FIRE DETECTION BY SATELLITE FOR FIRE CONTROL IN
FIRE DETECTION BY SATELLITE FOR FIRE CONTROL IN MONGOLIA Global Geostationary Fire Monitoring Workshop on 23 -25 March, 2004 Darmstadt Germany S. Tuya, K. Kajiwara, Y. Honda CERe. S of Chiba University & JAXA 1
Presentation outline 1. 2. 3. 4. 5. Introduction Goal & Objective Study area & Data Methodology Results & Conclusion 2
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 50 -60 forest fires and 80 -100 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. 3
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. 4
OBJECTIVE n n n 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 5
Study area GEOGRAPHICAL LOCATION. (41 O 35'N - 52 O 09'N and 87 O 44'E - 119 O 56'E) and bounded by Russia and China. 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 6
Mean annual NDVI 1982 -2000 Mongolia Russia Mongolia China 7
Forest 8
Grassland 9
DATA USE n Satellite data NOAA-AVHRR 1 km ( 4, 7 April 2000, 5. May, 2003) LANDSAT-TM ( 7. April, 2000) MODIS-TERRA 1 km, 500 m, 250 m ADEOS-II, GLI (5. May, 2003 26. May, 2003) 1 km (5. May, 2003 26. May, 2003) Ancillary data * Rivers, lakes, road and political boundaries 10
1. METHODOLOGY at ICC n Fire detection methodology in practice at ICC I. Active fire: a) T 3 > 45 o. C b) R 1(or R 2) = 6 – 12 II. Burnt area: a) T 3 > 35 - 45 o. C b) R 1(or R 2) = 3 – 6 CH 3 - Temperature of NOAA-AVHRR channel 3. CH 1 or CH 2 – reflectance of NOAA-AVHRR channel 1 or 2 CH 4 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 11
Daily Fire Map and Hot Spots From NOAAAVHRR Data Using Traditional method at ICC night afternoon Trends of steppe fire over Dornod and Khentii aimags (North Eastern part of Mongolia). 07. April. 2000 12
Total burned area map of Mongolia 2000 13
Fire Frequency Map of Mongolia 1996 -2001 14
2. METHODOLOGY at JRC n 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) > 311 K and Tb(3) - Tb(4) > 8 K 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 + 3 K. Tb(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 15
Daily Fire Map and Hot Spots From NOAAAVHRR 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. 16
Daily Fire Map and Hot Spots From NOAAAVHRR Data Using WFW system at JRC 17
Fire Frequency Map of Mongolia for the period of March-May 2000 using WFW and Arc View 18
Comparison of NOAA-AVHRR data and Landsat-TM data for fire monitoring Burned Area Map of Dornod Region ( 2000. 04. 10 ) Example of Burned area LANDSAT-TM Example of Burned area NOAA-14 19
Comparison of NOAA-AVHRR data and Landsat-TM data for fire monitoring Active Fire of Dornod Region ( 2000. 04. 10 ) Landsat-TM Active fire NOAA-14 Active Fire 20
3. METHODOLOGY USING THRESHOLD VALUE Fire detection threshold for potential fire pixels 1. For NOAA-AVHRR n CH (3) > 311 K and CH (3) - CH (4) > 8 K CH (2) < 0. 20 2. For MODIS-TERRA CH 21>360 K CH 31>320 K and CH 21 - CH 31>20 K 3. For ADEOS-II, GLI CH 30>330 K 21
Fire map using NOAA-AVHRR 1 km Steppe fire in 05. May 2003, Northern Mongolia - Red points is Hot spots - Dark blue is burned area ( 7953. sq. km 2 ) 22
Fire map using MODIS-TERRA 1 km Steppe fire in 05. May 2003, Northern Mongolia - Red points is Hot spots - Dark green is burned area (7521. sq. km 2 ) 23
Fire map using ADEOS-II, GLI 1 km Steppe fire in 05. May 2003, Northern Mongolia - Red points is Hot spots - Dark brown is burned area (7838. sq. km 2 ) 24
Burned area map using MODISTERRA- 1 km, 500 m, 250 m I II II 1 km I- 3633. sq. km 2 II- 3888. sq. km 2 500 m I- 3603. sq. km 2 II- 3752. sq. km 2 II 250 m I- 3626. sq. km 2 II- 3734. sq. km 2 Burned area of steppe fire on 05. May 2003 Dornod region in the Northern Mongolia 25
Burnt area maps of Mongolia for the spring period of 2003 ADEOS-II GLI 26. 05. 2003 ADEOS-II GLI 05. 2003 26
Operative service for end users Government ICC Internet X 25 protocol Inter Organization Network Other organizations Ministry Nature and Environment State Emergency Committee Civil Defence Office Fire Fighting Office in aimags Aimag Administrative Staff Hydrometeorological Center in Aimags 27
Result 1. 1. 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. 28
Result Ø Ø 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 29
Conclusion n 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. 30
Conclusion n n 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. 31
Thank you for your kind attention. Global Glo b al F Regional Asiaire Mo nit ori ng Local 32
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