THE OBSERVATIONS FROM SATELLITES TO HELP IN THE STRUGGLE AGAINST FIRES.
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THE OBSERVATIONS FROM SATELLITES TO HELP IN THE STRUGGLE AGAINST FIRES.
Romo, A., Casanova, J.L., Calle, A., and Sanz, J.LATUV - Remote Sensing Laboratory
University of Valladolid, SPAIN
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Index
•Introduction
•Fire’s phase and add value products from remote sensing data.
•LATUV installation.
•LATUV antennas.
•LATUV-MODIS processing chain.
•Remote sensing products.
•LATUV: some project.
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Introduction
•The use of space technologies provides a new perspective in the management of large event situations or natural disasters.
•In the particular case of fire, this needs to have in real time information about these.
•The information from satellites are ideal.
•LATUV from 1993, send different fire information layer to fire fighter authority.
•Here I present the different layer of information depending on the phase of the fire.
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Remote sensing products
AreaFire’s phase
Contextual spatial information
Infrastructures, towns, city, etc.
Land use, combustible
Structural parameter
Vulnerability
Structural risk
Prevention
Burned area
Burned area time evolution
Damage estimation
Damage evolutionPost-crisis
Fire line
Propagator model
Fire line evolution
Fire monitoring
Behavior prediction
Fire evolution
Crisis time
Fire alarm & Fire alarm
Active fire
Hot spots detection
Hot spot DDBBDetection
NDVI .vs. historical NDVI
Humidity, wind, etc
Fire risk
Vegetation stage
Meteorological products
Dynamic risk
Alerts
Fire’s phases and add value products from remote sensing data
MODIS data MSG, NOAA and FengYun data GFS Data
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Cold Room
Principal Laboratory
Robot Saver Cluster – 8 nodes
LATUV: Installations
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MODIS Antenna
NOAA & Fengyun & Seawifs Antenna
MSG Antenna
2 NOAA Antennas
LATUV: Antennas
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PGE Version Productos Descripción
PGE01 4.2.8.reproc MOD01 MOD03 Reconstrucción de la imagen (Level 1A), desconmutando los datos desde “Data Stream” (Level 0) y geolocaliza la posición de cada píxel.
PGE02 4.3.0 MOD02 Convierte las cuentas digitales (L1A) en valores de radiances (Level 1B) a través de los coeficientes de calibración.
PGE03 4.3.0 MOD35 MOD07 Genera los productos de mascara de nubes, productos atmosféricos y alerta de volcanes.
PGE04 4.2.2 MOD04 MOD05 Genera el producto de aerosoles y agua precipitable en la atmósfera.
PGE06 4.2.6 MOD06 Genera el producto de nubes
PGE11 4.0.10 MOD09 Cálculo de la reflectividad de las bandas 1-7 con corrección atmosférica.
PGE16 4.1.11 MOD11 PGE16 for TERRA runs the Level 2 and Level 3 Land Surface Temperature algorithms.
PGE30 4.0.1 MOD14 Calcula el producto de anomalías térmicas y detección de incendios.
PGE´s operativos en la actualidad en el LATUV Operative PGE's at present in the LATUV
LATUV MODIS processing chain•The LATUV’s MODIS processing chain is carry out through a NASA Institutional Algorithm.
•LATUV runs the next PGE.
•All PGE are running in a cluster with 8 nodes.
•HDFLook is used to project the final maps in equirectangular projection.
LATUV-MODIS processing chain
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LATUV processing areas.
LATUV processing areas
LATUV-MODIS processing chain
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Fires MODIS basic products
For each MODIS pass, LATUV uses to obtain the fire add value products:
NDVI
BT
band 21
Aggregate band 1
Aggregate band 2
Reflectivity band 2
+
MOD14 LST
BTband 31
LATUV-MODIS processing chain
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Remote sensing products: ALERTS
Vegetation stage
•This product is a report and it is composed by 8 maps and comments about these.
•LATUV estimated the average NDVI maps, the maximum and minimum NDVI maps (each 16 days) from historical MODIS data (2000-2004). These are our historical reference, the average, the better and the worse year.
•LATUV estimate the GREENESS index, the ratio and different between average and current NDVI maps and the historical NDVI tendency and current NDVI tendency.
•The spatial resolution is QKM.
•The product’s periodicity is twice/week.
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Example over Spain
Remote sensing products: ALERTS
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Meteorological products
Air Temperature ForecastAir Humidity Forecast
Wind Speed Forecast
The weather products are obtaining from GFS data. The weather forecast products were: ground temperature, humidity, accumulated rainfall, Cloudiness, CAPE instability index and wind speed.
The final resolution of this product is 10x10 km2 through “krigging’s interpolation”.
The product’s periodicity is each 3 hours.
