1. 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 alf@latuv.uva.es

2. 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.

3. 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.

4. 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 evolution Post-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 DDBB Detection 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 dataMSG, NOAA and FengYun dataGFS Data

5. Cold Room Principal Laboratory Robot SaverCluster – 8 nodes LATUV: Installations

6. MODIS Antenna NOAA & Fengyun & Seawifs Antenna MSG Antenna 2 NOAA Antennas LATUV: Antennas

7. 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

8. LATUV processing areas LATUV-MODIS processing chain

9. 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 BT band 31 LATUV-MODIS processing chain

10. 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.

11. Example over Spain Remote sensing products: ALERTS

12. 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 km 2 through “krigging’s interpolation”. The product’s periodicity is each 3 hours. Remote sensing products: ALERTS

13. 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  NDVI  Land  Surface 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

14. Extrapolated NDVI NDVI, current day current day Time No-Risk zone Risk zone  + 1.0 ·  ---> L 1 = LOW  + 2.0 ·  ---> L 2 = MODERATE  + 2.5 ·  ---> L 3 = HIGH  + 3.0 ·  ---> L 4 = EXTREME Province Average Designation of fire hazard from the distribution of residuals in provincial normalisation. For each aggregate NDVI pixel (1x1 km 2 ): 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 day Risk Quantification: Fire risk maps: Vegetation decrease algorithm Remote sensing products: ALERTS

15. Risk threshold: m= -30 m L 1 =LOW m L 2 = MODERATE m L 3 = HIGH m L 4 = EXTREME NDVI LST Risk threshold L1L1 L2L2 L3L3 L4L4 LST vs. NDVI : Indicator of the level of the real evapotranspiration. The T s 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 km 2) m>0 Exceptional situations with very high humidity content. m<0 Normal situation, to be analyzed The slope increases -> Evapotranspiration decreases Fire risk maps: Stress Algorithm Remote sensing products: ALERTS

16. DROUGHNESSINDEXDROUGHNESSINDEX VEGETATION EVOLUTION INDEX LOWMODER ATE HIGHEXTRE ME LOWMODER ATE HIGH MODER ATE HIGH MODER ATE HIGH EXTRE ME LOW MODER ATE HIGH EXTRE ME HIGH EXTRE ME 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

17. 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

18. Remote sensing products: DETECTION Operation When 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.

19. Hotspots Map example estimated from MODIS- AQUA image. The zoom show the Fire released power. Hotspots Map example Remote sensing products: DETECTION

20. Active Fires LATUV use the MSG data. The temporal resolution of this data is very high  15 min. 15 minutos Escena 51Escena 52 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

22. 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 2 3 4 5 6 7 8 9 Groups of hot-spots CASE 1 CASE 2 Remote sensing products: CRISIS TIME

23. 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. 1KM QKM Remote sensing products: CRISIS TIME

24. 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. 350750 K 1200350 750 1200 MW Remote sensing products: CRISIS TIME

25. INPUT Central image co-ordinate of burnt area Coordinate from hot spots list INPUT IMAGESNDVI  before fire  NDVI post fire Analysis parameters  Size of analysis matrix  Standard deviation factor Interactive INPUT Default parameters Difference algorithm Threshold NDVI based on contextual analysis of difference image Regression algorithm Threshold 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

26. DIFFERENCE: Contextual algorithm (NDVI b – NDVI a) >  b-a + 1.5  b-a REGRESSION: Contextual algorithm NDVI a = a. NDVI b + b NDVI a < NDVI a,f – 1.5 S Remote sensing products: POST CRISIS

27. BURNED AREA OVER GALICIA – 2005-08-21 Remote sensing products: POST CRISIS

28. BURNED AREA EVOLUTION Through the multi-temporal analysis is possible to obtain the average burned velocity. Remote sensing products: POST CRISIS

30. LATUV: some project