High-temporal resolution thermal volcano monitoring from space: a review of existing techniques

1. High-temporal resolution thermal volcano monitoring from space: a review of existing techniques Robert Wright Hawai’i Institute of Geophysics and Planetology

2. Lecture topics What do we want from a satellite thermal monitoring system? Underlying principles of hot-spot detection Some existing approaches to hot-spot detection Examples

3. Some requirements for a space-based thermal volcano monitoring system • Be able to detect high-temperature bodies at the decimeter scale • Depend on cost-free data • Make repetitive, frequent observations (eruption intensity fluctuates on < hourly time scales) • Minimise false positives • Minimise transfer of actual image data • Objective • Communicate results ‘rapidly’ • Any others you can think of……..?

4. Physical principles • L4 ~T4 • L12 ~ T2 c1l-5 Ll = exp(c2/lT)-1 • As the temperature of the emitting surface increases, the amount of radiance at all wavelengths increases and the wavelength of maximum emission shifts to shorter wavelengths • Short-wave infrared radiance data are great for detecting and quantifying hot targets

5. Sub-pixel-sized hot-spots Image: Clive Oppeheiner 34 m 300 K (100%) @ 4 mm, Ll = 0.4 Wm-2sr-1mm-1 @ 11 mm, Ll = 9.5 Wm-2sr-1mm-1 300 K (99.95%) 850 K (0.05%) @ 4 mm, Ll = 1.3 Wm-2sr-1mm-1 @ 11 mm, Ll = 9.6 Wm-2sr-1mm-1 • High-temperature radiators are apparent at short wavelengths even if they are much smaller than the spatial resolution of the imaging system, which they often are…. • 4 mm data are very important: work-horse of low resolution thermal monitoring systems

6. Sub-pixel-sized hot-spots • High-temperature surfaces are easily distinguishable from surfaces at ambient temperatures when imaged at short and long wavelengths

7. ‘High’ versus ‘low’ spatial resolution data ATSR – 1 km pixels • Many space-based resources available that acquire data in the important 4 and 12 mm bandpasses • High spatial resolution data can detect smaller, less intense thermal anomalies, but….. • Their low temporal resolution, low duty cycle, data volume make them (largely) impractical as volcano monitoring tools (but possible OK as a volcano “surveying tool”; see work of Rick Wessels) • Low spatial, high temporal resolution environmental/meteorological satellites are the best bet Landsat TM – 30 m pixels

8. Temporal resolution • Temporal resolution very important for monitoring • Data frequency varies depending on whether the satellite is in geostationary or low-Earth orbit • GOES: geostationary: 7-30 minute repeat • AVHRR/MODIS/AVHRR: LEO: 12-24 hour repeat • Frequency at which data are acquired can be improved by launching more satellites • In the future……….highly elliptical orbits?

9. Sensors for hot-spot monitoring AVHRR: 4 and 12 mm channels (1 km pixels) Temporal resolution = 6 hours, global coverage GOES: 4 and 12 mm channels (4 km pixels) High temporal resolution = 7-30 mins, limited coverage, no coverage at high latitudes ATSR: 1.6, 4 and 12 mm channels, 1 km pixels Temporal resolution = 3 days, global coverage MODIS: 4 and 12 mm channels, 1 km pixels Temporal resolution = 24 hours, global coverage

10. Approaches for automatic detection of volcanic thermal unrest in low spatial resolution satellite data

11. Brute force • Acquire, enhance and manually inspect the images MODIS band 22 (3.959 mm)

12. ‘Brute-force’ • Not very practical for global/regional/small scale monitoring at high temporal resolution • Humans introduce bias and are not to be trusted • Need ‘non-interactive’ methods for identifying hot-spots

13. Simple thresholding of the 4 mm radiance signal • Pixels with a 4 mm 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 radiance

14. The Spectral Comparison Method C2 BTl = lln[1+ C1/(l5Ll)] • Calculate DT for each pixel • Automatically accounts for variance in ambient background • Flag pixel as a ‘hot-spot’ pixel if DT > 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….)

