1 FIRE DETECTION BY SATELLITE FOR FIRE CONTROL IN MONGOLIA Global Geostationary Fire Monitoring Workshop on March, 2004 Darmstadt Germany S.Tuya, K.Kajiwara, Y.Honda CEReS of Chiba University & JAXA

2 Presentation outline 1. Introduction 2. Goal & Objective 3. Study area & Data 4. Methodology 5. Results & Conclusion

3 INTRODUCTION Forests and grasslands play an important role in the economy development of the country. Forest cover is 8.1% and grassland cover is 70% of all territory. In an average year occur the forest fires and steppe fires. Since 1987 the Information and Computer Center of Ministry for Nature and the Environment daily receives the AVHRR (Advances Very High Resolution Radiometer) data from NOAA meteorological satellite, which can be used to detect and monitor the forest and steppe fire over whole territory of Mongolia. Fire monitoring in Mongolia is essential for all kind of land-use planning and forest management. To detect and monitor wildfires and to support fire management activities with real time information on fire events is of high priority. To meet this objective, a fire detection methodology based an NOAA AVHRR data has been developed at the Information Computer Center. To improve the fire monitoring, a second processing chain was set up using the WFW software in 2000.

4 GOAL We need an ability to real time quickly detect, locate and respond fires using satellite data. To reducing their ecological and economical damages in the country.

5 OBJECTIVE Determine the location of active fires using satellite data Determine the total burned area Compare the suitability of different satellite data for fire monitoring and assessment

6 Study area GEOGRAPHICAL LOCATION. (41 O 35'N - 52 O 09'N and 87 O 44'E O 56'E) and bounded by Russia and China.GEOGRAPHICAL LOCATION TOTAL TERRITORY: 1,566,500 sq. km. POPULATION: more than 2.7 million persons. CAPITAL: Ulaanbaatar. Its population is more than 650,000 persons. BASIC OF MONGOLIAN ECONOMY: livestock farming. CLIMATE: continental

7 Mean annual NDVI Mongolia Mongolia Russia China

8 Forest

9 Grassland

10 DATA USE Satellite data NOAA-AVHRR 1km ( 4,7 April 2000, 5.May, 2003) LANDSAT-TM ( 7. April, 2000) MODIS-TERRA 1km, 500m, 250 m (5.May, May, 2003) ADEOS-II, GLI 1km (5.May, May, 2003) Ancillary data * Rivers, lakes, road and political boundaries

11 1. METHODOLOGY at ICC Fire detection methodology in practice at ICC I. Active fire: a)T3 > 45 o C b)R1(or R2) = 6 – 12 II. Burnt area: a)T3 > o C b)R1(or R2) = 3 – 6 CH3- Temperature of NOAA-AVHRR channel 3. CH1 or CH2 – reflectance of NOAA-AVHRR channel 1 or 2 CH4 or 5-Temperature of NOAA-AVHRR channel 4 or 5 are used for cloud masking. In the final image product, active fires are identified by visual interpretation and plausibility check. ICC -Information Computer Centre in Mongolia

12 Daily Fire Map and Hot Spots From NOAA- AVHRR Data Using Traditional method at ICC Trends of steppe fire over Dornod and Khentii aimags (North Eastern part of Mongolia). 07.April.2000 night afternoon

13 Total burned area map of Mongolia 2000

14 Fire Frequency Map of Mongolia

15 2. METHODOLOGY at JRC Fire detection methodology WFW in JRC I.Threshold Fire Test: a selection of pixels that could potentially contain fires, and thus be called "fire pixels". A pixel is selected as a potential fire if: Tb(3) > 311K and Tb(3) - Tb(4) > 8K II. Contextual Fire Test: a confirmation of the fire pixel classification by comparing the pixel with its immediate neighborhood. A potential fire is then confirmed if: [Tb(3) - Tb(4)] > Tb(34)bg + 2 s(34)bg and Tb(3) > Tb(3)bg + 2 s(3)bg + 3K. T b (i) represents the brightness temperature of channel i (i = 3, 4, 5). Tb(3)bg = Mean T b(3) in the background. s(3)bg = Standard deviation of T b(3) in the background. Tb(34)bg = Mean value of [T b(3) - Tb(4)] of pixels in the background. s(34) bg = Standard deviation of [Tb(3) - Tb(4)] of pixels in the background WFW in JRC- World Fire Web in Joint Research Centre

16 Daily Fire Map and Hot Spots From NOAA- AVHRR Data Using WFW system at JRC Daily, global fire maps are built up at the JRC in Italy from this regional data by automatically sharing regional fire maps over the internet. Global fire information is then available on-line, in near real-time.

