Identifying Forest Change with SAR

1. Identifying Forest Change with SAR January 2010 Martin Whittle Shaun Quegan University of Sheffield

2. Motivation • Motivated by the importance of tropical forests under the UNFCCC* Reduced Emissions from Deforestation and Degradation mechanism • Deforestation & forest degradation accounts for nearly one third of anthropogenic carbon emissions: management & reduction requires mapping & monitoring carbon stocks • Current focus is on Indonesia: widespread deforestation * United Nations Framework Convention on Climate Change

3. Objectives • Produce methodology for semi-automatic detection of natural forest loss using ALOS 46-day ScanSAR and FBD HV data. • Produce land cover maps for Sumatra, Indonesian Borneo and the Indonesian New Guinea islands based on ALOS data

4. JAXA ALOS-PALSAR ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) 46 – day recurrent orbit

5. Jan 2007 Sept 2007 June 2008 Composite ScanSAR image of Kampar region Each HH image has been de-noised by multi-channel filtering. Pixel size: 100 X 100 m Image size: 128 X 91.8 Km

6. Challenges • Although there such images are clearly information rich interpretation is not straightforward. • Detection of deforestation events requires that we look for a particular type of change

7. Animation This animation covering the whole year confirms that the suspect area extends from the known cleared region over the last few months. The area on the right hand side of the image is a mixture of paddy fields, coconut plantation, shrubs and forest re-growth

8. Deforestation event Deforestation event Adjacent forest Adjacent forest Distinctive behaviour Deforestation event Adjacent forest

9. Current scheme for identification

10. Individual pixels Intensity rise due to deforestation “Relaxation Time” Baseline Forest Background Standard deviation Time of step Parameterization

11. Method captures distinctive variation Linearised detection map

12. Validation: database comparison WWF 2007 WWF 2008-9 Delineates forest /non-forest only Difference gives deforestation 2007-2008 ScanSAR RGB

13. Detections compared with database deforestation TP – True positive (correct detection) FP – False positive (false alarm) FN – False negative – missed detection

14. Optimised detections / Area Msum – total MPF – Primary Forest 2008 MNF – Not forest 2008 MDF – deforestation 2007-8

15. Jan HH June HV December HH/HV Temporal ScanSAR & FBD Temporal ScanSAR compares well with FBD Temporal ScanSAR FBD 10 km

16. Acquisition Timeline

17. HH HV HH/HV Database accuracy 2008/06/30 2007/06/28 2007/06/28 10 km There is a clear discrepancy between the database estimates of deforestation and an intuitive assessment of the image.

18. FBD – weaker events FBD shows evidence of “weaker” deforestation events also shown by database difference.

19. Backed up by ScanSAR detections ScanSAR Detections FBD: 2008/06/30 R – HH G – HV B – HH/HV

20. Masked FBD This image shows the earlier FBD image (2007/06/28) masked by the database-estimated 2007-8 deforestation between. Multi-temporal ScanSAR detections are shown hatched in red. There are no detections in the blue (depolarising) regions leading us to suspect the database designation of forest in this region.

21. Differencing FBD A simple difference between the HV channels of the FBD images highlights some of the deforested areas detected by ScanSAR very clearly, but misses others – notably the anvil-shaped region. Also, the result of this procedure is very speckled causing problems for automatic recognition by this method.

22. Landsat Landsat 5 2006/09/03 Landsat5 2008/06/20 The earlier Landsat image above was the best available from the time and emphasises the difficulty of using optical Imagery to develop accurate databases. What is visible tends to confirm our suspicions, based on FBD Analysis, that the database overestimates the primary forest region.

23. Discussion • Optimised detection algorithm can recognise only ~10% of forest according to WWF databases. • FBD reveals evidence that DB’s may be overestimating forested regions in 2007 & hence the deforestation. • If this is true it would increase the detection rate.

24. A B Cross sections Some results for cross sections taken through the deforested region

25. ScanSAR HH (dB) Jan 2007 – June 2008 Maximum change ~ 3dB

26. FBD HV and HH (dB) HH change ~zilch HV change ~ -3dB

27. Why should HH response be different ?

28. PALSAR Modes Incidence angles: FBD: 36.6◦-40.9◦. ScanSAR: 18.1o to 43.0◦.

29. Summary The FBD images shown here suggest that they can supplement databases derived from cloud-obscured optical imagery. Time-separated images clearly also contain deforestation information directly and we are examining texture–based measures to augment this and effect automatic detection. Notably, the regions picked out by FBD are both similar and different to those accessed Multi-temporal methods using ScanSAR suggesting that data fusion techniques may be usefully employed.

30. Effect of flooding ALOS user handbook