Fire Detection and Mapping using Satellite Remote Sensing: Sensors, Systems, and Solutions

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Fire Detection and Mapping using Satellite Remote
Sensing Sensors, Systems, and Solutions

• Robert H. Fraser CCRS
• Robert Landry CCRS
• Ron Hall CFS
• Dieter Oertel DLR

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Rob Fraser Research Scientist Canada Centre for
Remote Sensing Natural Resources Canada
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Sensors and Systems for Active Fire Detection

• (A) NOAA/AVHRR
• Visible, NIR, MIR, 2 Thermal channels at 1 km
• Meteorological sensor, although workhorse for
  fire detection for 20 years
• Inexpensive to set up (most commonly used sensor
  for national fire monitoring systems)
• Channel 3 is shared in post N14 satellites (1.6
  ?m 3a operated during daytime, 3.7 ?m 3b during
  night)

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Sensors and Systems for Active Fire Detection

• (A) NOAA/AVHRR Products and Systems
• IGBP-DIS Global Fire Product (1992-1993)
• World Fire Web (1998-2001)
• NOAA NESDIS Operational Significant Event
  Imagery and SSD Experimental Fire Analysis page

22 World Fire Web HRPT stations
SSD Experimental Fire Analysis Page
NOAA Significant Event Imagery
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Sensors and Systems for Active Fire Detection
Fire M3 Regional Product over Quebec

• (A) NOAA/AVHRR Products and Systems
• Canadian Fire M3 System (NRCan)
• North American historical (1985-2001) fire
  mapping project (NASA LULUC)
• National AVHRR fire monitoring systems in Brazil,
  Indonesia, Mexico, Australia FIREWATCH, Finland
  VTT/ESA/FMI, Russia, and others

Brazil INPE
Australia Firewatch
Finland FireAlarm System
Russia Avialesookhrana
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Sensors and Systems for Active Fire Detection

• (B) ERS-2/ATSR-2
• Channels similar to AVHRR
• ESA/ESRIN created ATSR World Fire Atlas (1995-Jan
  2002 latest)
• Night time imagery
• Two algorithms based on 3.7 ?m thresholds
• Validated using a network approach

ATSR World Fire Atlas August 2001 ATSR
Algorithm 1
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Sensors and Systems for Active Fire Detection

• (C) GOES
• Geostationary perspective captures diurnal fire
  cycle
• Experimental Wildfire ABBA Fire Product (CIMSS
  and NOAA)
• Hotspot monitoring web site at Hawaii Inst.
  Geophysics

Wildfire ABBA Regional Overview July 9, 2002
Hawaii Hotspots from Int. of Geophysics
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Sensors and Systems for Active Fire Detection

• (D) DMSP OLS
• NOAA-NESDIS NGDC night time detection using
  visible channel and database of stable lights
• Provide near real time delivery of OLS data and
  algorithms to regional centers

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Sensors and Systems for Active Fire Detection

• (E) Terra and Aqua with MODIS
• Launched in 1999 aboard EOS/Terra, 2002 aboard
  Aqua
• Only sensor providing global daily fire products
  in near real-time
• Two channels specifically designed for fire
  detection (band 21 _at_ 4 ?m saturates at 450 K,
  band 31 at 11 ?m saturates at 400 K)
• Sensor will be used for fire detection,
  estimating rate of emissions, and
  smouldering/flaming ratio

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Sensors and Systems for Active Fire Detection

• (F) MODIS Fire Products and Systems
• Land Rapid Response
• (NASA GSFC / Umd / USFS)

Rapid Response (250m false color) Quebec July
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University of Maryland Web Fire Maps
July 7-9
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Sensors and Systems for Active Fire Detection

• (F) MODIS Fire Products and Systems
• Land Rapid Response
• at USFS RSAC

USFS Remote Sensing Application Center
Regional Cartographic Products
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Sensors and Systems for Active Fire Detection

• (F) MODIS Fire Products and Systems
• (iii) GEOMAC internet-based mapping tool designed
  for fire managers