17 Daily Fire Map and Hot Spots From NOAA- AVHRR Data Using WFW system at JRC

18 Fire Frequency Map of Mongolia for the period of March-May 2000 using WFW and Arc View

19 Comparison of NOAA-AVHRR data and Landsat-TM data for fire monitoring Burned Area Map of Dornod Region ( ) Example of Burned area Example of Burned area LANDSAT-TM NOAA-14

20 Comparison of NOAA-AVHRR data and Landsat-TM data for fire monitoring Active Fire of Dornod Region ( ) Landsat-TM Active fire NOAA-14 Active Fire

21 3. METHODOLOGY USING THRESHOLD VALUE Fire detection threshold for potential fire pixels 1.For NOAA-AVHRR CH (3) > 311K and CH (3) - CH (4) > 8K CH (2) < For MODIS-TERRA CH21>360K CH31>320K and CH21- CH31>20K 3. For ADEOS-II, GLI CH30>330K

22 Fire map using NOAA-AVHRR 1km Steppe fire in 05.May 2003, Northern Mongolia - Red points is Hot spots - Dark blue is burned area ( 7953.sq.km 2 )

23 Fire map using MODIS-TERRA 1km Steppe fire in 05.May 2003, Northern Mongolia - Red points is Hot spots - Dark green is burned area (7521.sq.km 2 )

24 Fire map using ADEOS-II, GLI 1km Steppe fire in 05.May 2003, Northern Mongolia - Red points is Hot spots - Dark brown is burned area (7838.sq.km 2 )

25 Burned area map using MODIS- TERRA- 1km, 500m,250m Burned area of steppe fire on 05.May 2003 Dornod region in the Northern Mongolia 1km 500m 250m I sq.km 2 I sq.km 2 I sq.km 2 II sq.km 2 II sq.km 2 II sq.km 2 I II I I

26 Burnt area maps of Mongolia for the spring period of 2003 ADEOS-II GLI ADEOS-II GLI

27 Operative service for end users Internet X25 protocol Inter Organization Network State Emergency Committee Fire Fighting Office Civil Defence Office Fire Fighting Office in aimags Hydrometeorological Center in Aimags Aimag Administrative Staff Ministry Nature and Environment Government Other organizations ICC

28 Result  The new processing chain can detect fires and burnt area automatically.  To a small extend, both methodologies confuses active fires with very hot land surfaces.  The major disadvantage of the WFW system compared to the local method is, that real time observation is not possible. Necessary ephemeris data for the fire processing is available at the earliest one day after the image reception.

29 Result  AVHRR has two major advantages for fire monitoring. First, its observation covers the entire region everyday at a moderate resolution 1.1 km, which is critical for operational fire monitoring. Second, it has wide spectral coverage. But AVHRR images give the general locations and size of burned area of current fires.  Used ADEOS-II GLI and MODIS-TERRA images can progressed the accuracy for calculating the burned area and hotspots. The estimation of burned area using new sensors gives details information on burnt areas for the environmental assessments of damage. A totally 4,946,99 thousand ha grassland was burnt on 26.May, 2003

30 Conclusion Fire monitoring methodology improvement in Mongolia is essential for all kind of land-use planning and forest management. A large data base can be achieved over the entire fire season for further evaluations and research activities. The WFW approach is able to cover large areas (e.g. entire NOAA scene), where as the traditional method concentrates on specific regions of interests.

31 Conclusion To a small extend, both methodologies confuses active fires with very hot land surfaces. Understand the impacts of global environmental change related on the major individual influences of local and regional climate change. Therefore wild fires in the Mongolia are one of factors of local area individuals to great global change. Consequently, I think fire monitoring in Mongolia is one part of local activities contribution to global change research.

32 Global Regional Asia Local Global Fire Monitoring. Thank you for your kind attention