1) The document proposes a method to jointly process dual polarized entropy/alpha decomposition and coherence optimization for improved forest height mapping from radar data. 2) Dual polarized entropy/alpha decomposition is used to classify forest/non-forest areas while coherence optimization estimates forest height by exploiting the relationship between coherence and height. 3) Initial results comparing optimized coherence to LiDAR height data show consistency, validating the potential of the integrated method. Further quantitative analysis is needed to fully evaluate accuracy.

1. Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Improved Forest Height Mapping Z-S Zhou, P. Caccetta, E. Lehmann, A. Held – CSIRO, AU S. McNeill – Landcare, NZ A. Mitchell, A. Milne and I. Tapley - CRC for Spatial Information & UNSW K. Lowell - CRC for Spatial Information & University of Melbourne

2. Contents Introduction Dual Polarisation Entropy/alpha Decomposition Partial Polarised Coherence Optimisation Joint Processing of Dual Polarised Entropy/alpha Decomposition and Coherence Optimisation for Forest Height Mapping Conclusions and Future Work * Acknowledgments: The Australian Department of Climate Change and Energy Efficiency, Forestry Tasmania, Geoscience Australia, JAXA.

3. Introduction NCAS ( National Carbon Accounting System ) -- Land Cover Change Project Australia’s National Forest Cover Change Program [Department of Climate Change (AGO)] Continental Landsat Archive 1972-2006,2007,2008, 2009, 2010, 2011…. Forest Change Products [methods] Forest Vegetation Trends www.climatechange.gov.au/ncas

4. Consistently processed time series entire Australian continent 19 time periods used so far Few clouds !? NCAS Landcover Change Project: Optical Time Series Data (25m) ……… . … … … 1972 2010 Mosaic ~ 400 scenes

5. Forest-Non-Forest : Digital Classification of Cloud Free Image at one time Epoch NCAS 2006 Image Detail 50km by 60km Classification probability (forest) Dark green High probability Light green ‘uncertain’

6. Classification probabilities (forest) Dark green High probability Light green ‘uncertain’ No classification in cloudy area … Lead to use radar data instead Digital Classification of Cloudy Image NCAS 2005 Image + cloud mask (blue lines) Detail 50km by 60km

7. Eigenvector-Based Polarimetric Target Decomposition Eigenvectors / Eigenvalues Analysis Polarimetric Entropy Probabilities Unit Target Vector alpha (Cloude-Pottier, TGARS, 1997) Orthogonal Eigenvectors Real Eigenvalues

8. Entropy/alpha (H /  Space Low Entropy Medium Entropy High Entropy Surface Scattering Volume Scattering Multiple Scattering 0 0.2 0.4 0.6 0.8 1 0 10 20 30 40 50 60 70 80 90 Entropy (H) Alpha ( a ) 9 3 X-BRAGG SURFACE DIPOLE DIHEDRAL SCATTERER SURFACE ROUGHNESS PROPAGATION EFFECTS FORESTRY DBLE BOUNCE BRANCH / CROWN STRUCTURE CLOUD OF ANISOTROPIC NEEDLES NON FEASIBLE REGION 1 4 7 2 6 5 VEGETATION 8

9. Dual Polarisation Radar Mode For reasons of cost, data rate and coverage in radar design, it often employs a single transmitted polarisation state and a coherent dual channel receiver to measure orthogonal components of scattered signal. The PALSAR sensor is just such a fully coherent-on-receive mode. Such dual polarised radars are not capable of reconstructing the complete scattering matrix [S] but instead can be used to reconstruct a 2x2 wave coherency matrix [J]. (Cloude, POLINSAR 2007)

10. Dual Polarised Entropy/alpha Decomposition Wave Coherency Matrix (H Transmit, H,V Coherent Receive - PALSAR) (V Transmit, H,V Coherent Receive -) (Cloude, POLINSAR 2007)

