Embed Size (px)
Citation preview
Operational Forest Fire Monitoring in Brazil
Wilfrid Schroeder, M.Sc.
PROARCO - Fire Monitoring System
Brazilian Institute for the Environment and Natural Renewable Resources – IBAMA
IBAMA is the Major Environmental Agency in Brazil
About us...
Local O ff ices
S tate Agencies
W here w e are
M in istry of Env ironm ent
Brazilian P residency
Responsible for Forestry, Animal life, fishery, etc.. One of the Primary Goal is to Manage and Protect
the Brazilian Legal Amazon
The Challenge:
Large land area requiring wide scale monitoring system
Little or no access from surface: observations need to be made from above
Illegal logging activities going on over remote areas New land areas being created using fire as a tool
for clearing fields Large number of vegetation fires
The Brazilian Amazon
Total Area: 5.2 Million km2
Number of States Covered: 9
The Arc of Deforestation
Total Area: 1.6 Million km2
Number of States Covered: 7
The Beginning
Late 80’s – Start using AVHRR’s afternoon pass– Technical cooperation with INPE
Problems with detection algorithm– High number of spurious fires detected
Limited field inspection– Few satellite hot spot coordinates visited
The Need
January-March 1998 – The Great Roraima Forest Fire– Little operational capacity at that moment
prevented early detection and combat Operational Fire Monitoring Facility made
necessary– Pressure from the international community
July 1998 – The PROARCO system was established
The Concept
Intensive use of Remote Sensing and GIS technologies for fire monitoring
Use of meteorological data for fire risk assessment
Quick access to reports/bulletins- providing near real time data through internet, fax, and vehicles with satellite communication capability
Increase law enforcement activities
Have the local communities involved
Remote Sensing Fires
System based on previous AVHRR use experience
Detection algorithm experiencing constant improvement
Use of evening overpass (NOAA-12) – to avoid saturation from bright surfaces
Satellite hot spot data being used for field inspections based on different alert levels (green and yellow)– Hot spot location and persistence criteria
Fixed threshold method– Adjusting limits through histogram analyses– Trial and error
AVHRR Channel 3– Separating all potential fires through saturation
AVHRR Channel 1, 2 and 4:– Eliminating bright targets (clouds, water bodies,
bare soils,…)
Algorithm Basics
Overall Performance
Number of spurious fires greatly reduced
Overall Performance
Poor image navigation are still noticed occasionally
Day to day variation as a limiting factor
Overall Performance
0
50
100
150
200
250
182 186 190 194 198 202 206 210 214 218 222 226 230 234 238
Julian Day
Hot
Spo
ts
0
10
20
30
40
50
60
70
80
90
Clo
ud
Cov
er (
%)
NOAA12 Clouds
NOAA-12 July-August 2001
Overall Performance
Image Acquisition Problems
Courtesy of INPE
Overall Performance
Resulting Spurious Fires
Overall Performance
Similar Effects Affecting the NDVI
CPTEC/INPE
Zoomed area showing a large number of false green pixels
Overall Performance
Worth Mentioning – NOAA/AVHRR
Detection algorithm performing well
Image navigation still requires operator’s hands-on
Pixel distortion towards the edge of the image reduces detection capacity and affects hot spot statistics
Image acquisition characteristics affect the quality of derived products
Need for improvement
July 2000 – Implementation of CIRA’s RAMSDIS system based on GOES-8 data
Cloud Masking
Potential Fires
Tb4 > 2ºC
Day: Tb2 > 17ºC
1 2 3
4 X 5
6 7 8
Night: Tb2 > 41ºC
Statistics
Sunglint Model
Persistence
GOES Fire Detection Algorithm
Improved monitoring capability (every 30min) Reduced Response Time
Need for Improvement
Satellite data quality assessment facilitated
Need for Improvement
Great results from visual image interpretation
Need for Improvement
Northern Sectors
Southern Sector
Poor results from automated fire detection algorithm
Need for Improvement
Need for Improvement
Persistence Check
Need for Improvement
Worth Mentioning – GOES
Hot spot location errors are found to be in the 2km range
