Assessment and Prediction of Land Use/Land Cover Change in the National Capital of Burundi Using Multi-temporary Landsat Data and Cellular Automata-Markov Chain Model

• Main Authors: Audace Ntakirutimana, Chaiwiwat Vansarochana
• Format: Article
• Language: English
• Published: Mahidol University 2021-08-01
• Series: Environment and Natural Resources Journal
• Subjects: gitega district; land degradation; kappa statistics; simulation; land change modeler; geoinformatics
• Online Access: https://ph02.tci-thaijo.org/index.php/ennrj/article/view/244689

Gitega District has experienced significant land use and land cover changes due to human activity. This has increased land degradation and environmental issues. However, there is no data on LULC change to guide land-use planning. This study assessed the rate and magnitude of LULC change over the last 35 years and also simulated future scenarios using Geoinformatics. In the first step, five LULC classes were extracted from satellite images from 1984, 2002, and 2019 using the supervised classification method. Overall accuracy and Kappa statistics of more than 85% and 82% respectively were achieved with 30 reference samples. Change analysis highlighted by Land Change Modeler (1984-2019) indicated a significant increase in Agriculture of 94 km2, a slight increase in Shrub Land and Built-up Area of 5.5 km2 and 2 km2, respectively; and a steep decrease in Trees Cover and Grass Land of 62.5 km2 and 39 km2, respectively. Markov Chain and CA-Markov models were further calibrated to simulate LULC changes in 2038 and 2057 using the 2019 base map. Evaluation and analysis of 2019-2057 simulation results showed a moderate agreement of 75% for Kappa and the same trends of LULC change: Trees Cover, Grass Land, and Shrub Land will decrease by 11.5 km2, 13 km2, 11.5 km2 respectively, whereas Agriculture and Built-up Area will increase by 30 km2 and 6 km2 respectively in 2057. These study outcomes can support decision-making towards restoration measures of land degradation and long-term environmental conservation in the region.