Deforestation Susceptibility Modelling Using Logistic Regression: In Buikwe District, Uganda.
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Forests are some of the most important ecosystems because of the services they offer, however, they continue to be lost due to both natural and man-made causes. Due to the forest loss scare, many studies have been done about the reduction in forest cover and the rate of destruction. These studies have left out predictability which is important for environmental managers. Predictive data shows areas of potential change, so that the environmental managers can focus their attention on the predicted areas. In Uganda, forests that require conservation priority need to be identified so that the National Forest Plan of 2011/12 to 2021/22 and the United Nations SDG 15 can be attained. Therefore, the main aim of this research was predicting areas in Buikwe district that are susceptible to deforestation. The specific objectives of this research were; to determine the causal factors of deforestation in Buikwe district, to determine the forest cover changes; (2011-2016) and (2016-2021), and to determine deforestation susceptibility zones in Buikwe district. The literature both in this field and related fields was reviewed to identify factors that influence deforestation. Decision on factors was done basing on the researcher’s findings, conclusions and recommendations. Forest cover was obtained by classifying Landsat images of different years. Logistic regression was used to analyse the relationship between the identified causal factors and deforestation. Factors with stronger relationships were selected to predict areas that are susceptible to deforestation. The accuracy of the model was assessed using a ROC curve. Causal identified factors were; slope and elevation, distance from; built-up areas, roads, and agricultural areas. Forests were covering 36%, 25% and 23% of Buikwe district in 2011, 2016 and 2021 respectively. Use of more advanced methods in future studies in this field was and revision of the Mabira forest management plan were recommended.