Visual image interpretation has been able to detect 100% of the major fires in National Parks all over Brazil
Response time is averaging 2 hours at most cases Coincident meteorological analyses helps planning
fire combat management in near real time Constant image acquisition geometry Coarse spatial resolution introduces high number of
spurious fires Automated detection is still of limited use
A New Era
July 2001 – MODIS hot spot data via ftp access
September 2001 – visual in-flight inspection of MODIS hot spot coordinates showing great results
A New Era
Rapid Response System images used as a confirmation
A New Era
Where you see smoke there will be a fire!! Courtesy of NASA
A New Era
Day to day variation also observed
0
20
40
60
80
100
120
140
160
180
182 186 190 194 198 202 206 210 214 218 222 226 230 234 238
Julian Day
Hot
Spo
ts
MODIS July-August 2001
A New Era
Worth Mentioning – MODIS
Hot spot location errors are found to be in the 250m range
Coincident high resolution visible images favors fire confirmation during day time
Pixel distortion creates similar problems observed with NOAA/AVHRR – what is made worst by non-overlapping images near the equator
Keep on Moving
January 2002 – DMSP OLS data made available through NGDC / C. Elvidge et al.
Good image navigation
Keep on Moving
Cities
State Boundaries
Noise from the South Atlantic Magnetic Anomaly
Keep on Moving
Courtesy of NASAMulti-angle Imaging SpectroRadiometer (MISR) Instrument aboard NASA's Terra Spacecraft
Keep on Moving
Spurious Fires Detected
Fire detection requiring operator’s hands on Stable lights file outdate as a limiting factor
Keep on Moving
Keep on Moving
Worth Mentioning – DMSP
Good correlation with NOAA/AVHRR
Sources of contamination limits detection capacity to larger fires (increasing omission error by the use of more restrictive thresholds)
Image acquisition time does not match fire peak activity hours
Stable lights file must be updated on a regular basis
Data QA
Helicopters and small aircrafts are used to field inspect the hot spot coordinates, feeding back the monitoring system with valuable information for fine tuning the satellite fire detection algorithms and methods
Airborne sensors are used during specific satellite data validation campaigns
Satellite data inter-comparison helps identifying commission/omission errors and assessing image navigation problems
Airborne satellite data validation campaigns
Data QA
Prescribed Burn at IBGE Reserve in Brasília – September 2000
Data QA
Visible Band
Forest Mapper Instrument
IR (8.55 m)
Fire Mapper Instrument
Data QA
Agricultural Burning in a Cerrado Area (savana) in the State of Tocantins - September 2000
Visible Band
Forest Mapper Instrument
IR 8.55 m
Fire Mapper Instrument
IR sensors to be used onboard orbital platforms
Data QA
Infrared Spectral Imaging Radiometer (ISIR) Image over Namib Desert Acquired from Space Shuttle Discovery on 7 August 1997
Airborne System:
-Pair of Kodak MegaPlus digital cameras (Forest Mapper)
-IR Sensor (Fire Mapper)
Courtesy of NASA
SIVAM AircraftsData QA
Data QA
GIS system for satellite data ingestion
Final Considerations
Increasing spatial resolution (visible channels) allowed for visual confirmation of fires in the images (smoke plumes)
Improved navigation parameters reducing processing time (no GCP collection needed) and making field inspection easier
Increasing spectral resolution / mid-IR channel saturation facilitating fire/non fire discrimination
Latest Improvements
Final Considerations
Varying pixel size through image cross section imposes some significant limitations to hot spot data applicability (specially with polar orbiting spacecrafts)
Full global cover every 12 hours is imperative. Tropical areas are affected by little image overlapping between consecutive orbits
Geostationary automated hot spot detection suffers from low confidence problems caused by spatial resolution limitations
Remaining Points
Acknowledgements
INPE – CPTEC
United States Forest Service – USFS
CIRA – Colorado State University
NASA Goddard Space Flight Center
University of Maryland
National Geophysical Data Center
World Bank