11. Dual Polarised Entropy/alpha Decomposition Related to 3x3 Polarimetric Coherency Matrix [T] (Cloude, POLINSAR 2007)

12. Dual Polarised Entropy/alpha Decomposition Scattering Angle and Entropy (Cloude, POLINSAR 2007)

13. Dual Polarised Entropy/alpha Space ... Genuine decomposation classess to be inverstigated Entropy H alpha (degrees)

14. Dual Polarised Entropy/alpha Decomposition Alpha (left) and Entropy (right) Maps of PALSAR Scene 381-6340 Acquired on 4 Oct 2008

15. AOI: a 10x10km Square (yellow box) alpha / Entropy / Intensity Forest/Non-forest

16. Partial Polarimetric Coherence Optimisation The coherence between two different polarisation channels: (Cloude & Papathanassiou, 1997) According to Reigber et al. (IGARSS 2008), HH-HV is clearly the better choice for all forested areas. where <> denotes spatial averaging, and contain the polarimetric information, while contain baseline dependent polarimetric and interferometric information. In the HH-HV pair, where and , total decorrelation over the forested areas is observed since the predominantly polarimetric decorrelation between the HH-HV polarised backscattered signals is from areas dominated by volume scattering.

17. Polarimetric Coherence Optimisation To solve the coherence optimisation problem, we must maximise the modulus of a complex Lagrangian function L defined as The maximisation problem can be described by setting the partial derivatives to zero. (Cloude & Papathanassiou, 1997) By solving these matrix equations, the estimates for and the optimal scattering mechanisms and the corresponding coherences in images i and j are obtained from the resulting eigenvalue problems

18. Partial Polarimetric Coherence Optimisation HV Coherence Optimised Coherence 0.9 0.1 0.5

19. Joint Processing of Dual Polarised Entropy/alpha Decomposition and Partial Polarimetric Coherence Optimisation 1). Generation of Entropy/alpha maps from PALSAR FBD SLC data implementing the above dual polarised Entropy/alpha decomposition algorithms; 2). Creation of the forest/non-forest discrimination map/mask using the Entropy/alpha classifier; 3). Coherence optimisation using multiple scattering mechanism approach described; 4). Non-forest region removal from the coherence map by the forest/non-forest mask derived from dual-pol Entropy/alpha maps; 5). Verification by in situ LiDAR forest canopy height data.

20. Optimised Coherence and LiDAR Canopy Height Map Optimised Coherence of AOI: a 10x10km Square with Non-forest Mask in White of HH-HV Pair Acquired on 19 Aug and 4 Oct 2008 LiDAR Forest Canopy Height Map of the AOI Acquired in Sept 2007: Blue – 0 meter, Green- 20 metres and Red – 40 metres. (Courtesy of Forestry Tasmania)

21. Optimised Coherence and LiDAR Canopy Height Map According to Le Toan (K&C 2010), the interferometric coherence ratio is sensitive to forest canopy height and the trend of coherence ratio is decreasing with a canopy height increase. That means the low coherence (red in left image) indicates a higher canopy height (red in right image). One reason for anomaly/inconsistency in some areas could be that the ground cover changed due to the different acquisition dates of radar and LiDAR data (one year gap).

22. Conclusions and Future Work Based on dual polarised Entropy/alpha decomposition and the partial polarimetric coherence optimisation, an integrated forest/non-forest discrimination and optimised coherence–forest height estimation method for producing forest extent change and trend information was proposed . Initial results on (a) the use of dual polarised Entropy/alpha decomposition for forest/non-forest discrimination, (b) optimised coherence of PALSAR dual-pol data as a source of information for forest height retrieval are consistent with in situ LiDAR forest height data. Future work will aim at quantitative analysis of accuracy of forest/non-forest discrimination and relation of coherence-forest height with help of reference data.

23. Thank you Zheng-Shu Zhou CSIRO Mathematics, Informatics and Statistics Phone: 08 9333 6189 Email: zheng-shu.zhou@csiro.au Web: www.cmis.csiro.au Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: enquiries@csiro.au Web: www.csiro.au