Using remote sensing and GIS to monitor land cover dynamics for Budongo Forest Reserve
Karungi, Ann Lylean
MetadataShow full item record
Studying land use dynamics of these forest reserves is essential for analysing various ecological and developmental consequences over time. This study aimed at analysing and modelling the temporal dynamics of land cover in Budongo forest reserve using remote sensing and GIS techniques. Land use classification and analysis was performed using GIS and Remote sensing technique, and GIS aided ‘Markov Cellular Automata’ technique was used to model the land use change and determine the magnitude, rate and dynamics of change in the spatial extent of the Budongo Forest Reserve. Seven multi-temporal datasets of 1990, 1995, 2000, 2005, 2010, 2014 and 2019 imageries were classified using maximum likelihood classifier. An accuracy of more than 80% was obtained for all the images. Land use Change Modeller (LCM) and Markovian processes were employed to analyze the pattern and trend of change. The results revealed that forested area decreased during 1990-2000 and increased in the in the period 2005-2019. The increase was evident in the northern part during the study period. Based on the past trend of land use changes from 1990 to 2019, the future land cover for the year of 2025 and 2030 were generated using Markov Cellular Automata’ technique in the Idrisi Selva. The model was validated using VALIDATE tool in Idrisi and Kappa statistics were computed. The results of Kappa statistics between the predicted and observed Land use land cover map in 2010 and 2019 showed high performance with the Kappa index exceeding 0.8 indicating high agreement between predicted and classified land cover maps. The model was then used to predict future land use and land cover maps of year 2025 and 2030 based using land cover maps of 2014 and 2019. Modelling predicted a continuous increase in the forest area in the northern part through 2030 and fluctuating trends for other land cover categories. The use remote sensing and GIS approach allowed us to quantify the extent of the land cover changes, the rate of change, land cover conversions as well as predicting in to the future. Thus GIS modelling would assist planners in identifying and monitoring land use trends and also provides decision support to promote proper socio-economic planning and environmental land management region so that the necessary measures to prevent the adverse effect can be introduced.