We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination ${{\bf R}^{\bf 2}}$ of 0.70, while the external validation indicates an RMSE of 13 t/ha with ${{\bf R}^{\bf 2}}$ of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study ( $ {>} {\bf 100}\;{\bf t/ha}$ ) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.

Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery

The capability of synthetic aperture radar (SAR) for estimating above-ground woody biomass in tropical savanna woodland is investigated, primarily through the estimation of canopy height from SAR interferometry (InSAR). The study area is a heterogeneous savanna woodland. Radar data used are AIRSAR C-band SAR interferometry (InSAR) and fully polarimetric L and P band SAR and Intermap Technologies Xband InSAR data. Results show that although X and C-band DSMs are indicative of general vegetation patterns, the height retrieval remains inaccurate due to the heterogeneity of the savanna woodland canopy. Initial results also show that L-hv and P-hv backscatter is dominated not only by high biomass areas, but also by areas of leafy palmetto. Keywords-biomass estimation, tree height, tropical savanna woodland;heterogeneous, digital surface model (DSM); AIRSAR, Intermap Technologies.

Airborne Synthetic Aperture Radar for Estimating Above-ground Woody Biomass in Tropical Savanna Woodland

The aim of this thesis is to evaluate the capability of radio detection and ranging (radar) data collected by the Advanced Land Observing Satellite (ALOS) Phased Array type Lband Synthetic Aperture radar (PALSAR), supported by field measurements obtained through ground survey, to predict and map Above Ground Woody Biomass (AGWB) in the tropical savannas of the developing country of Belize, and to understand how the forest structure may influence the backscatter observed. Firstly, an extensive inventory of the woody vegetation of the tropical savannas of Belize was created by measuring the diameter at breast height (dbh), the total height (ht) and the location of 6547 trees in plots covering a total woodland area of 30.8 hectares, located within four protected areas (the Rio Bravo Conservation and Management Area (11×1ha), Deep River (108×0.1ha) and Manatee Forest Reserve (1ha) and the Bladen Nature Reserve (1ha) and also from plots located in unprotected areas (7×1ha). These measurements of forest structure, when combined with information about forest management practices obtained from local organisations revealed that different forms of protection and management may lead to the development of pine woodlands with different structural characteristics in these savannas. Secondly, a case-study was conducted to establish the sensitivity of the ALOS PALSAR backscatter data to AGWB and determine the effect of sample plot size to their relationship. The findings of this case-study show that the L-band backscatter in these low density pine woodlands is a possible predictor of AGWB and confirm that the appropriate sample plot size for predicting AGWB is one hectare; while the sensitivity degrades significantly with decreasing sample plot size. Taken together, the findings described above were combined to assess the capability of ALOS PALSAR backscatter to predict AGWB in these woodlands. A semi-empirical Water Cloud Model (WCM) describing the interaction between the backscatter and vegetation was re-arranged to enable the prediction of AGWB. Non-linear regression analysis revealed that the ALOS PALSAR backscatter predicted AGWB with an R2=0.92; an external validation conducted with additional ground reference data estimated this AGWB prediction to have an RMSE ~13 t/ha. The form of the regression

Using satellite Earth observation & field measurements to assess the above ground woody biomass in the tropical savanna woodlands of Belize

Satellite SAR data offers land managers an affordable, all-weather capability for detailed land cover mapping. Visual
classification of these data may be more appropriate to the resource base in many developing countries and human
interpreters can often overcome problems of speckle more effectively than automated classification procedures. We
report work in progress on the visual interpretation of SAR data to classify land cover types within tropical savannas.
Airborne L-band SAR data for a region in Belize, Central America is degraded to approximate the single polarisation hh
and dual polarization hh/hv data that is expected from the ALOS PALSAR satellite sensor. Interpretations of these two
types of data by multiple interpreters were compared to explore how the number of polarizations, the effective spatial
resolution and the visual presentation of the SAR data affected the ability of interpreters to classify land cover. An
average classification accuracy of 78% for hh and 85% for hh/hv data were achieved for all classes and interpreters.
Denser high forest areas were accurately interpreted using both data sets, whilst a red-green colour composite of the
hh/hv data allowed grass dominated areas to be separated from areas of savanna woodland. Conclusions are drawn about
the benefits of certain presentations of backscatter data to assist visual interpretation.

Visual interpretation of synthetic aperture radar data for assessing land cover in tropical savannahs

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Visual interpretation of synthetic aperture radar data for assessing land cover in tropical savannahs