Fire Detection and Fire Prediction Systems:FF

Transcription

1 Fire Detection and Fire Prediction Systems:FF Toshihisa HONMA*, Ratih D. DIMYATI**(FF) Kazuya KAKU***, Orbita ROSWINTIARTI**, Aswin USUP****(FF1) Keiji KIMURA*, Agus KRISTIYONO***** and Hendrik SEGAH****(FF2) *HU, **LAPAN, ***JAXA, ****UNPAR, *****BPPT

2 Contents 0. JST/JICA Project 1.Introduction 2.Land Cover Classification 3.Meteorological Simulation 4.Peat Fire Detection and Alarm Systems 5. Fire Monitoring in Kalimantan Areas

3 Wild Fire and Carbon Management in Peat-forest in Indonesia Mitsuru Osaki 1, Toshihisa Honma 1, Hidenori Takahashi 2, Hozuma Sekine 3, Noriyuki Tanaka 1, Takashi Hirano 1, Takashi Kohyama 1, Toshio Iwakuma 1, Totsuo Sueda 4, Wataru Takeuchi 5, Kazuya Kaku 6, Bambang Setiadi 7, Hari Purwanto 8, Herwint Simbolon 9, Ratih Dimyati 10, Suwido Hester Limin 11 and Tania June 12 1 Hokkaido University 2 NPO Kalimantan Interchange Association 3 Mitsubishi Research Institute, Inc. 4 Ehime University 5 University of Tokyo 6 Japan Aerospace Exploration Agency (JAXA) 7 National Standardization Agency of Indonesia (NSB) 8 State Ministry of Research and Technology (RISTEK) 9 Research Center for Biology, The Indonesian Institute of Sciences (LIPI) 10 The Indonesian National Institute of Aeronautics and Space (LAPAN) 11 University of Palangka Raya (UNPAR) 12 Bogor Agriculture University (IPB)

4 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)

5 New peat management system Global Economic Impact (Carbon Credit, Funding Employments, ) Indonesia Local Community Peat Land Manageme nt Environment Impact (GHGs Emission, Biodiversity, Fire, water, ) Mitigate Global Warming Social impact (Education, Service, Culture, ) Enhance Ecosystem Services Develop sustainable system for "Peat Carbon Initiative (Advanced mechanism of CDM / REDD)

6 Project Groups FF:Fire Detection and Fire Prediction System CA:Carbon Assessment System CM:Carbon Management System CI:Integrated Peat Management System

7 Project Outputs:FF 1. The fire hotspot algorithms that suitable with Indonesia with levels of accuracy. (FF1) 2. The biomass burning emission estimated from different soil (peat and mineral soil) and land cover types. (FF2) 3. The in-situ fire information (locations and coverage) through the ground-checking validation (FF1) 4. Development of Fire Prediction Model (FF1) 5. Model of water regime (FF1,FF2) 6. Detection of land cover / land use change (quantitative area estimation) (FF2) 7. Establishing of a spectral library (plant / soil) for the investigation area (FF2)

8 Project Goals & Activities Goals FF1 Construct wild/peat fire control system FF2 Mapping and modeling the land cover :EMRP Area Activities FF1 To develop the fire hotspot algorithms which account for differences in soil and land cover types. To evaluate the fire hotspot algorithms through local validation against fire information on the ground. To derive the levels of accuracy of the resulted fire hotspot algorithms. To develop the peat moisture content threshold for fire to burn. To provide fire suppression teams and other stakeholder agencies with the accurate fire hotspot information. FF2 Mapping of land cover / land use attributes in at the landscape scale by means of operationally available satellite data Estimating land use/cover parameters and forest properties by means of new high resolution satellite data Modeling carbon budget by recovered and growing existing vegetation

9 1. Introduction A lot of peat fire occurred in Indonesia when ENSO occurred because of light rainfall and high temperature. Firstly, as a background data of peat fire, we have to classify the latest land cover by ALOS/PALSAR imagery around Palangka Raya. And the local meteorological simulation with MM5 is calculated for the important factors of the peat fire expansion. Then, we execute the peat fire expansion simulation. In addition, we will combine these systems with new fire detection method. Finally, we will build the peat fire detection and alarm information systems with remote sensing and simulation technologies in this project.

