TropiSAR data analysis and biomass inversion

Transcription

1 TropiSAR data analysis and biomass inversion Thuy Le Toan, Ludovic Villard, Yannick Lasne,Thierry Koleck CESBIO Toulouse, France Réunion TOSCA-TropiSAR January 2011

2 Outline Data analysis Revisiting the backscatter normalisation Biomass inversion using intensity Biomass inversion using intensity and height Summary and further works

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4 High temporal stability of γ HH over forests Total dynamic range : ~15 db, dynamic range over forests ~5 db

5 High temporal stability of γ VV over forest Total dynamic range: ~10-12 db, dynamic range of forests < 4 db

6 High temporal stability of γ HV over forests Total dynamic range: ~16 db, dynamic range over forests ~8 db

7 Normalisation of backscattering coefficient for dense forests & Topographic correction

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9 Introduction of a specific normalization : alpha0 coefficient We conclude that the scattering from the volume dominates Hence the angular variation should be corrected with a specific normalization, accounting for the volume delineated by the penetration length : Introduction of alpha0 :

10 Topography correction for alpha0 : How to correct the alpha0 normalization to account for ground slopes? 2 hypothesis : the top canopy surface follows the ground topography the vegetation has azimuthal symmetry

11 Topography correction for alpha0 :

12 when α 0 HV or γ 0 HV backscattering parameter should be considered in biomass retrieval? To invert the whole scene with high biomass, low biomass forest and non forest areas, a strategy is to compute α 0 HV when scattering is dominated by volume effect (rather than surface) and γ 0 HV when the signal can reach the ground level. Various indicators are under investigation 1/ Polarimetric analysis: Preliminary results did not show the copolarized phase difference as relevant indicator for separating ground and volume components. Others phase indicators will be investigated further. 2/ SAR Tomography Assessment of the ground component by retrieving ground phases 3/ PolInSAR analysis : Use of the phase center separation 4/ Intensity analysis To exploit the differences in mechanisms of HH, VV and HV related to volume and ground scattering

13 Ground Volume Ground Volume (Tebaldini, Rocca,Polimi)

14 Biomass indicators under test γ HV Intensity at HV Volume scattering But with topographic effect

15 Paracou, French Guiana 1 db threshold

16 BIOSAR 1 Remningstorp Boreal forest Gamma HH (db) Gamma VV (db) Differences in Backscatter 6 5 Diff in Gamma (db) HH - VV (March) HH - VV (April) HH - VV (May) ,3 75, ,5 167,1 170,1 171,1 212,7 228,5 253,2 257,6 272,6 272,7 277,8 290,1 Biomass (tons/ha)

17 Comparison with other data sets α HV α HV γ γ HV HV

18 20% of error in biomass

19 Biomass inversion The retrieval method is based on the inversion of the empirical relationship between biomass B and the backscatter coefficient γ ( a ) log10 B = γ + HV γ is in db and B in ton/ha. a 0 is a constant in db which accounts for the calibration between different datasets. For unsupervised inversion a 0 =

20 Statistical assessment of the inversion results

21 Statistical assessment of 15 plots of 250 m x 250 m RMSD (t/ha) RMSD (%) MPE (%) r P r s

22 RMSD (t/ha) RMSD (%) MPE (%) r P r s

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24 Summary of γ and α inversion results over 15 reference plots Method RMSD (t/ha) RMSD (%) MPE (%) r P r s γ unsupervised γ with calibration (a 0 = db) α unsupervised α unsupervised with topo correction α with calibration and topo correction

25 Bayesian inverse approach Proposed to better handle various sources of errors and to improve the retrieval at high biomass by using multiple biomass indicators The available information for biomass inversion is summarised by the posterior probability distribution biomass P(B D meas ) SAR measurements Intensity Polarimetry Interferometry Ecological knowledge Hypothesis: relationships between biomass and measurements D theo = f (B) Soil Climate D meas = R D theo Random noise not considered in the model

26 Illustration of the improvement in inversion when different SAR measurements are used Use of HV and PolInSAR height and/or polarimetric measurements Estimators that can be used to estimate biomass: Maximum Likelihood or conditional mean estimators. For the conditional mean, used to minimise Mean Square error: B estimmmse = K B B P( R) D theo = B B B P( R) D theo theo P( R) D Using multiple information sources, for example intensity and PolInSAR height B estimmmse = K K 1 2 B B P( R D 1 1theo ) P( R D 2 2theo )

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28 Bayesian inversion : using αhv and Lidar canopy height at 30 plots of ha. Biomass-Height relationships are derived from 30 subplots. Validation over 30 other subplots

29 Works to be done 1. To continue topographic correction, with degraded resolution (SRTM) 2. To assess uncertainty taking into account various sources of error (e.g. in situ biomass) To assess geophysical error 3. Bayes inversion with Pol-InSAR height. Investigations on other biomass indicators (polarimetry, interferometry) 1. Verification of alpha normalisation on other sites (e.g. Landes) and refining the general curve

30 The end

31 Plot 11: Undisturbed forest Biomass= 428 t/ha Tree height from tomography (L. Ferro-Famil) Plot 10: Disturbed forest (40 trees > 40 cm removed in 1987) Biomass= 310 t/ha

32 Summary and Lesson learnt Sensitivity of SAR intensity to biomass after 300 t/ha quantified: exploitable but requires high radiometric accuracy Preliminary results at Paracou show that error of 20% in the retrieved biomass could be reached

33 A scheme for uncertainty assessment D meas = R. f (B) The random noise R can be discomposed into different noise sources, e.g. = error due geophysical uncertainty the in relationship RGeo (e.g. due to nuisance factors such as forest type, topography, changes in conditions, error in in situ biomass) = error due to statistical properties of radar images (e.g. speckle, RRadar instrument noise) More generally, the pdf of the noise sources can be expressed by a joint density function of multiple noise sources P( R, R,... R ) = 1 2 n i i i= 1 n P ( R )

34 Mapping of tropical forest biomass: a challenging issue Earlier results (e.g. Imhoft 1995) reported on lower biomass saturation level, even at P band ( t/ha) Recent works using BioSAR-1 data over boreal forests proved that it is possible to retrieve biomass from P-band airborne SAR up to 300 t/ha Question: Can we map high biomass in tropical forests from P- band SAR? Since tropical forests play an important role in the terrestrial carbon budget, the TropiSAR campaign is helping to address one of the key objectives of BIOMASS