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    pres_h2a_okconvert.ppt pres_h2a_okconvert.ppt Presentation Transcript

    • 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
    • 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.
    • 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
        • 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
    • 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’
    • 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
    • Eigenvector-Based Polarimetric Target Decomposition Eigenvectors / Eigenvalues Analysis Polarimetric Entropy Probabilities Unit Target Vector alpha (Cloude-Pottier, TGARS, 1997) Orthogonal Eigenvectors Real Eigenvalues
    • 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
    • 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)
    • Dual Polarised Entropy/alpha Decomposition Wave Coherency Matrix (H Transmit, H,V Coherent Receive - PALSAR) (V Transmit, H,V Coherent Receive -) (Cloude, POLINSAR 2007)
    • Dual Polarised Entropy/alpha Decomposition Related to 3x3 Polarimetric Coherency Matrix [T] (Cloude, POLINSAR 2007)
    • Dual Polarised Entropy/alpha Decomposition Scattering Angle and Entropy (Cloude, POLINSAR 2007)
    • Dual Polarised Entropy/alpha Space ... Genuine decomposation classess to be inverstigated Entropy H alpha (degrees)
    • Dual Polarised Entropy/alpha Decomposition Alpha (left) and Entropy (right) Maps of PALSAR Scene 381-6340 Acquired on 4 Oct 2008
    • AOI: a 10x10km Square (yellow box) alpha / Entropy / Intensity Forest/Non-forest
    • 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.
    • 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
    • Partial Polarimetric Coherence Optimisation HV Coherence Optimised Coherence 0.9 0.1 0.5
    • 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.
    • 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)
    • 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).
    • 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.
    • 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