High-accuracy land use and land cover maps (LULC) are increasingly in demand for environmental management and decision-making. Despite the limitation, Machine learning classifiers (MLC) fill the gap in any complex issue related to LULC data accuracy. Visualizing land-cover information is critical in mitigating Côte d’Ivoire’s deforestation and land use planning using the Google Earth Engine (GEE) software. This paper estimates the probability of RF classification in South Western Côte d’Ivoire. Landsat 8 Surface Reflectance Tiers 1 (L8OLI/TIRS) data with a resolution of 30 mn for 2020 were used to classify the western and southwestern Forest areas of Côte d’Ivoire. The Random Forest (RF) learning classifier was calibrated using 80% training data and 20% testing data to assess GEE classification accuracy performance. The findings indicate that the Forest land class accounts for 39.48% of the entire study area, followed by the Bareland class, the Cultivated land class 21.28±0.90%, the Water class 1.94±0.27%, and the 0.96±0.60% Urban class respectively. The classification reliability test results show that 99.85%±1.95 is the overall training accuracy (OTA), and 99.81±1.95% for the training kappa (TK). The overall validation accuracy (VOA) is 94.02±1.90%, while 92.25±1.88% validation kappa (VK) and 92.45±1.88% RF Accuracy. The different coefficients classification accuracy results obtained from the RF confusion matrix indicate that each class has three good performances. This is due to the cultivated land samples lower spatial resolution and smaller sample numbers, resulting in a lower PA for this class than for the other classes. All had producer accuracy (PA) and user accuracy (UA) more than 90% using the L8OLI/TIRS data. Using the RF-based classification method integrated into the GEE provides an efficient and high scores accuracy for classifying land use and land cover in the study area.

No abstract

No abstract