Transcript Document

High-temporal resolution thermal
volcano monitoring from space:
a review of existing techniques
Robert Wright
Hawai’i Institute of Geophysics and Planetology
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
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……..?
Physical principles
Ll =
c1l-5
exp(c2/lT)-1
•L4 ~T4
•L12 ~ T2
• 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
Sub-pixel-sized hot-spots
Image: Clive Oppeheiner
300 K (100%)
300 K (99.95%)
850 K (0.05%)
@ 4 mm, Ll = 0.4 Wm-2sr-1mm-1
@ 11 mm, Ll = 9.5 Wm-2sr-1mm-1
@ 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
34 m
Sub-pixel-sized hot-spots
• High-temperature surfaces are easily distinguishable from surfaces at ambient temperatures
when imaged at short and long wavelengths
‘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
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?
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
Approaches for automatic detection of volcanic
thermal unrest in low spatial
resolution satellite data
Brute force
• Acquire, enhance and manually inspect the images
MODIS band 22 (3.959 mm)
‘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
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
The Spectral Comparison Method
BTl =
C2
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….)
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
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?
‘Raw’ 4 mm
daytime data
Corrected 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
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
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
A multi-temporal approach
• Pergola et al. (2004) use a multi-temporal approach at Etna and Stromboli
T4(x,y,t) =
T4(x,y,t) – T4ref(x,y)
sT4(x,y)
• ‘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
Some case studies describing applications of
the data
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
Basaltic lava lakes
Block lava flows
Strombolian activity
Phreatic activity
Phreatomagmatic activity
Fumarolic activity
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?
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
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
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
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
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)
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