Full text: Technical Commission VIII (B8)

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Figure 2c: Tomogram along Azimuth (3) 
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Figure 3a: Tomogram along Range (A) 
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Figure 3b: Tomogram along Range (B) 
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Figure 3c: Tomogram along Range (C) 
The tomograms of the forested patches indicate scattering 
occurring relatively uniformly throughout the canopy. In 
contrast, the regrowth areas show more intense scattering from 
the ground area than the upper canopy. This may reflect the 
presence of more ‘double-bounce’ contributions from the lower 
portion of the canopy. 
At this point there is no direct *truth' with which to make a 
comparison of radar structure function. However it should be 
noted that the forest patches are relatively homogeneous such 
that it would be surprising to see large tomographic variations 
across this data set. A scene containing mixed species will be 
addressed shortly. 
5. CONCLUSIONS 
The worldwide destruction of forest as a source of CO2 
emissions into the atmosphere is a major driver for attempts to 
determine the existing forest biomass content with increasing 
accuracy and spatial resolution. In previous work it has been 
shown that derivation of forest height using POLInSAR 
methods, together with use of an allotropic expression relating 
height to forest biomass could produce good results in the 
context of relatively homogeneous forests. In this paper we 
provide extensions to that work, utilizing a technique whereby 
3D tomograms can be determined using a data set from an 
airborne, single-baseline, L-Band polarimetric InSAR system. 
The longer-term objective is to demonstrate that knowledge of 
the canopy structure function will allow for biomass 
differentiation in non-homogeneous forests. In this paper we 
show the first tomograms in the same forest area used for the 
previously noted biomass extraction. These examples show, 
qualitatively at least, that structural variation does occur even in 
  
  
  
    
  
     
  
     
  
  
  
  
  
   
    
     
   
   
    
    
  
    
  
     
   
    
   
    
    
  
   
    
  
  
  
   
   
    
   
   
  
    
    
    
      
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
the relatively homogeneous lodgepole pine forest test area 
being addressed. 
Our study will be extended to various forest types in Canada 
and is expected to be applicable in various forest types over 
different tropical regions as well. Additionally, a novel dual 
baseline approach using the same system will be examined to 
determine if the expected improvement in structure function 
resolution can be achieved. Lastly, classification algorithms 
relating biomass to the structure function information will be 
developed and tested. 
6. ACKNOWLEDGMENTS 
The authors would like to acknowledge Alberta Innovates 
Technology Futures for funding this research. We are 
particularly grateful to Intermap Technologies Corp., Calgary, 
for providing the invaluable data and technical support. 
7. REFERENCES 
Bamler, R., 1997. Digital Terrain Models from Radar 
Interferometry, Photogrammetric Week, Stuttgart, Germany. 
Cloude S.R, 2006A. Polarization Coherence Tomography. 
Radio Science. Vol.41, RS4017. 
Cloude S.R, 2006B. Polarization Coherence Tomography, in 
Proceedings of 6th European SAR Conference, EUSAR 06, 
Dresden, May. 
Cloude, S.R., 2009. Polarisation: Applications In Remote 
Sensing. Oxford University Press. 
Dong J., Kaufmann k., Myneni R., Tucker C., Kauppi P., Liski 
J., Buermann W., Alexeyev V., Hughes M., 2003. Remote 
sensing estimates of boreal and temperate forest woody 
biomass: carbon pools, sources, and sinks. Remote Sensing of 
Environment, Vol.84, pp. 393-410. 
Hajnsek I, Papathanassiou K.P., 2009. Tropical-Forest- 
Parameter Estimation by Means of POLinSAR: the INDREX-II 
Campaign. [EEE Transactions on Geosciences and remote 
Sensing, Vol 47, No.2. 
Houghton, R.A., F. Hall, and S.J. Goetz. 2009. Importance of 
biomass in the global carbon cycle. Journal of Geophysical 
Research 114. 
Leckie, D. G., and K. J. Ranson, 1998. Forestry applications 
using imaging radar. Principles and Applications of Imaging 
Radar, Vol. 2, 3d ed. Manual of Remote Sensing, F. M. 
Henderson and A. J. Lewis, Eds., John Wiley & Sons, 435—509. 
Lefsky M.A., Harding D.J., Keller M., Cohen W.B., Carabajal 
C.C., Del Bom Espirito-Santo F., Hunter M.O. and De Oliveira 
J.R., 2005. Estimates of forest canopy height and aboveground 
biomass using ICESat. Geophysical Research Letters, Vol. 32, 
L22S02. 
Le Toan, T., Beaudoin, A., Riom, J. and Guyon, D., 1992 
Relating Forest Biomass to SAR Data. IEEE Transactions on 
Geoscience and Remote Sensing, Vol. 30, No. 2, pp.403-411. 
  
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