Detection of Forest Fires Using Remotely Sensed Data

Transcription

1 Detection of Forest Fires Using Remotely Sensed Data Dr.Jaruntorn Boonyanuphap Faculty of Agriculture Natural Resources and Environment, Naresuan University

2 The Impacts of the forest fires Disturbance to ecosystem Increase in trace gases Pollution problems Economic losses

3 The main causative agent of Forest fires Natural Lighting Meteorology condition Human activity Clean the soil before planting Eliminate crop waste Renew grass to feed livestock

4 To effective fire management Should know and understand the environmental and social factors which influence fires and its impacts.

5 Understand a Forest Fires Issue Document the events Gather fire information Gather ancillary information

6 Fire Detection One of the most important aspects of forest fires control is a system of locating fire before they are able to spread and out of control.

7 Two main ways to derive the information of forest fires 1. Ground Observation 2. Remote sensing data

8 Ground Observation Fire Tower or Forest Fire Control Unit Ground survey Can only cover small area Inaccessibility to some areas Limitation of time detection Fighting Click

9 Remote sensing data Feasibility of data acquisition Synoptic view Multispectral approach Repetitive coverage Global available of data

10 Conclusion Forest fire detection using Remote sensing data Burned Areas Smoke Plumes or Smoke haze Hot Spots Variation and changes in vegetation Change in the temperature Changes in water content of the ground layer

11 Detection Burned Areas Visible Wavelength RGB Color Composite Combination of Panchromatic and Multispectral Image Maps of Burned Area

12 Source: Systems for World Surveillance, Inc., 1998 Visible Wavelength The burn areas are not clearly evident using this wavelength data alone. SPOT- Panchromatic image ( µm) Burned Area

13 754-TM color composite (RGB) RGB Color Composite Source: NRCT Fire Direction Active fire Burned Area 432-TM color composite (RGB) Huai Kha Khaeng Wildlife Sanctuary, Thailand. Burned Area

14 Source: Systems for World Surveillance, Inc., 1998 SPOT - Pan & XS Imagery can be combined to identify burn areas more clearly The Panchromatic image shows the burn areas are not clearly evident using this data alone However, we can see the roads or the line feature clearly due to high resolution

15 Source: Systems for World Surveillance, Inc., 1998 Multispectral Image The Multi-spectral image provide a lower resolution which restricts one's ability to zoom in close. However However,, we can easily separate the burned area from unburned area

16 Source: Systems for World Surveillance, Inc., 1998 Combination of Panchromatic and Multispectral Image Combining the high resolution panchromatic image and the information on vegetative cover from the multi-spectral image, we can best define the areas affected by fire. The reddish areas is the damage caused by fire

17 Source: CCRS,1999 Comparison of boundaries of forest are derived from satellite-based technique and traditional method A : A pixels of active fire are detected from NOAA/AVHRR B : The boundaries of the burned areas are observed by ground survey Active fire in portion of the Northwest Territories during 1995 That can seem the satellitebased technique does a better job, detecting more fires than the ground survey at much less cost. Burned Area

18 Detection Smoke Plumes Visible Wavelength RGB Color Composite

19 ( µm) Smoke Plumes Hot Spot GOES-9 Super Rapid Scan Observations (SRSO)

20 Source: CCRS NOAA-14 satellite on June 25, 1995 : Canada Smoke plumes are detected by AVHRR channel-1 Burning areas (red spots) are detected by the computer algorithm RGB

21 Source: NOAA Multichannel Color Composites AVHRR channels 1, 2, 4 : R G B Smoke Plumes are represented in yellow

22 Source: CRISP SPOT - XS Imagery The image of detected smoke plumes and burned scars Information about date, time, location map and scale bar should be included in image for easy reference Sumatra

23 Hot Spots Short wave infrared (SWIR) The actively burning fire have much stronger emission in the µm m wavelength than thermal infrared Combination of SWIR and Thermal Infrared The difference in SWIR and thermal infrared band Free from the affect of atmospheric parameter