Remote sensing products: ALERTS
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Fire risk maps: General Outline
MODULE MVC
Function to obtain a series of dynamic maximum value composite NDVI images
INPUT IMAGES: 60 daily NDVI images
ASCII file containing the list of images
MVC IMAGES
MODULE PARABOLIC REGRESSION
Function to obtain a image of residual values between extrapolated and actual NDVI
NDVI IMAGE
Geographical parameters of country to analysis
Residual values image
INPUT IMAGES
NDVILandSurface temperature
Geographical parameters: number of cells, size of cell analysis
MODULE TS-NDVI SLOPE
Function to obtain a image of slope regression values
Slope values image
Geographical parameters of country to analysis
MODULE RISK FUSION
Map of forest fire risk
Remote sensing products: ALERTS
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Extrapolated NDVINDVI, current day
current day
Time
No-Risk zone
Risk zone
+ 1.0 · ---> L1 = LOW
+ 2.0 · ---> L2 = MODERATE
+ 2.5 · ---> L3 = HIGH
+ 3.0 · ---> L4 = EXTREME
Province Average
Designation of fire hazard from the distribution of residuals in provincial normalisation.
For each aggregate NDVI pixel (1x1 km2):
• Fitting of the MVC images to a parabolic spline: Period: 2 months----> 6 NDVI-MVC.
• Calculation of the “residual”: difference between the NDVI extrapolated by the spline and the real NDVI of the current dayctbtatNDVI 2)(
icurrentiedextrapolati NDVINDVIDif ,,
Risk Quantification:
Fire risk maps: Vegetation decrease algorithm
Remote sensing products: ALERTS
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Risk threshold: m= -30m<-30 --->L1 =LOWm<-50 --->L2 = MODERATEm<-70 --->L3 = HIGHm<-90 --->L4 = EXTREME
NDVI
LST
Risk threshold
L1
L2L3L4
• LST vs. NDVI : Indicator of the level of the real evapotranspiration.
• The Ts vs. NDVI relationship analyzed cell by cell can be represented by means a linear relationship.
• The slope “m” characterizes the humidity level of the whole cell. This humidity level is assigned to the whole cell (10x10 km2)
m>0 Exceptional situations with very high humidity content.
m<0 Normal situation, to be analyzed
nNDVImTs
The slope increases -> Evapotranspiration decreases
Fire risk maps: Stress Algorithm
Remote sensing products: ALERTS
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DROUGHNESS
INDEX
VEGETATION EVOLUTION INDEX
LOW MODERATE
HIGH EXTREME
LOW MODERATE
MODERATE
HIGH
MODERATE
MODERATE
HIGH HIGH
MODERATE
HIGH HIGH EXTREME
LOW
MODERATE
HIGH
EXTREME
HIGH HIGH EXTREME
EXTREME
The fusion of the risks coming from the first two indicators is done accordingly to the following table, obtained through empirical analysis of the fuzzy type.
Fusion of the two indicator
Remote sensing products: ALERTS
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Forest Fire Risk index maps example
MODIS False color composite and Fire Risk Map examples. Date acquisition: 03/09/2005. MODIS image show the fire’s smoke in Asturias and Galicia´s region. The fire risk over this area have extreme risk fire (magenta and red colours).
Remote sensing products: ALERTS
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Remote sensing products: DETECTION
OperationWhen the MSG, TERRA, AQUA, NOAA and FENGYUN satellites pass over the LATUV, Hot Spot maps are create for different regions in Europe.
MODIS-Terra and MODIS-Aqua satellites•The hot spot detection from Terra and Aqua satellites is carry out through a NASA Institutional Algorithm.
•LATUV runs the PGE30-version 4.0.1.
•In the case that a pixel appears on fire, the temperature and extension are obtaining by Dozier’s method.
•With fire temperature and extension, we estimated the Power fire through Stefan-Boltzmann law.
•We compare the real time hot spots with hot spot DDBB and eliminate the false alarm.
•The new hot spots table is convert in shape vector file.
NUM_FUEGO X_UTM Y_UTM HH_MM DD_MM_AA PLATAFORMAAREAINC TEMP_FIRE TEMP_NOFIRINTENSIDAD CONFIANZA1 573499 4138492 12:14 29:09:05 TERRA 4163.5 350.0 301.2 6.4 712 573980 4138817 15:30 29:09:05 AQUA 3.2 1199.0 304.5 11.0 823 278652 4822997 15:30 29:09:05 AQUA 420.7 485.0 294.3 9.0 41
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Hotspots Map example estimated from MODIS-AQUA image. The zoom show the Fire released power.
Hotspots Map example
Remote sensing products: DETECTION
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Active FiresLATUV use the MSG data. The temporal resolution of this data is very high 15 min.
15 minutos
Escena 51 Escena 52
0
10
20
30
40
50
60
70
0 2 4 6 8 10 12 14 16 18 20 22 24hora
1. T
emp
erat
ura
(ºC
)
-10-8-6-4-20246810
Dif
eren
cia
(t1-
t0)
(ºC
)
For detection LATUV analyze the gradient between two continuous MSG temperature sequences.