15. Contextual algorithms • Combine spectral AND spatial analysis • Each pixel in image treated as a “potential” hot-spot and its multi-spectral characteristics compared against adjacent non-hot-spot pixels. Thresholds are  less empirical and more scene dependent • A potential hot-spot is reclassified as an actual hot-spot if: • T4 > T4b + nsT4b • AND • DT > DTb + nsDTb • Detection does not rely on radiance threshold but does rely on s threshold • Neighbourhood operation – computationally intensive

16. Dealing with daytime data • The Earth emits AND reflects at 4 mm • Need to isolate the portion of the signal thermally emitted by the target • Spectral/contextual algorithms account for extra emitted energy • What about the reflected energy? Corrected 4 mm daytime data ‘Raw’ 4 mm daytime data • ‘Cold’ but ‘reflective’ surfaces can generate false positives (e.g. snow, sand) • Use the “mean” approach or the “per-pixel” approach L4corr = L4 – 0.0426 × L1.6

17. Dealing with daytime data • ‘Sun-glint’ – specular reflection anomaly that can produces ‘false positives’ • Identify ‘potential’ sun-glint pixels on the basis of sun-sensor geometry and exclude them qg < nº, where cosqg = cosqvcosqs sinqvsinqscosf

18. Nighttime short, short-wave infrared data • Wooster and Rothery (1997a,b); Wooster et al., (1997) • Night-time 1.6 mm data acquired by the Along-Track Scanning Radiometer • Only detects material at magmatic temperatures: makes thresholding very simple • Hopeless during the day due to contamination by reflected sunlight Wooster and Rothery, 1997

19. A multi-temporal approach T4(x,y,t) – T4ref(x,y) T4(x,y,t) = sT4(x,y) • Pergola et al. (2004) use a multi-temporal approach at Etna and Stromboli • ‘Stack’ co-registered images of an area of interest • Characterise the thermal ‘behaviour’ of each pixel over an extended period of time • Hot-spots identified when a pixel begins to behave (thermally) ‘differently’ than it has in the past • Great potential for detecting low temperature events

20. Some case studies describing applications of the data

21. What 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 longevity Easier Harder Basaltic lava flows Lava domes Strombolian activity Phreatic activity Phreatomagmatic activity Fumarolic activity Basaltic lava lakes Block lava flows

22. 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?

23. Dome growth at Popo • 10 × 10 kernal centred at Popo’s summit • Record the peak radiance from the group (Pr) and the mean of the remainder (Br) • In the absence of any time-independent forcing mechanism, Pr and Br should be well correlated Wright et al., 2002

24. Dome growth at Popo • However, a volcanic radiance source, radiance from which is time-independent will cause Pr and Br to de-couple • Use adjacent inactive volcano to normalise for environmental effects • Easy to identify volcanic activity in GOES radiance time-series Wright et al., 2002

25. Dome growth at Popo • Elevated GOES radiance coincides temporally with periods of heightened explosivity of the dome • Periods of heightened explosivity follow substantial decreases in SO2 flux • Restricted degassing = overpressure = explosions Wright et al., 2002

26. Dome growth at Lascar Wooster and Rothery, 1997 • Cyclic activity described in terms of generation of overpressure within the dome due to degassing induced decreases in permeability and compaction • Satellite measurements of radiance corroborate physical model

27. Estimating lava eruption rates Harris et al, 1997 • Very easy to detect lava flows • Spectral radiance from the flow surface is related to the area of lava at a given temperature within the field of view • In other words….the higher the eruption rate the greater area lava will be able to spread before it cools to a given temperature, and the higher the corresponding at-satellite radiance will be • Pieri and Baloga (1986) • Harris et al. (1997) • Wright et al. (2001)

28. Conclusions • Principles of satellite detection of volcanic hot-spots are well established and much work continues to be done in both the volcanological and wildfire communities • Many different “flavours” of hot-spot detection algorithms • Trade-off between detecting low intensity anomalies and false positives • An approach tailored to your volcano of interest is probably the best solution