24 Source: NOAA Short wave infrared (SWIR) AVHRR Ch.3 (3.8 µm) : 3/8/98 Study Area : Northern Brazil and Venezuela HotSpot Indonesia

25 Combination of SWIR and Thermal Infrared To locate the fire spots based on the pixels that have radiance value of SWIR channel greater than brightness temperature of Thermal channel During the day At night

26 Source: Alfaro, 1999 GOES-8 (Geostationary Operational Environmental Satellite) The difference in SWIR and thermal infrared band SWIR channel : 3.9 µm Thermal channel : 10.7 µm Difference Radiance Value = Thermal channel - SWIR channel At night During the day : fog product : reflectivity product

27 Source: Alfaro, 1999 GOES-8 8 Image (fog/reflectivity( product) April 13, 1997 at 14:15 LT reflectivity product April 14, 1997 at 05:15 LT fog product Detection

28 Variation and changes in vegetation 1. Normalized Difference Vegetation Index (NDVI) Detectable in visible and near infrared system NDVI = (Ch2 Ch1) / (Ch2 + Ch1) Ch1 = Red Band Ch2 = Near Infrared Band 2. Normalized Burn Ratio (NBR) Identification of burned area and severity level NBR = (R7 - R4) / (R7 + R4) R4 = TM Band 4 (near IR) R7 = TM Band 7 (mid IR or SWIR) Detection

29 Source: Eric S. Kasischke,1992 NDVI AVHRR images of the interior of Alaska 15 and 30 June and 15 August 1990 Green : high NDVI value Yellow and Red : low NDVI values White : very low to zero NDVI value Large areas in the August image that exhibit a drop in NDVI correspond to the locations of wildfires that occurred during the summer of Click

30 Normalized Burn Ratio (NBR) Northwest Glacier National Park, Montana, USA TM 543 color composite Acquire date : September 1, 1995 Starvation Burns in 1994 Source: M.E.S.C.

31 Compute the NBR Data set For Spring and Late-Summer date : before and after fire According to NBR = (R7 - R4) / (R7 + R4) R7 increased with fire, while R4 decreased Get four NBR datasets (the( NBR Spring and the NBR Last-Summer pairs) Determine Fire Severity from NBR Difference NBR Difference can be derived from : The NBR image after fire - The NBR image before fire

32 Source: M.E.S.C. NBR Spring Difference Image (NBR:May1995)-(NBR:May1994) (NBR:May1994) Clouds in the May 5,1995 Snow covered in both spring scenes NBR Late-Summer Difference Image (NBR:Sept1995)-(NBR:Aug1994) (NBR:Aug1994) Bright Area : High NBR value (burned area)

33 Source: M.E.S.C. Combine NBR Spring Difference and NBR Late-Summer Spring Difference datasets Fire Severity Detection

34 Source: Prakash,1999 Change in the temperature Use SWIR channel to estimate high temperature of surface fires Sub-pixel The fires often do not occupy the whole pixel TM7 ( µm) has temperature sensitivity is between C, while the lower sensitivity limit of TM5 ( µm) is 267 C Thus temperature sensitivity range of both channel is C (sub-pixel can compute only for this temperature range)

35 Source: Prakash,1999 Estimation of surface fires temperature in Jharia coalfield, India The DN value of pixel is a mixed signature of the actual fire and the background The total spectral radiance (R λ ) of pixel as an average of spectral radiance of fire spot (R fλ ) and the background spectral radiance (R bλ ) for both channel Solving equation : R λ = P R fλ + (1-P)( R bλ P : the pixel proportion of the fire spot (1-P) : the background area

36 The radiant of fire spot and its temperature estimate by solving equation P 1-P Background pixels Anomalous pixels P 1-P Subpixel area of fires Subpixel non-fires area Field photograph of surface coalmine fire Source: Prakash,1999

37 Source: Prakash,1999 False color composite of part of the Jharia coalfield, India TM 753 : RGB color composite Windows I, J, K, L, M, and N depict area of surface fire Yellow pixel is highest temperature area Red pixel is lower temperature area Detection

