The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
Appr. One 
Appr. Two 
Appr. Three 
8.5+4.2m 
18.7±6.1m 
17.5±4.2m 
Table 3. Estimated tree height from INDREX-II Data 
& ' 
the ground roughness was increased such that dihedral return 
was severely limited. In these instances, the calculated 
coherence region becomes more circular and the resulting 
ground phase projections become noisier. This in turn affects 
the forest height estimation. 
4. CONCLUSIONS 
Three different approaches to forest height estimation have 
been tested on both simulated and real L-Band PolInSAR data. 
The results from the simulated data are quite encouraging. The 
forest height estimated by the DEM differencing is normally 
underestimated at a level of about 2/3 of the true forest height. 
The results from the other two approaches are very similar to 
each other and are within about 10% of the simulated canopy 
height provided that ground return (dihedral bounce) is 
adequate. It should be noted that with the simulated data set, 
there are no negative effects caused by temporal decorrelation 
or other factors associated with real data. 
The results from real data are compared against ground 
measurements. It is concluded that the estimated forest height 
from 2-D search approach is quite close to the average h' W o, 
roughly within 10% error. The combined approach is slightly 
worse than the 2-D search approach, getting about 15% but with 
a significant reduction in the computational load. On the other 
hand, the height estimates from real dataset are considerably 
noisier and less robust than for the simulated data. It is 
suggested that the comparative noisiness and additional biases 
are due to some temporal decorrelation of the repeat pass data, 
and to additional attenuation of the signal in the lower part of 
the canopy due to a dense understory that is not incorporated 
into the RVOG model. 
ACKNOWLEDGMENT 
Figure 7 Estimated forest height from INDREX-II data using 
three approaches: (a) DEM differencing; (b) 2-D search; (c) 
Combined. The region inside the black box is used for statistics. 
The two “+” signs are the locations of two tree transects. 
From Figure 7 and Table 3, it is observed that, assuming the 
average tree height in the region is 20m (i.e., < h'/otP), the 
DEM differencing approach is estimating about 42% of <h' IO o->- 
The 2-D search approach gives the best result, 95% and the 
combined approach 88%. This is at a similar level to the results 
from the simulated dataset. If the results, on the other hand, are 
referenced to <h I0 d> rather than </?W> an additional 3 meter 
underestimate is observed. At this point there is no strong 
argument to prefer the one metric over the other as the more 
representative canopy height. 
However, the estimated tree height from INDREX-II L-Band 
dataset is much noisier than those from simulated dataset 
mainly due to the noisier estimation of ground elevation. 
Although space precludes showing them here, the elliptical 
regions estimated from this dataset are typically much smaller, 
with lower coherence, rounder appearance and show an 
apparent lack of dihedral bounce. Two major contributors to 
this are likely: (1) temporal decorrelation, (2) the dense 
understory of the forest, which may act to attenuate the dihedral 
response of the larger trees. Consequently the phase separation 
was reduced resulting in poorly shaped coherence regions and 
inaccurate estimation of ground phase. This has been observed 
in results from other PolSARproSim simulated datasets, where 
We thank European Space Agency for providing the data, 
Alberta Ingenuity Fund for partial financial support, and Dr. 
Mark Williams for valuable comments regarding 
PolSARproSim. 
REFERENCE 
Cloude, S.R., Papathanassiou, K.P., 1998. Polarimetric SAR 
Interferometry, IEEE Transactions on Geoscience and Remote 
Sensing, Vol. 36, No.5, pp.1551-1565. 
Cloude, S.R., Papathanassiou, K.P., 2003. Three-stage inversion 
process for polarimetric SAR interferometry, IEE Proceedings - 
Radar Sonar Navigation, Vol. 150, No. 3, pp. 125-134. 
Cloude, S.R., 2006. Polarization coherence tomography, Radio 
Science, Vol. 41, RS4017, 2006. 
Cloude, S.R., Zhang, Q., Mercer, B., 2007. Tropical Forest 
Structure Estimation using L-Band Polarization Coherence 
Tomography (PCT). Proceedings of 5th International 
Symposium on Retrieval of Bio- and Geophysical Parameters 
from SAR Data for Land Applications. September 25-28, 2007, 
Bari, Italy. 
Hjansek, I., Kugler, F., Papathanassiou, R., Scheiber, K., Horn, 
R., Moreira, A., Hoekman, D., Davidson, M., Attema, E., 2005a. 
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