A LEXANDER K OLTUNOV GOES Early Fire Detection Project An Overview and Update [email protected]...
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ALEXANDER KOLTUNOV GOES Early Fire Detection Project An Overview and Update [email protected]Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis (Principal Investigator) TFRSAC-Spring 2013, NASA Ames, Monterrey Bay CA, 4/17/2013
A LEXANDER K OLTUNOV GOES Early Fire Detection Project An Overview and Update [email protected] Center for Spatial Technologies and Remote Sensing
A LEXANDER K OLTUNOV GOES Early Fire Detection Project An
Overview and Update [email protected] Center for Spatial
Technologies and Remote Sensing (CSTARS), University of California,
Davis (Principal Investigator) TFRSAC-Spring 2013, NASA Ames,
Monterrey Bay CA, 4/17/2013
Slide 2
GOES Early Fire Detection (GOES-EFD) System Detect new
incidents consistently within first 1-2 hours, preferably before
they are reported by conventional sources A low-cost and reliable
capacity for systematic rapid detection and initial confirmation of
new ignitions at regional level as a component of the USFS Active
Fire Mapping program run by the FS RSAC complement existing related
products (WF- ABBA, MODIS/VIIRS Active Fire ) 2
Slide 3
GOES-EFD system integration within USFS Active Fire Mapping
program Provide standardized geospatial EFD products seasonal
regional Integrate EFD into existing decision-making environment at
dispatch centers and GACCs EFD to improve situational awareness,
response planning, confirmation for fires reported via other
sources 3
Slide 4
GOES-EFD Algorithm, Software, and Utility Programs GOES-EFD
Algorithm, Software, and Utility Programs GOES-EFD effort:
Structure and Participants GOES Images Landsat Images CAL FIRE Los
Angeles County Fire Department (LACoFD) San-Bernardino National
Forest Federal Interagency Communications Center (FICC) Supporting
End-User Partners UC Davis FS RSAC CSUMB Developers New ignition
locations Detection confidence Metadata per incident Performance
stats over previous fire seasons New ignition locations Detection
confidence Metadata per incident Performance stats over previous
fire seasons Output Products Run @ RSAC and NASA Ames Feedback,
Requirements, Evaluations, Integration R&D, Implementation,
Validation, Integration USDA FS UC Davis DHS Sponsors RSAC: Format,
Merge, Distribute to First Responders Developers Team: UCD: Alex
Koltunov, Wei Li, Elaine Prins, Susan Ustin RSAC: Brad Quayle,
Brian Schwind CSUMB: Vince Ambrosia 4
Slide 5
Where are we today? Alpha-version (GOES-EFD ver. 0.2)
readyAlpha-version (GOES-EFD ver. 0.2) ready simulated real-time
mode, simulated real-time mode, mainly Matlab implementation;
mainly Matlab implementation; Case studies in CA indicate:Case
studies in CA indicate: faster initial detection than the
operational satellite algorithm faster initial detection than the
operational satellite algorithm potential to provide the earliest
alarm, time and again potential to provide the earliest alarm, time
and again Algorithm optimizations and tests underway (as resources
permit)Algorithm optimizations and tests underway (as resources
permit) GOES-EFD ver.03 near completion (July 2013)GOES-EFD ver.03
near completion (July 2013) with ~35% fewer false alarms with ~35%
fewer false alarms 5
Slide 6
2013-15: Major development-test iterations, implementation, and
integration: complete the GOES-EFD -version2013-15: Major
development-test iterations, implementation, and integration:
complete the GOES-EFD -version 2015: Deployment of GOES-EFD- at
USFS RSAC as a component of the FS Active Fire Mapping Program2015:
Deployment of GOES-EFD- at USFS RSAC as a component of the FS
Active Fire Mapping Program 2015-2016: Near-real-time delivery to
participating users;2015-2016: Near-real-time delivery to
participating users; initial training and evaluation by
participating users user feedback and performance documentation
follow-up optimizations 2017: Post-Deployment system maintenance
and enhancement2017: Post-Deployment system maintenance and
enhancement 2016-2017: Adaptation to GOES-R Advance Baseline
Imager2016-2017: Adaptation to GOES-R Advance Baseline Imager
GOES-EFD: Intended Schedule 6
Slide 7
Geostationary Satellites (GOES East/West): Frequent, Low-Cost
Imaging of Vast Territories GOES Imager (NOAA): Viewing geometry
fixed Viewing geometry fixed Visible + Thermal Infrared (TIR)
images Visible + Thermal Infrared (TIR) images @ ~15-30 min step
(5-min during Rapid Scan) @ ~15-30 min step (5-min during Rapid
Scan) Effective TIR pixel size ~ 6 x 4 km over CA Effective TIR
pixel size ~ 6 x 4 km over CA GOES-WestGOES-East 7
Slide 8
Active Fire Monitoring vs. Early Fire Detection *Wildfire
Automated Biomass Burning Algorithm Active Fire MonitoringEarly
Fire Detection Maximize % of detected burning pixelsMaximize % of
detected new fire incidents (ignitions) Minimize % of false fire
pixelsMinimize the number of false new incidents (alarms) Estimate
flaming area, temperature, etc.Minimize time to initial detection
of an incident Perform consistently all year-round globally (e.g.
