The National Biomass Study of the Ministry of Water, Lands and the Environment. This information covers changes in land cover such as tree plantation, coniferous plantations, tropical high forests woodlands, grasslands, wetlands, water resources and land use such as subsistence and commercial farmland, and changes in landscape among other aspects. In the National Biomass Study (NBS) project, the country was split into 9,000 plots with 3 sample plots at each intersection. This information details the woody biomass stock for each plot and it can be used to assess the relationship between tree cover and poverty. The data is extremely rich in bio-physical factors and also includes the distribution of infrastructure like markets, roads, schools and others. Besides, the GIS format of the data allows us to explore the possibilities of merging the datasets using GIS variables.
In The Ugandan situation two decades ago, the country was faced with deteriorating economic, social and environmental conditions. The approach we use to link these problems uses statistical estimation techniques. Our approach to the analysis of the links between poverty and biomass using maps begins with the construction of a poverty map. A test is done to compare the means for the survey and census variables and the variables that pass the significance test are considered for the regression analysis. A logical next step is to make the connection between welfare and biophysical information. Obtaining information on biomass use for administrative units is not straightforward, because of confidentiality, different data formats, the intricacies of geo-analysis and because environmental conditions do not follow administrative boundaries. The preliminary poverty estimates for rural Uganda control for spatial autocorrelation solely by relying on Population Sampling Unit or (PSU) means calculated from the census. By controlling for biophysical characteristics of the estimation procedure, the efficiency of the derived poverty estimates may be improved, leading to more precise estimates and enhancing the level of spatial disaggregation that is attainable.