1. Using SAR Intensity and Coherence to Detect A Moorland Wildfire Scar

2. Presentation Structure Fire –Fires & Moorlands –UK Wildfires (news clip) –Fire Scar Detection Research question & objectives (pilot study) Methodology –Why SAR? –Study Site –SAR pre-processing chain Results –Intensity –Coherence Conclusion & Future Work

3. Why Fire is Important in Moorlands? Destroy vegetation Fuel load, adaptation Climate Wildlife Vegetation Soil Humans CO 2 emissionsRemove habitat Adaptation Managed burns Arson Degradation Erosion Rate of re vegetation

4. UK Wildfires Source: BBC News, 4 May 2011 http://www.bbc.co.uk/news/uk-13277476

5. UK Fire Scar Detection Source: http://effis.jrc.ec.europa.eu /

6. Research Question (Pilot Study) How well can the C-band SAR intensity and coherence signal detect a fire scar within a degraded UK moorland environment? Objectives Determine the ability of SAR intensity and InSAR coherence to detect the fire scar over time in a moorland environment Analyse qualitatively how scene variables such as precipitation and CORINE land cover classes affect the SAR intensity and coherence signal, both inside and outside the fire scar

7. Why SAR? See through cloud and smoke Active sensor: acquire images day and night Good temporal resolution of data SAR very sensitive to moisture content ideal for mapping fire scars Source: Landmap Radar Imaging Course http://landmap.mimas.ac.uk http://landmap.mimas.ac.uk

8. SAR Interaction Source: Landmap Radar Imaging Course http://landmap.mimas.ac.uk http://landmap.mimas.ac.uk

9. Study Area Longdendale

10.  Nearest Neighbour resampling method  One image used as the input reference file, the other image is coregistered to this. ENVI Band Math using the formula 10*alog10(b1) Degraded to 100m using a Nearest Neighbour resampling method in ENVI. 5 backscatter sample points for each land cover class was extracted from the radar data.  Equivalent looks variable set to -1 threshold for speckle filtering is calc by the software – 0.5227/sqrt  Multitemporal DeGrandi Filter used  25m DEM  No GCP (however a sub-pixel accuracy can still be achieved when DORIS data has been used)  Generated Sigma Nought values  Calculate Ground Range GR (m) = Rg ÷ sin IA  Calculate number of Azimuth Looks = GR ÷ Az 1. Basic Import for ASAR or ERS-2 Single Look Complex (slc) Intensity Image (pwr) 3.A Amplitude Coregistration Resampled & resized images (rsp) Filtered image(fil) 5. Geocoding Radiometric Calibration Geocoded 25m images (geo) Level 1 SLC from ESA 4. Multi-temporal Despeckling 2. Focusing and Multilooking 6. Geocoded images to dB 100m Greyscale Geocoded SAR image Process Outputs/Inputs Processes Final Product Key 3. Amplitude Coregistration

11. Intensity & Precipitation time series Pre- fire Post- fire

12. Intensity & Land Cover Results

13. InSAR Pairs – Coherence Analysis

14. Coherence Results

15. Summary & Conclusion Precipitation & land cover are key variables for understanding the SAR intensity and coherence –Within the fire scar peat bog gave highest intensity return –Rainfall just prior to image acquisition increased intensity values for all land cover classes inside the fire scar Image results are sensitive to: –Filtering algorithm applied > recommend Degrandi multitemporal –Initial baseline of InSAR pairs > temporal decorrelation A large fire scar in a degraded moorland environment can be detected using SAR intensity. InSAR coherence needs to be further explored.

16. Future Work Investigate fire scars of different sizes, severity, land cover & precipitation conditions Analyse the affect of radar polarisation and frequency on fire scar detection –X band & L band data –Cross polarised and co-polarised data Applying classification method for fire scar mapping Explore Kinder 2008 & Wainstalls 2011 case studies –GPS boundary collected this summer –Kinder boundary obtained from MFF

17. Acknowledgements Access to fire log and fire scar GPS data PDNP Fire Operations Group Access to ERS-2, ALOS PALSAR & ASAR data as part of Category 1 Project 2999 School of Environment & Development for funding to support this research Mimas & Landmap for funding, time & resources to support this research References KEELEY, J. (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18, 116-126. LENTILE, L. B et al., (2006) Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15, 319-345. Martin Evans & Juan Yang at SED for Upper North Grain weather data

18. Thank you for Listening

19. Images for Intensity Analysis