Result: Machine learning algorithms for optimization of image classification in spatially constrained regions: A case of Eritrea, East Africa

This paper presents the application of Machine Learning (ML) algorithms to solve the problem of optimization of classification tasks in Remote Sensing (RS) data processing. RS data is effective in spatial environmental monitoring since it enables detection of areas affected by natural hazards: droughts, desertification, coastal floods and deforestation. Vulnerable regions can be identified using analysis of spaceborne images for strategic land planning and decision making. The effectiveness of several ML models was tested using Geographic Resources Analysis Support System (GRASS) GIS software for satellite image analysis. Employing ML enabled to perform image classification tasks based on similarity of spectral reflectance of pixels. The following algorithms were tested and compared: Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), and Linear Discriminant Analysis (LDA). The ML models were adopted to classify a time series of the Landsat 8-9 OLI/TIRS images and evaluate changes in land cover types in coastal and desert areas of Eritrea. This region encompasses the protected Semenawi Bahri National Park, notable for a diverse range of unique wildlife near the Massawa Channel, Red Sea. The results demonstrated changes in land cover types over the period of 2014-2024 which proved the climate-related effects on landscape dynamics. This paper demonstrated the efficiency of the ML methods in Geographic Information Systems (GIS) tailored to solve specific spatially constrained problems of land cover type identifying using scripting in GRASS GIS.