10 El Nino and Wildfire in Indonesia When El Nino occurs, it rains less around Indonesia because the convection over Indonesia moves Eastward. normal El Nino WMO(1984) El Nino Index (NINO.3) by JMA

11 Outline of fire detection and alarm information systems Hotspots Fire History Firefighters Topology (SRTM/ASTER) Vegetation (AVNIR2/PALSAR) Regional Met. prediction Fire prediction simulation WWW Cell SMS Met. Simulation (MM5 / WRF) Met. data (JMA/NOAA) static data dynamic data output calculation Inhabitant Designed by Dr.Nakau

12 2. Land cover classification by ALOS/PALSAR imagery around Palangka Raya We classified the vegetation into eight categories from the HH and HV polarization of ALOS/PALSAR with the ISODATA method which is a kind of the non-training classification. We made the land cover maps of three periods: July 9, August 24 and October 9, 2007 around Palangka Raya. The POSTEL vegetation is used for the sampling data. The results are compared with the vegetation map by SarVision, and both classifications are very similar. This means that the latest vegetation map can be renewed every 46-day of the ALOS s repeat cycle.

13 Estimation of CO 2 emission from EMRP area Non-forest (8tC/ha/year emission) 413,000ha 1.65MtC Thin Forest (15-40%) (6tC/ha/year emission) 1,670,000ha 10.02MtC Thick Forest (40%-) (4tC/ha/year emission) 19,000ha 0.076MtC Total: 11.7MtC from 2,100,000ha by the soil respiration around the EMRP area Japanese annual emission of Carbon is about 341MtC(2005). This area emits the 3.4% of CO2 from Japan. Original data by POSTEL(2008)

14 Results of land cover analysis We can analyze the land cover every 46-days with ALOS-PALSAR imagery. Water Dense forest Thin forest Thin bush Burnt forest Burnt bush Bare ground Town July 9, 2007 August 24, 2007 October 9, 2007 by Mr.Baba

15 3. Local area meteorological simulation with MM5 The weather information with high accuracy is needed as the main factor of fire expansion. We conduct the local-area meteorological simulation with MM5 around Palangka Raya. The initial resolution of the weather forecast is 1 degree mesh from the NCEP Global Tropospheric Analysis. The finest resolution in this study is 4.1km grid. For example, we calculated the meteorological simulation from September 2 to September 22, 2006 when a lot of peat fire occurred.

16 by Mr.Hatanaka Study area of MM5 meteorological simulation Domain 1 Area mesh numbers Domain 1 111km Domain 2 Domain 4 Domain 2 37km Domain km Domain 4 4.1km Domain 3

17 A result of MM5 simulation Wind field around Palangka Raya The local wind can be analyzed by this model. Palangka Raya At 13:00 local time in Palangka Raya, on September 4, by Mr.Hatanaka

18 4. Building peat fire detection and alarm information systems We are now constructing the peat fire control system. We are making the fire expansion simulation and the information service to firefighters. They will use the outputs of the system --- the detected fire, and simulated fire expansion --- and they will be able to distinguish the fire more effectively.

19 Step1: Making prototype in JAPAN Indonesia Japan JAXA Web Semi- Real time Firefighters in Palangka Raya (automatic) 手動配信 Analysis (Validation) Visualization(GIS) Japan Indonesia Terra & Aqua (MODIS) ALOS (PALSAR/PRISM/ AVNIR2) (on demand) Data storage Hokkaido Univ. Analysis: Fire detection & fire expansion simulation From 2009 to 2010 Designed by Mr. Nagano

20 Step2:Transfer of Technology Analysis LAPAN Semi- Real time Validation Visualization (GIS) On demand Data storage Indonesia Japan JAXA (automatic) Web Hokkaido Univ. Firefighters Transfer Japan Indonesia Terra & Aqua (MODIS) (on demand) ALOS(PALSAR/PRISM/AVNIR2) From 2011 to 2012 Designed by Mr. Nagano