The result is store in DDBB and show in internet.
Remote sensing products: DETECTION
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Remote sensing products: DETECTION
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MODIS-Terra and MODIS-Aqua satellites FLAMING FRONT
We separate in two different case the estimation of flaming front:
CASE 1: When we have 1 or 2 continues hot spots pixel …
CASE 2: When I have 3 or more continues hot spots pixel ...
#1: 4 hs#2: 7 hs#3: 4 hs#4: 5 hs#5: 13 hs#6: 5 hs#7: 2 hs#8: 5 hs#9: 2 hs
1
2345
6 78
9
Groups of hot-spots
CASE 1
CASE 2
Remote sensing products: CRISIS TIME
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MODIS-Terra and MODIS-Aqua satellites FLAMING FRONT
When I have 1 or 2 continues hot spots pixel …
•During the detection through Dozier’s method we estimate the flaming area from 1KM resolution.
•From NIR (QKM resolution) band the flaming can be located where the reflectance is very low inside the 1KM pixel.
•We convert the flaming area in number of pixel.
•After, we choose the pixels with minimum reflectance.
1KMQKM
Remote sensing products: CRISIS TIME
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MODIS-Terra and MODIS-Aqua satellites FLAMING FRONT
When I have 3 or more continues hot spots pixel ...
•During the detection through Dozier’s method we estimate the flaming area and fire temperature from 1KM resolution. With these we calculate the power.
•Through clustering technique, we merge the hot spot with similar power.
350 750 K 1200350 350 750 1200
MW
Remote sensing products: CRISIS TIME
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INPUT Central image co-ordinate of burnt area
Coordinate from hot spots list
INPUT IMAGESNDVI before fire
NDVI post fire
Analysis parametersSize of analysis matrixStandard deviation factor
Interactive INPUT
Default parameters
Difference algorithmThreshold NDVI based on contextual analysis of difference image
Regression algorithmThreshold NDVI based on contextual analysis of regression image
Filter of isolated points
Mask image of burnt area
Area (Ha.) of burnt area image of burnt area
Contextual algorithm
MODIS-Terra and MODIS-Aqua satellites BURNED AREA - General Outline
Remote sensing products: POST CRISIS
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DIFFERENCE: Contextual algorithm
(NDVIb – NDVIa) > b-a + 1.5 b-a
REGRESSION: Contextual algorithm
NDVIa = a . NDVIb + bNDVIa < NDVIa,f – 1.5 S
Remote sensing products: POST CRISIS
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BURNED AREA OVER GALICIA – 2005-08-21
Remote sensing products: POST CRISIS
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BURNED AREA EVOLUTION
Through the multi-temporal analysis is possible to obtain the average burned velocity.
Remote sensing products: POST CRISIS
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Remote sensing products: POST CRISIS
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LATUV: some project
PROJECT CONTRACT
A.- DETERMINACIÓN DEL RIESGO DE INCENDIOS FORESTALES MEDIANTE IMÁGENES MODIS (CGL-2004-00173)
CICYT
B.- DRAGON PROGRAMME ESA
C.- ESTABLISHMENT OF PRE-OPERATIONAL CRISIS DATA MANAGEMENT CENTRE (CDMC)
ESA
D.- EUROPEAN FIRE ALARM EVALUATION SURVEILLANCE AND TRACKING BY OBSERVATION FROM SATELLITES (EFAESTOS)
ESA
E.- Near Real Time Operational Demostration Project ESA
F.- SUBCONTRACT OF WORKS IN THE FRAMEWORK OF THE CRISIS DATA MANAGEMENT CENTER
ESA
G.- DEMOBIRD Project (ESTEC Contract nº 17192/03/NL/GS) ESA
H.- FIRE RISK EVALUATION IN MEDITERRANEAN ENVIRONMENT (FI.R.E.M.EN)
D.G. XII (UE)
PROJECT CONTRACT
I.- FOREST FIRE EARTH WATCH: UTILISATION STUDY AND MISSION CONCEPT REFINEMENT (ESTEC, 13063/98/NL/GD)
ESA
J.- DESARROLLO DE UN SISTEMA EXPERTO PARA PREDECIR EL PELIGRO DE INCENDIOS FORESTALES EN GALICIA Y EL NOROESTE DE CASTILLA Y LEON
(1FD97-1122-C06-06)
DGESIC-FEDER
K.- DETECCION Y SEGUIMIENTO DE INCENDIOS FORESTALES MEDIANTE TELEDETECCION ESPACIAL (AGF99-0964)
CICYT
L.- FOREST FIRE EARTH WATCH: ALGORITHM SUITABILITY REVIEW AND DEV ELOPMENT (APP-JP/99-02-047/AT/at
ESA
M.- EAU ET FEU SCOT
N.- FORMA (SCR-E/111404/D/SV/MA) ESA-SCOT-CRTS