THE OBSERVATIONS FROM SATELLITES TO HELP IN THE STRUGGLE AGAINST FIRES

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

[email protected]

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.

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.

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

Cold Room

Principal Laboratory

Robot Saver Cluster – 8 nodes

LATUV: Installations

MODIS Antenna

NOAA & Fengyun & Seawifs Antenna

MSG Antenna

2 NOAA Antennas

LATUV: Antennas

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

LATUV processing areas.

LATUV processing areas


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


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.

Example over Spain


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.


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


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


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


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


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: 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

Hotspots Map example estimated from MODIS-AQUA image. The zoom show the Fire released power.

Hotspots Map example


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.



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


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



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


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

DIFFERENCE: Contextual algorithm

(NDVIb – NDVIa) > b-a + 1.5 b-a

REGRESSION: Contextual algorithm

NDVIa = a . NDVIb + bNDVIa < NDVIa,f – 1.5 S


BURNED AREA OVER GALICIA – 2005-08-21


BURNED AREA EVOLUTION

Through the multi-temporal analysis is possible to obtain the average burned velocity.



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

Documents

THE OBSERVATIONS FROM SATELLITES TO HELP IN THE STRUGGLE AGAINST FIRES