Land Cover Mapping in Lake Rawa Pening Using Landsat 9 Imagery and Google Earth Engine

Abstract

Lake Rawa Pening, in Semarang Regency, is one of the super lakes of revitalization priority. Lake revitalization is an activity to restore the natural function of the lake as a water reservoir through lake dredging, cleaning of invasive alien plants, and land use planning. This makes land use and land cover information in Lake Rawa Pening useful for formulating policy strategies related to revitalization. This study will discuss land cover mapping in Lake Rawa Pening. Mapping using Landsat 9 Imagery and machine learning on Google Earth Engine (GEE). Machine Learning used in this study is CART and RF. The research result shows that the land cover map with the best accuracy is obtained from machine learning RF with an overall accuracy of around 0.78, while CART machine learning is approximately 0.76. The overall accuracy values for CART and RF are not much different because they are both decision tree-based machine learning. This research needs to be developed using cloud masking, comparing image transformations, and comparing its predecessor data, namely Landsat 8. This is useful for providing representative land cover data as the basis for the policy of revitalizing Lake Rawa Pening.