Idris Ibrahim, Salman Salis Khalid, Hudu Hamza Musa, Nafisah Abdullahi Ahmed
Accurate mapping of cropland in urbanizing areas is critical for sustainable land use planning and climate-resilient urban development. In this study, the suitability of two machine learning algorithms, namely Random Forest and Gradient Tree Boosting, was tested and compared in the context of land use/land cover classification in the Abuja Municipal Area Council in Nigeria. The evaluation was carried out with the integration of multi-sensor data obtained from Sentinel-1 Synthetic Aperture Radar and Sentinel-2 optical imagery in the Google Earth Engine platform. In order to improve the overall accuracy of the classification results, an extensive feature set was developed and incorporated into the machine learning algorithms. The feature set was developed based on the integration of spectral and phenological features with texture features. The overall accuracy of the classification results obtained with the two algorithms was found to be satisfactory, with values above 77%. However, the overall accuracy obtained with the Gradient Tree Boosting classifier was slightly better at 77.5%, with a Kappa coefficient of 0.719. However, when the class-specific accuracy was evaluated, the Random Forest classifier was found to be better in the context of classifying the cropland class. The overall accuracy of the Random Forest classifier in classifying the cropland class was found to be better in terms of precision at 78.9%, recall at 75.7%, and F1 score at 77.2%. Therefore, the Random Forest classifier was selected for the final classification of the study area. The classified image obtained with the Random Forest classifier estimated the total extent of the cropland class to be around 47,924 hectares in the study area in the year 2025. The estimated extent of the cropland class accounts for around 27.1% of the total area. The results obtained in this study demonstrate the suitability of the integration of multi-sensor data in improving the overall accuracy of the classification results. In addition, the results obtained in this study demonstrate the importance of class-specific accuracy in the selection of the machine learning algorithms for the purpose of classifying the land cover class.Top of Form