Sentinel Asia Wild Fire Control Initiative

Transcription

1 Sentinel Asia Wild Fire Control Initiative JPTM Step2 No.3 July 6-8 Manila Philippine Toshihisa HONMA (Hokkaido Univ.) for Masami FUKUDA (Univ. of Fukuyama)

2 Objective of Sentinel Asia Wildfire Monitoring and Control Initiative *Contribution to the Asia-Pacific region for Wildfire Management and Control *Mitigation of Wild Fire Damage to Infrastructure and Forest Resources *Contribution to Global Environment: Mitigation of global warming Impact to future global warming Carbon Cycle: Sink Source Emitted CO 2 from wild fire 2~4 Gt (IPCC report 2007) Contribution to Kyoto Protocol REDD: Reducing Emissions from Deforestation and Degradation in Developing countries *Sustainable Forest Management

3 Global Wild Fire Occurrence Year of 2005 by NASA

4 Basic Concept of Wild Fire Control Initiative Hot Spot

5 Fire Management Cycle Fire Warning (Fire Danger Warning) Early Fire Detection Forecasting Fire Expansion Early Fire Control Full Suppression Restoration of damaged forest Sentinel Asia

6 Sentinel Asia Wildfire Monitoring WG Milestone STEP1 STEP APRSAF-13 APRSAF-14 APRSAF-15 APRSAF-16 APRSAF-17 JPTM#1 JPTM#2 JPTM#3 JPTM#4 STEP2 JPTM#1 STEP2 JPTM#2 STEP2 JPTM#3 Wildfire Monitoring WG Start Web site Validation Phase Operation Phase Hotspot Detection Algorithm Improvement Validation Campaign Algorithm Study JST/JICA Project for Kalimantan, Indonesia Fire Danger Rating System Improvement System STEP1 System Development STEP2 System/ Utilization of WINDS

7 STEP1( ) Activity Summary Construction of Web-GIS for MODIS Hotspot (MOD14) AIT, Univ of Tokyo, CSIRO, CRISP and LAPAN are providing Hotspot Information Validation of MODIS Hotspot (MOD14) Campaign using Ground Truth Data and Satellite Imagery in Kalimantan, Indonesia, Thailand, and Mongolia Study of Hotspot Detection Algorithm to improve MOD14 7 kinds of Algorithm under study: Hokkaido Univ., CRISP, Soul National Univ., and JAXA sub working group activity

8 STEP2 ongoing Plan 1 Early Fire Control in cooperation with Users JST /JICA Project: Wild Fire and Carbon Management in Peat-forest in Indonesia 2 To expand activities into mitigation and preparedness phase Fire Danger Rating System (FDRS) (Action Item of JPTM #2)

9 Status of JST/JICA Project Wild Fire and Carbon Management in Peat-Forest in Indonesia

10 Satelite Sensing Program Fire detection/ Vegetation estimation/ Geo-information/ Water content in soil/ (CO 2 profile by GOSAT) Carbon Estimation Program Ecosystem Quantification/ Biomass & Geo-information by Airborne Laser / Peat-mass/ Soil-information/ CO 2 flux Carbon Management Program Water table management/ Fire protection/ Rehabilitation of peatland/ Ecosystem protection/ Social activity Each Output 1)Fire Prediction, Detection & Control System 2) Carbon Estimation System 3) Carbon Management System 4)Environmental Education System Integrated Output Peat Carbon Initiative 1)Tropical Peat Conservation by Integrated Carbon Management Model 2)CDM & REDD of Tropical Peat 3)Activation of Rural Society 4)International Network of Peat Carbon Initiative (Collaborate with GCP, GLP, and Sentinel Asia)

11 Study Site from 1997 Central Kalimantang, Indoneshia Peatland Mega Rice Project Palangkaraya CO 2 observation tower at SF(Natural Forest), KF(Disturbed Forest) and KB(Fire- affected area). Study Topics: Green House Gasses Flux (CO 2, CH 4, N 2 O) Fire Detection and Protection Water Table Monitoring and Management Peatland Ecology Integrated Farming

12 Carbon Estimation Program Amount of CO 2 Accumulation(NEP) during May 2004 to May 2005 in tropical peat in Central Kalimantan NEP (gc m -2 y -1 ) CO 2 is released from all sites (CO 2 source) 0 NEP Hirano et al., unpublished UDF DF BC UDF: Forest in Un-drained Peat DF: Forest in Drained Peat BC: Land in Drained and Fire-affected Peat CO 2 emission (11 Mt C/M ha/y) from disturbed tropical peatland in Mega Rice Project Area, which equal to 3% CO 2 emission from Japan Forest in Un-drained Peat Forest in Drained Peat Land in Drained and Fire-affected Peat

13 Relationship between Ground Water Level and Fire Simulate carbon loss by fire using data of Water Table

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15 What Factors regulate Carbon in Tropical Peat? Deforestation Dryness of ground surface Decrease water holding capacity Drainage Decrease water table Water Carbon Emission by Fire Carbon Loss through Water Carbon Emission by Microorganisms Degradation