for comparative studies) Optimize for fire season and a chosen
surveyed scene GOES WF-ABBA, MODIS / VIIRS Active Fire, AVHRR
Primary Objectives are Related but Rather Different: GOES-EFD and
So Are the Optimal Algorithms 8
Slide 9
GOES-EFD is a tool specifically optimized for the objectives of
Early Detection 9
Slide 10
4 km background pixel fire pixel Burning Area Physical Basis
for Infrared Fire Detection T=700 K T=600 K T=500 K Radiance
Thermal IR 4 m 12 m Blackbody Spectrum T=320 K 4 km Plancks Law:
Radiance ( ) = B ( , T ) wavelength temperature Primary regions
used for detection: Primary regions used for detection: Short-wave
TIR (3 - 5 m) Long-wave TIR(10 - 12 m) fire R>>R fire R 4 m
>> R 11 m soil RR soil R 4 m ~ R 11 m 10
Slide 11
Annette fire: GOES 4- m Band vs. 11- m Band low high 4 m 11 m
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Slide 12
GOES-EFD algorithmic principle: merge Temporal + Contextual
information Database of Optimal Basis Images Training Stage
Detection Stage Inspection Image Select Images Compute Parameters
Combine Past Images compare Pre-processing Post-processing
Reference Image Koltunov, Ben-Dor, & Ustin (2009) Int J of Rem
Sens Koltunov & Ustin S.L. (2007) Rem Sens Environ
Multitemporal background prediction by Dynamic Detection Model:
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Slide 13
GOES-EFD ver. 0.2: Training and Preprocessing 13
Slide 14
GOES-EFD ver. 0.2: Detection Stage 14
Slide 15
Pixel-wise Unfiltered Fire Mask Fire Pixels Rejected Thermal
Anomalies Cloud Snow 15
Slide 16
From Pixels to Events (potential incidents) GOES-EFD target
objects are New Incidents (multi-pixel, multi-frame objects) past
frame current frame high low Fire Confidence An old event: do not
report/ report as re-detected A new event: report this event Event
Tracker: Analyze the temporal evolution of spatially connected
groups of fire pixels 16
Slide 17
Retrospective Assessment of Incident Detection Timeliness and
Accuracy Official wildfire incident records + Landsat-based burn
detection Koltunov A., Ustin, S. L., Prins, E (2012) On timeliness
and accuracy of wildfire detection by the GOES WF-ABBA algorithm
over California during the 2006 fire season, Remote Sensing of
Environment, v.127: 194-209 Not a trivial problem: Official records
are incomplete High resolution imagery is infrequent Very different
from validating an Active Fire Product While any kind of error in
the database is possible, not all kinds of errors are equally
probable Derive useful and reliable performance measures, despite
uncertainties and biases in truth data 17
Slide 18
Initial Experiment with fire season 2006 Central California
Central California Detection Period: 40 days; 2852 images: Aug 3
Oct 1 at ~20-min time step on average. -- Substantial Cloud Cover
Wildfire Incidents* Used: Large (>2 ha final size) wildfires; CA
only CA only * Used wildfire incident databases from: California
Department of Forestry and Fire Protection (CAL FIRE) Geospatial
Multi-Agency Coordination (GeoMAC) group Include wildfire incident
reports from: USFS, BLM, NPS, CAL FIRE, et al. Sample #1: 13 fires
with known initial report HOUR Sample #2: 25 fires with known
initial report DATE 18
Slide 19
Detection latency statistics: GOES-EFD ver.02 Detection latency
relative to initial report from conventional sources perfect
algorithm 19
Slide 20
Performance statistics: GOES-EFD ver.02 Performance statistics:
GOES-EFD ver.02 GOES-EFD rapid GOES-EFD regular GOES-EFD @30min
WFABBA @30min Detected incidents for 13 res with recorded report
hour Detected in < 1 hour11/1310/13 7/13 Detected before
reported4/13 3/132/13 Total latency reduction 216 Min 142 min 105
min 45 min for 25 res with recorded report date Detected in < 12
hours16/2515/25 11/25 False / non-wildfire incidents up to 784up to
7938 to 5339 to 55 GOES-EFD tends to detect fires earlier than
WF-ABBA GOES-EFD can provide the earliest alarm 20
Slide 21
Recent and ongoing activities toward GOES-EFD ver.03 Targeting
false positives due to:Targeting false positives due to: Motion of
undetected cloud pixels Sub-optimal image registration across time
Analyzing spatial gradients and spatial context of temporal
changeAnalyzing spatial gradients and spatial context of temporal
change Correlation between spatial connectivity, size, and
magnitude of thermal anomaly pixels? Spatial filtering of the
residuals after multitemporal anomaly detection of Long-wave TIR?
