: limitations1) Discriminating volcanic activity from brush firesTwo things to consider:1) Proximity to knownactive vents2) Time of year

MODVOLC: limitationsBrush fires much more common in Central America in March-May2000-2005

MODVOLC: limitations2) Alerts may be seasonally biased, presumably due to cloud cover2000-2005

MODVOLC: limitations3) Eruptions can go undetected if any or all of the following are true:a) Eruption is short: consider repeat period of MODIS=12hoursb) Eruption is small: scale of eruptive products is minimalc) Eruption is ash-rich: ash plumes cool quicklyd) Eruption occurs underneath heavy cloud cover

MODVOLCPerformance in Central America, 2000-2007Eruptive activity detected at:1) Santiaguito: persistent dome extrusion2) Fuego: persistent effusive/explosive activity beginning Jan 20023) Pacaya: effusive/explosive activity starting in Jan 20054) Santa Ana (?): pre-eruptive intensification of fumarole field, 1 month before Oct 1, 2005phreatomagmatic eruption5) Arenal: persistent effusive/explosive activityEruptive activity missed at:1) Santa Ana: phreatomagmatic eruption on Oct 1, 20052) San Miguel: ash emissions in 2000-2007, minor ash eruption Jan 2002.3) San Cristobal: minor explosive activity and ash emission4) Concepcion: minor explosive eruptions Jan 2005, Feb-Apr 20075) Telica: minor ash-gas eruption in early 2000, small eruption in Nov 20046) Masaya: visible incandescence in Santiago crater, minor explosion in 2001

MODVOLCTwo useful derivative products from MODVOLCExample 1: Santiaguito-Short-term fluctuations likely due tocloud cover- No clear trends in above data-Pixel size is 1 km – so normal errorin location is about 1 km- Example above consistent withcentral vent activity

MODVOLCTwo useful derivative products from MODVOLCExample 2: Fuego- Large scale fluctuations aresignificant- High amplitude peaks aresignificant – note log scale – andcorrelate with distal alerts-Distal alerts (up to 4.5 km) aresignificant – exceed 1 km pixel size- Central vent activity punctuated bylava/pyroclastic flows

MODVOLC- Those plots show total radiative heat loss – but MODVOLC only outputs Band22 (4 microns) and 32 (12 microns) radiance.- How can total radiative heat loss be determined from two wavelength-specificradiances?Solution: empirical relation for these particular bands, courtesy ofKaufmann et al. (1998)E = 4.34 x 10 -19 (T 4h8 -T 4b8 )E = radiative heat loss in MWT 4h = 4 micron temperature of alert pixelT 4b = 4 micron temperature of backgroundKaufman, Y.J., Justice, C.O., Flynn, L.P., Kendall, J.D., Prins, E.M., Giglio, L., Ward, D.E., Menzel, W.P., Setzer, A.W.,1998. Potential global fire monitoring from EOS-MODIS. J. Geophys.Res. 103, 32215– 32238.

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks

Univ. Hawaii GOES systemWhat does it do? Monitors radiance in GOES images, focusing on small areascentered around active volcanoes. GOES images every spot in the WesternHemisphere once every 15-30 minutes.Who developed it? Andy Harris others at Hawaii Institute of Geophysics andPlanetology, University of HawaiiHarris, A.J.L., Pilger, E., and Flynn, L.P., 2002. Web-based hot spot monitoring using GOES: what itis and how it works. Advances in Environmental Monitoring and Modeling, (http://www.kcl.ac.uk/ kis/schools/ hums/ geog/ advemm/ vol1no3.html) 1(3), 5-36.Harris, A.J.L., Pilger, E., Flynn, L.P., and Rowland, S.K., 2002. Real-Time Hot Spot Monitoring usingGOES: Case Studies from 1997-2000. Advances in Environmental Monitoring and Modeling,(http://www.kcl.ac.uk/kis/schools/hums/geog/advemm/vol1no3.html) 1(3), 134-151.Where are the data?http://goes.higp.hawaii.edu

Univ. Hawaii GOES system1) Example: Fuego volcano, GuatemalaSmithsonian GVN Weekly Bulletins, from INSIVUMEH:14-20 March 2007: Strombolian activity, lava flows, pyroclastic flows18-24 April 2007: Strombolian activity, lava overtopping crater, pyroclastic flows

Outline1) What can thermal monitoring tell us about volcanism?2) Basic principles of thermal monitoring3) Current satellite sensors and data available4) High-resolution versus low-resolution data5) Monitoring and detecting thermal anomalies6) MODVOLC: automated monitoring using MODIS7) Univ. Hawaii GOES system8) Final remarks