Validation of Random Forest Algorithm to Monitor Land Cover Classification and Change Detection using Remote Sensing Data in Google Earth Engine

Monitoring land cover classification and change detection based on remote sensing images using a machine learning algorithm has become one of the important factors. For our case study, we select Vientiane capital as the study area. Our proposed method aims to perform the land cover classification using random forest algorithm supervised classification in Google Earth Engine (GEE) and post classification comparison (PCC) of change detection using Arc GIS software between 1990 and 2020 with 5 years interval periods are evaluated. In this paper, we utilize GEE combining with multiple sources of satellite optical images time-series from three main satellites, Landsat 5, Landsat 8, and Sentinel 2 integrating with multiple spectral, spatial, temporal, and textural features. Spectral indices such as NDVI and NDBI are calculated to enhance the accurate performance. Our results show that all six classes are obtained highly accurate land cover classification, with overall accuracy over 97.73% for training data and 90.35% for testing data, and kappa statistic of 0.97 for training data and 0.87 for testing data in 2020.