Second-dominant image motion and local misalignment tests Preparing
a large-area testPreparing a large-area test 21
Slide 22
Incidents with wrong combinations of Spatial, Spectral, and
Temporal features eliminated ~ 21% of false new incidents (at error
prob.< 0.1%)eliminated ~ 21% of false new incidents (at error
prob.< 0.1%) Rejected False Alarm True (wildfire) Event BT-4m
DDM Background Prediction Residual BT-4m DDM Background Prediction
Residual 22
Slide 23
Laplacian Spatio-Temporal Filters of Long-wave Thermal Band
eliminated ~ 17% of false new incidents (at error prob.<
0.1%)eliminated ~ 17% of false new incidents (at error prob.<
0.1%) 23
Slide 24
Improved Performance statistics: GOES-EFD ver.03 GOES-EFD
regular WFABBA @30min Detected incidents for 13 res with recorded
report hour Detected in < 1 hour10/137/13 Detected before
reported4/132/13 Total latency reduction 142 min 45 min for 25 res
with recorded report date Detected in < 12 hours15/2511/25 False
/ non-wildfire incidents 79 55 51 eliminated ~ 35% of false new
incidents (at error prob.< 0.2%)eliminated ~ 35% of false new
incidents (at error prob.< 0.2%) 24
Slide 25
GOES-R Advanced Baseline Imager (2016) Full disk coverage:
every 15 minutesFull disk coverage: every 15 minutes Continental US
coverage: every 5 minutes.Continental US coverage: every 5 minutes.
Spatial resolution : 2 km in TIRSpatial resolution : 2 km in TIR A
new channel at: 10.3 m.A new channel at: 10.3 m. Fewer saturated
pixelsFewer saturated pixels When GOES-R is available: Mature, well
tested GOES- EFD systemMature, well tested GOES- EFD system
EFD-prepared, EFD-friendly user communityEFD-prepared, EFD-friendly
user community acceptance by scientific communityacceptance by
scientific community 25
Slide 26
Conclusions We are moving forward, as resources permitWe are
moving forward, as resources permit Prospective users:Prospective
users: when you say you need Earlier Detection you are helping
speed up the development ! Stay tuned 26
Slide 27
2013-15: Major development-test iterations, implementation, and
integration: complete the GOES-EFD -version2013-15: Major
development-test iterations, implementation, and integration:
complete the GOES-EFD -version 2015: Deployment of GOES-EFD- at
USFS RSAC as a component of the FS Active Fire Mapping Program2015:
Deployment of GOES-EFD- at USFS RSAC as a component of the FS
Active Fire Mapping Program 2015-2016: Near-real-time delivery to
participating users;2015-2016: Near-real-time delivery to
participating users; initial training and evaluation by
participating users user feedback and performance documentation
follow-up optimizations 2017: Post-Deployment system maintenance
and enhancement2017: Post-Deployment system maintenance and
enhancement 2016-2017: Adaptation to GOES-R Advance Baseline
Imager2016-2017: Adaptation to GOES-R Advance Baseline Imager
GOES-EFD: Intended Schedule 27
Slide 28
We gratefully acknowledge Kevin Guerrero, Mark Rosenberg CAL
FIRE Jim CrawfordLA County Fire Dept. Shawna Legarza, FICC Quinn
Hart,UC Davis Mui Lay, George SheerUC Davis Dedicated Support by :
and personally thank 19 UC Davis GOES Receiver infrastructure and
data are provided by CIMIS (California Irrigation Management
Information System) program CIMIS (California Irrigation Management
Information System) program http://wwwcimis.water.ca.gov/cimis
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Slide 32
New burn detection in Landsat pairs June 25 Zoom 1 July 27 dNBR
dNBRA no true burns Zoom 2: dNBR dNBRA July 27 June 25 one true
burn dNBR dNBRA RGB= bands (7,4,3) Path-Row: 43-34 zooms What if,
there is a new burn near suspected false positive? What if, no new
burns found near suspected false positive? 32
Slide 33
Example: Rejecting 1 confident fire pixel of the 4-pixel
Annette fire (2006/8/3) BT-4m DDM Background Prediction Residual
fr.671 t=2006.08.01.1645 cold anomaly hot anomaly dBT4-11 DDM
Background Prediction Residual fr.671 t=2006.08.01.1645 33
Slide 34
Automatic Thermal Image Registration Automatic Thermal Image
Registration Radiance ~4 m original registered 34