This study combines census, survey and bio-physical data to generate patially disaggregated poverty/biomass information for rural Uganda. It makes a methodological contribution to small area welfare estimation by exploring how the inclusion of bio-physical information improves small area welfare estimates. By combining the generated poverty estimates with national biophysical data, this study explores the correlation between poverty (welfare) and natural resource degradation at a level of geographic detail that has not been feasible previously. The precision of the resulting estimates of poverty have improved by the inclusion of bio-physical information and the poverty estimates appear to be more robust, as the standard errors show a decline in some cases by up to 40 percent. The coefficient of variation, that is, the ratio of the standard error and the point estimate decline in general as well. Overall, we conclude that the estimates of the poverty measures are more robust when biophysical information is taken into account. Part of the output from this study are maps showing poverty and biomass overlays for Uganda. These maps can be used as a planning tool and for targeting purposes.