Quantifying thermal activity from space
using daytime SWIR imagery
Sam Murphy
University of Campinas, Brazil
fire and volcano hazards
• direct heat exposure
• asphyxiation and other respiratory problems
• loss of agricultural and forest landscapes
• damage to urban environments
• liberation of toxic elements and compounds into the atmosphere
global distribution of hotspots
global distribution of hotspots
global distribution of volcanoes
volcanic plumes
Sentinel 2
high temporal resoluton
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ephemeral events
fast response
change detection and precurors
monitoring dynamic activity through time
Golden Opportunity
first global, high spatial resolution monitoring system of thermally active surfaces
current state-of-theart
MODIS
• 1 km
• 0.5 days
detection
• basis of thermal monitoring systems
• must be automated for global monitoring
APPLICATIONS
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size, geometry and location
detection of new (previously unknown) activity
calibration of MODIS-type data products
quantification thermal activity
detection algorithm
• Giglio et al. (2008), RSE
• Landsat 8 data used
• applicable to Sentinel 2 data
detection algorithm
• Giglio et al. (2008), RSE
• Landsat 8 data used
• applicable to Sentinel 2 data
mask obvious water pixels
identify obvious hot pixels
identify candidate hot pixels
background characterization
contextual analysis
water masks
water masks
water masks
improved water mask
Threshold
AND
Solar Elevation Angle
𝑏𝑙𝑢𝑒
> 1.5
𝑔𝑟𝑒𝑒𝑛
improved obvious fire pixel detection
R =2.2 μm, G = 1.61 μm, B = 0.87 μm
hot pixel classification map
R =2.2 μm, G = 1.61 μm, B = 0.87 μ m
greyscale = VIS
red overlay = hot pixels
quantification
• all quantification is limited to within dynamic range
• fires and volcanoes can saturated SWIR
• even so, decades of work on quantification
active radiance
𝑛
𝐿λ,𝑎𝑐𝑡𝑖𝑣𝑒 = τλ
𝑓𝑖 𝐿(λ,𝑇𝑖 )
𝑖=1
daytime radiance
𝐿λ,𝑎𝑐𝑡𝑖𝑣𝑒 = 𝐿λ,𝑠𝑒𝑛𝑠𝑜𝑟 − 𝐿λ,𝑝𝑎𝑠𝑠𝑖𝑣𝑒
passive radiance
𝐿λ,𝑝𝑎𝑠𝑠𝑖𝑣𝑒 = τλ ρλ 𝐿λ,𝐷 + 𝐿λ,𝑈
passive
radiance
transmitted
reflected
downwelling
radiance
upwelling
radiance
passive radiance
𝐿λ,𝑝𝑎𝑠𝑠𝑖𝑣𝑒 = τλ ρλ 𝐿λ,𝐷 + 𝐿λ,𝑈
passive
radiance
transmitted
reflected
downwelling
radiance
upwelling
radiance
passive radiance
𝐿λ,𝑝𝑎𝑠𝑠𝑖𝑣𝑒 = τλ ρλ 𝐿λ,𝐷 + 𝐿λ,𝑈
passive
radiance
transmitted
reflected
downwelling
radiance
upwelling
radiance
calculate active radiance
1. detect anomalously hot pixels
2. remove hot pixels from a given spectral radiance image
3. interpolate passive spectral radiance from non-active pixels
𝐿λ,𝑎𝑐𝑡𝑖𝑣𝑒 = 𝐿λ,𝑠𝑒𝑛𝑠𝑜𝑟 − 𝐿λ,𝐸[𝑝𝑎𝑠𝑠𝑖𝑣𝑒]
metrics of thermal activity
1. radiance is not an ideal metric
• depends on spectral position & width of waveband
• not readily input into models of thermal activity
2. temperature is not an ideal metric
• hot surfaces tend to be thermally heterogeneous
• subpixel temperatures not constrainable
3. radiante flux is a good option
• measures a physical characteristic of the surface
• is not sensor dependent
• can be contrained with just two SWIR wavebands
or use metrics that describe
detection classification map
calculate radiante flux
calculate radiante flux
subpixel temperature distributions
are sensitive to exact shape
calculate radiante flux
subpixel temperature distributions
are sensitive to exact shape
radiante flux depends on the integral
Radiant flux example – Stromboli
Volcano
Total Radiant Flux [MW]
Radiant flux example – Stromboli
Volcano
Time
Conclusion
• can detect thermally anomalous pixels in daytime imagery
• can retrieve active radiance spectra
• can calculate radiant flux
• synergy with Landsat 8
• Sentinel 2 can be used to create the world’s first:
• operational
• high-spatial resolution
• global monitoring system
Thank you
Merci
Grazie
Obrigado