Regional mapping and monitoring land use/land cover changes: a modified approach using an ensemble machine learning and multitemporal Landsat data

Abstract

Regional mapping and monitoring of land use/land cover (LULC) still remain a challenge that depend on classifier and remote sensing data selected. This study aims to create precise LULC maps and explore the efficiency of an ensemble machine learning approach that integrates random forest (RF) and support vector machine (SVM). Two sets of remote sensing data were multi-temporal Landsat and a single scene from QuickBird covering the coastal area of the United Arab Emirates (UAE) were used. By training the classifier using samples collected from QuickBird and knowledge-based and optimal parameterization, the overall accuracy was enhanced from 70% to more than 90%. For the proposed approach, the result showed that the F1-score was 0.99. The results exhibited a rapid increase in all classes, accompanied by a significant change in the shoreline. The proposed approach has the potential to be applied to other regions and to produce accurate LULC maps.

Keywords

• uae
• lulc
• random forest
• support vector machine
• ensemble machine learning
• remote sensing
• change detection
• landsat