GEOMAC Internet Map Server
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Sensors and Systems for Active Fire Detection
Other sensors FUEGO constellation BIRD
(DLR) Meteosat Second Generation (MSG) MTSAT-1R
Eventual Geostationary Coverage
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Outstanding Issues for Active Fire Detection

• Validation of satellite fire products
• AVHRR channel 3a/3b sharing
• Need information systems to archive hotspot data
  from various sensors/systems (e.g. a global
  hotspot clearinghouse building on efforts of NOAA
  Fire Detection Program and Global Fire Monitoring
  Centre)
• Continued research on active fire products
  contribution to emissions modelling

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Small Satellite Mission BIRD DLR Hot Spot Images

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Dieter Oertel Institute of Space Sensor
Technology and Planetary Exploration
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The BIRD Payload

• Payload platform of the flight model
• Total mass 30.2 kg

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Hot spots in Italy (BIRD, Nov. 5, 2001)
Equivalent fire temperature
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Hot spot detection by GOES and BIRD (Nov. 23,
2001)
GOES
BIRD
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BIRD MIR images of bush fires, Sydney area,
Australia obtained on a - January 4, 2002 b -
January 5, 2002 c - January 9, 2002
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Fragment of BIRD bush fire images (Australia,
January 2002)
Equivalent fire temperature
MIR, Jan. 9, 2002
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Typical characteristics of fire fronts (BIRD,
Australia, Jan. 5, 2002)
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Fire detection by MODIS and BIRD (Australia,
January 5, 2002)
MODIS Fire map
BIRD Fire map
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Industrial hot spots Munich area (BIRD, Jan. 29,
2002)
0.1 1 10 MW
MIR
Radiative energy release
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BIRD Detects Hot Spots in and around Munich

• Infrared Image of Munich region 29 of Jan.2002,
  local time 1010 h

Hot spot Wood waste that burned for several
hours (4m diameter, hot temperature) by Farmer J.
Kranz (written in his working diary)
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China coal seam fires (BIRD, January-February
2002)
Radiative energy release
MIR, Feb. 6, 2002
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Easter Fires (BIRD, Steiermark -Kaernten, Austria
March 30, 2002)
Energy release
MIR
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Industrial Hot Spots (BIRD, Ruhr area, Germany,
Mar. 28, 2002)
Thyssen T 530 K, A 2.4 Ha, E 101 MW
T 470 K, A 2.1 Ha, E 51 MW
Energy release
MIR
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USA Fires June 2002
BIRD MIR image obtained at night over New Mexico
/ Colorado on 17 June 2002. Swath width 190 km,
swath length 380 km. Fire are visible in the
upper part (red color) - probably in the San Juan
Natiotional Forest - which is about 290km
North-north-west of Albuquerque. Albuquerque is
shown in the image by blue color, using a 2 K
temperature thresholding to the background
temperature.
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USA Fires June 2002
The San Juan park fire analysis shows 620 MIR
pixels are occupied by fire. The MIR fire-pixel
brightness temperatures are between 300 and
400K. The Bi-spectral method allowed to retrieve
fire temperatures from 650 to 1000 K
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USA Fires June 2002
BIRD MIR image of the Denver / Pike Forest area
obtained 21 June 2002
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USA Fires June 2002
22 remaining small hot spots of the Pike Forest
fire could be identified in the MIR image of 21
June 2002
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VGT Scar Mapping Validation with Landsat-TM
Scar Mapping

• Robert Landry, Heather MacLeod, Don Raymond
• Canada Centre for Remote Sensing
• Ron Hall
• Canadian Forest Service

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Robert Landry Research Scientist Canada Centre
for Remote Sensing Natural Resoures Canada
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Virginia Hills burnt forest
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(No Transcript)
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Fire events distributionper Ecozone
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Burn area product validationExperimental design
Factor 1 Ecozone (6 classes) 4,5,6,9,12,15 Facto
r 2 Fire size (3 classes) 1) 200-400, 2)
400-600, 3) 600 ha) Factor 3 Fuel type (3
classes) 1) conifers, 2) deciduous, 3)
mixed Replicate minimum 3 replicates
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Burn area validationDataset characteristics

