The necessity of mapping changes in land cover categories based on satellite imageries is a challenging task especially in terms of arable land and grasslands. The phenological phases of arable lands change quickly while grasslands is more stable. It might be hard to capture these changes regarding the spectral overlap between crops in full growth and grass itself. We have introduced a relatively simple processing workflow with good efficiency and accuracy. Our proposed method utilises the combination of a Multivariate Alteration Change Detection Algorithm and an existing boosting method, such as the AdaBoost algorithm with different weak learners and the most recent one – Extreme Gradient Boosting that is actually a relatively new approach in remote sensing. According to the results, the highest overall accuracy is 89.51 %. The proposed process workflow was tested on Landsat data with 30 m spatial resolution, using open-source software: R and GRASS GIS, Orfeo Toolbox library.
Key words: Remote Sensing, Machine Learning, MAD, Boosting, AdaBoost, Machine Learning, Object-Based Image Analysis