21 After 2013 Designed by Mr. Nagano Step3:Sustainable Use Analysis LAPAN Semi- Real time Validation Visualization (GIS) On demand Data storage Indonesia (automatic) Web Firefighters: Palangka Raya Japan (on demand) JAXA Terra & Aqua (MODIS) ALOS(PALSAR/PRISM/AVNIR2)

22 5.Fire Monitoring in Kalimantan Areas Wild fire in Kalimantan Fire detection using wireless sensor network An example of system configuration

23 Fire Vulnerability Map in Central Kalimantan Low Middle High Extreme

24 Fire Detection Using Wireless Sensor Network Critical issues for monitoring peat fire Fault-Tolerant Communication Sensor nodes can be burnt by fire Sensor nodes must be densely deployed Fire can also be detected when a group of neighboring nodes goes down Network lifetime Battery-powered sensor nodes should guarantee sufficient network lifetime Energy-aware routing Adaptive power mode management for processor and radio Topology control

25 Fire Detection Using Wireless Sensor Network TinyOS Modules in prototype Application Power Monitor and Topology Control (energy state of nodes in the KR) Fault Tolerant Sensing Application Routing Routing Layer (PISA based Algorithm) Basic Module Messaging Messaging Layer Our Module Packet Radio Packet TX Power Control Control S/W SW Bit RFM Clocks Temperature Sensor HW

26 Fire Detection Using Wireless Sensor Network Topology control Topology : configuration of communicating parties which node is able/allowed to communicate with which other nodes Keep the minimum number of sensors awake, and let other nodes sleep Increase network longevity while maintaining connectivity Via adaptive radio power control

27 泥炭監視地域 Peat Observation Area 電池駆動可能 Battery drive is possible 他のセンサーデータを無線中継 Repeater for other Sensor s data データセンタへ To Data Center 設置されたノードは自律的にネットワークを構築 Installed Nodes autonoumously establishes the networks Adhoc センサネットワーク Sensor Networks

28 An Example of System Configuration Configuration diagram (Sample areas) 500m Transmission Range User NB100 + BU2110J Stargate NetBridge (Web Server) SN21140J Sensor Node

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31 0'2 00' ' 30'13 0'13 30'14 0'14 30' '13 30'14 0'14 30'15 0'2 00'2 30'3 00'3 30'PETA 1 WILAYAH PENGEMBANGAN LAHAN GAMBUT (PLG) DI PROVINSI KALIMANTAN TENGAH N 1A Blok E W S E ( PALANGKA RAYA Bereng Bengkel ( Tumbang Nusa ( Mangkatip KETERANGAN Batas Blok TN Sebangau Pembagian Wilayah PLG : Blok A ( Ha) Sungai Jalan Tubuh Air S. Sampit S. Katingan Kilometers TAMAN NASIONAL SEBANGAU Blok C Pangkoh( Bahaur Hilir( Papuyu ( S. Sebangau 0Blok B Blok A ( Lamunti ( Dadahup ( Palangka Lama ( Pulang Pisau ( Muara Dadahup ( Palangkau ( Anjir Palambang ( KUALA KAPUAS '0 S. Kapuas K A L I M A N T A N S E L A T A N Blok B ( Ha) Blok C ( Ha) Blok D ( Ha) Blok E ( Ha) PETA SITUASI KALBAR KALTIM KALTENG L A U T J A W A Blok D3KALSEL

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33 Ground Truth 9 September, 成果物及び考察 問題点

34 Sensor Network of Fire Detection Base station Fine Network Coarse Network Coarse Network canal/road

35 Wireless sensor network technology is now mature enough to be implemented in realworld applications. In general, the WSN approach is effective for various monitoring applications. In particular, the WSN is quite suitable for wildfire monitoring with UAV. We need to customize the WSN for the target application by developing application-specific software.

36 Semi-realtime fire detection and alarm systems Sensor Network,UAV Hotspots Fire History Topology (SRTM/ASTER) Vegetation (AVNIR2/PALSAR) Regional Met. prediction Met. Simulation (MM5 / WRF) Met. data (JMA/NOAA) Fire detection&prediction systems WWW Cell SMS GIS Stakeholders static data dynamic data output calculation Firefighters Inhabitant Designed by Dr.Nakau