16 Water and Carbon Evaluation Water Evaluation by GOSAT (Surface Water Content) and Monitoring- Networks on Water Table Carbon Flux Evaluation by GOSAT, Airborne, Tower (Eddy Flux), and Ground Direct Solar Observation in Tropical Peat 1. GOSAT(CO2) ALOS(PALSAR) 2. Airborne Sensor 3. Ground measurement Carbon Stock Evaluation by amount of biomass and peat land subsidence monitoring with PALSAR and GPS GPS 4.GPS GPS GPS Basement Peat land Basement

17 Application of Satellite ALOS Daichi Data Figure 1 50-meter mosaic image in Southeast Asia Figure 1 is one example of the public SAR picture. Red, green, and blue were allotted to the difference between the horizontally-polarized wave and the vertically-polarized wave, which is measured by PALSAR, and this image was made a color. It is shown that it is deforestation ground or not a forest seeing purple and a forest seeing green in a whole. Biomass Peat loss by fire Water content in surface

18 Application of Satellite GOSAT IBKI Data Below Figure is a global map of XCO2 (unvalidated) for the month of September It should be acknowledged that the current results are unvalidated and consequently it is not appropriate to interpret estimated individual column averaged dry air mole fraction values. After initial validation activities and adjustments of corresponding data processing methods, the concentration data will be made available for the registered general public from around the end of January, 2010 target period. Then column data should cover all Indonesia area. Column averaged dry air mole fraction distribution of carbon dioxide for the month of September, 2009, obtained from IBUKI observation data (unvalidated) 提供 :JAXA

19 Stock Change GOSAT& Airborne Interference method Flux Change by Respiration Chamber flux & Eddy flux by Fire

20 Satellite Sensing Program Terra & Aqua (MODIS) ALOS (PALSAR/PRISM/AVNIR2) Terrestrial sensing network Meteorological Data Ground Water Level Data and Model on C-Stock & C-Flux Fire detection Vegetation estimation Geo-information Carbon Initiative (Automatic data collect) Relationship between Peat Dome and Water Content (Data analysis) Carbon -Water Model Water content in soil

21 Estimation and Improvement of FDRS

22 Fire Danger Alarm in Alaska

23 Precaution Fire Danger Rating System (FDRS) The Fine Fuel Moisture Code (FFMC) represents the moisture content of litter and cured fine fuels, 1-2 cm deep. It expresses the ease of ignition and fuel flammability. FFMC is sensitive to daily changes in temperature, rainfall, relative humidity, and wind speed. Time lag is 2/3 day, which means that it takes two thirds of a day for the fine fuels to react to a change in the weather. The Duff Moisture Code (DMC) represents the moisture content of loosely compacted, decomposing organic matter, 5-10 cm. deep, which determines resistance to control. DMC is sensitive to temperature, rainfall, and relative humidity. Time Lag is 12 days. The Drought Code (DC) represents the deep layer of compacted organic matter, cm. deep, which determines resistance to extinguishment. It indicates seasonal drought and smouldering fires in deep duff or large logs. DC is sensitive to temperature and rainfall. Time lag is 52 days. The Initial Spread Index (ISI) represents a numerical rating of fire spread immediately after ignition without the influence of variable fuel quantity (the fuel type isn't considered). It fluctuates with wind speed and time of day. ISI is a combination of FFMC and wind. The Build Up Index (BUI) represents total fuel available for combustion. In the absence of rain, BUI fluctuates little throughout the day. BUI is a combination of DMC and DC. The Fire Weather Index (FWI) represents the intensity of a spreading fire. FWI is a combination of ISI and BUI.

24 Basic structure of the Canadian Forest Fire Weather Index System (Van Wagner, 1987)

25 Data and Figures in Indonesia provided by LAPAN FWI on 15 September, 2006 FWI on 15 September, 2008 (2.5km grid)

26 An example of FWI on 15 September, 2006 and Hotspot on 16 September, Low Index of FWI, but fire occurred Canadian Fire Danger Index may not suitable to Southeast Asia Origial data: FWI: LAPAN and MMD Hotspot: UT / AIT High Index of FWI, and fire occurred High Index of FWI, but fire didn t occur

27 Improving Steps 1 st Step: To verify the FWI (and other weather indices) compared with Hotspot 2 nd Step: To improve and modify the FWI which is suitable to Southeast Asia with previous data (Dry Season in 2006 and 2008). It will be consulted with Alaskan Fire Service which has enormous experiments in Alaska. 3 rd Step: To verify the modified FWI to the occurring Hotspot

28 FIREBIRD Program: Wildfire Monitoring by ISS (International Space Station) Astronauts and Small infrared camera on ISS Wildfire in Northeast Australia observed by MODIS on 6 May 2010

29 FIREBIRD PROGRAM Fire Fighting with Astronauts on ISS

30 Cost Benefit of Fire Control A scheme of REDD(Reducing Emissions from Deforestation and Degradation in Developming Countries) Let s assume mean burned area is equal to 100,000 ha /year in Central Kalimantan Once 10 % of fire damage is reduced by suppression activity, total gain will be 10,000ha 20ton $40 = $ 8 million Preservation of Forest Ecosystem

31 Sentinel Asia Wild Fire Initiative Goal