38 Changes in water content of the ground layer Advantage Disadvantage Microwave System (SAR) ERS JERS RADASAT Can penetrate cloud and thick haze Unable to detect hot spots or smoke plumes directly associated with fires

39 Source: CRISP Delineating Land/Forest Fire Burnt Scars with ERS InterferometricSynthetic Aperture Radar C-band SAR imagery of ERS-1 1 and ERS-2 Mapping burned areas of South East Asia in 1997 Tropical Forests : Constant backscattering coefficient (σo)( between -77 and -66 db Low interferometric coherence Study area in South Kalimantan Delineation of the possible burned areas : Use multitemporal SAR to compare the coherence images in 1996 and 1997 dataset Observe change in σo o and/or an increase in interferometric coherence of the area

40 Pseudocolor mosaics of the coherence-intensity images April, 1996 October, 1997 Vegetated : shades of cyan Red band : Interferometric Coherence Densely vegetated : brighter cyan Green band : ERS-1 1 backscattered amplitude Rivers and water : black Blue band : ERS-2 2 backscattered amplitude Non-vegetated : shades of red Settlements and built-up up areas : bright white ERS--SAR SAR

41 Source: CRISP Classification Using Thresholding the Coherence Change Red : Possible burned areas Increase in interferometric coherence Green : Vegetated areas White : Old clearings/settlements Yellow : Old clearings with regrowth

42 Multispectral SPOT Image of the Study Area The delineation of the possible burned area needs to be validated by ground data Cloud-free multispectral SPOT image as "ground-truth" Bluish white : Smoke plumes Reddish : Vegetated Dark : Possible burned areas September 8, 1997 A, B and C : Cleared for plantations E and F : burned vegetation area D : unburned vegetation area Detection Source: CRISP

43 Conclusion Remote sensing data can provide economical way and essential information useful in forest fire detection, monitoring, hazard assessment and management as well as prevention of future fire. Forest fire event can be detected from different wavelengths and sensors : Optical/Infrared sensor can provide information about: - Fire hot spot - Aerosols characteristic and distribution of the smoke haze - Burned area Limitation : cannot penetrate cloud and thick haze condition

44 Future Conclusion Active sensor (SAR) is able to acquire image in anytime and free of could cover. - observe forests/vegetation change in σo and/or an increase in interferometric coherence. Limitation : Unable to detect hot spot or smoke plumes directly, thus it not able to tell whether the forest clearings are due to fires or other means.

45 Future Plans : Develop the fire detection algorithm based on the high correlation to field-based burn effect. Improve accuracy and detail of satellite sensor to detect forest fire even. For instance, the MODIS sensor can provide excellent spectral discrimination(36 band). Reference

46 Reference Alfaro, R., Detection of the forest fire of April 1997 in Guanacaste, Costa Rica, using GOES-8 image. International Journal of Remote Sensing, VOL. 20, NO. 6, CCRS, Satellite-based Forest Fire Monitoring. CRISP Delineating Land/Forest Fire Burnt Scars with ERS Interferometric Synthetic Aperture Radar. CRISP, Fires and Smoke Haze at Timber Logging Areas in Riau, Sumatra Eric, S., Kasischke, Monitoring of Wildfires in Boreal Forests Using Large Area AVHRR NDVI Composite Image. GOES-9. Super Rapid Scan Observations (SRSO).

47 Reference M.E.S.C, The Normalized Burn Ratio (NBR) : A LANDSAT Tm Radiometric Measure of Burn Severity NOAA NOAA Satellite service division. NRCT Huai Kha Khaeng Forest Fire Monitoring. Thailand. Prakash, A., and Gupta, P., A Surface fire in Jharia coalfield, India-their distribution and estimation of area and temperature from TM data. International Journal of Remote Sensing, VOL. 20, NO. 10, Systems for World Surveillance, Inc., Forest Fire Assessment : Pan & XS Satellite Imagery can be combined to identify burn areas. Final

48 Thank You