• Landsat-TM
• 1998,1999 - over 55 imagery
• Number of fires mapped (over 200 ha. single
  polygon detected only)
• 197 fires were mapped

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Burn area validation - Sampling dataset
Landsat-TM 1998,1999 - over 55
imagery Number of fires mapped - over 200
ha. - 197 fires were mapped
Landsat-TM dataset for SPOT-VGT mapping
validation
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Burn area product validationDeliverables

• Statistical reports
• UTM - total burn area, polygon size distribution
  (fragmentation)
• LCC - total burn area, fuel class distribution,
  ecozone distribution
• Burn vectors
• UTM - smooth vectors (e00)
• LCC - full resolution and 75 meters resampled
  (during UTM to LCC projection) for Canada-wide
  composite
• LCC database, all VGT and TM derived vector are
  compiled within the same ArcView project

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Burn area product validation where are we ?

• Data preparation
• Fire event selection based on experiment design
  (completed)
• Extract burn vectors from Landsat-TM scenes
  (completed)
• Compile statistics from TM and VGT (completed)
• Define and identify common fire event between TM
  and VGT
• Analysis of false alarms from VGT/TM (TBD)
• Identify problematic fires (TM and VGT)
  (completed)
• Reprocess (feedback loop) problematic fires
  (completed)
• Validation and modeling
• Definition of control fire sample set, and
  validation fire sample set
• Statistical analysis (regression, 3-factor ANOVA)
• Modeling analysis
• Modeling testing with the validation fire sample
  set (loop process)
• Results

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Burn area product validation
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Burn area product validationobservations

• Fire history increases ambiguities in the mapping
  process,
• Humid areas are difficult to map because of
  different fuel type,
• The algorithm maps complete burn, not partial
  burn... In deciduous area (regeneration within
  old fire), partial burn signature seems to
  dominate, so fragmentation becomes high so more
  problematic to map,
• False alarms detected from VGT and TM (beaches,
  recent depletions)

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Burn area product validationobservations (TM
related)

• within complete burnt area, TM NIR and Mid-IR
  spectral signatures seems to be correlated to
  stem density, and not necessarily to burn
  severity,
• time lapse between Landsat-TM acquisition and
  fire occurrence has significant impact on
  spectral signature, therefore on our ability to
  correlate the signature to severity,
• fire occurrence within the fire season has an
  impact on the observed signature responses
  (contribution of the fireweed),
• partial burn (lt50 crown burnt, with any
  combination of ground burnt) is challenging to
  assess (research ongoing)

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Conclusions

• Validation of SPOT-Vegetation with Landsat-TM for
  the fire seasons 1998 1999
• over 197 fires across Canada areas comprising
  six eco-zones and different fuel types,
• still under progress
• plans to present preliminary results (modelling)
  during the spring
• Fire scar mapping with SPOT-Vegetation will
  fulfil other requirements of the relevant
  federal, provincial, and forest management
  agencies
• FireM3 has demonstrated the feasibility of a
  complex end-to-end process from satellite
  acquisition to web-product, available in almost
  real time

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Conclusions

• Fire scar mapping with SPOT-Vegetation and the
  derived products will contribute to
• National Forest Information System (NFIS)
• The State of Canadas Forests Reporting

Next steps...

• Carbon emission estimate from fire
• through a pilot project...burn severity

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Defense Support Program (DSP) Satellites The
AeroSpace Corporation Source D. Pack et al.
Civilian Uses of Surveillance Satellites

• Features of DSP Satellites
• designed to track missile launches
• scans nearly entire earth every 10 seconds
• uses infra-red detectors
• dynamic range of sensor can measure wide range
• in fire intensity without sensor saturation
• not clear if system can be used to track
• non-USA fires

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Fire Intensity Plot from DSP Satellite of
Topanga-Malibu Fire Nov. 2, 1993 (source D. Pack
The Aerpspace Corp.)
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Diurnal Intensity from DSP Satellite of Burning
Across South Africa July 3, 1992 (source D. Pack
et al. The Aerospace Corp.)
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Thank You