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

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