Monitoring the forest fund lands of Kon Tum province, Vietnam using remote sensing data of Earth

Abstract

In recent years, cloud computing technology has become increasingly useful and practical in many fields, including forestry. Monitoring forest dynamics throughout a relatively large area requires collecting a large amount of input data, and processing it is very complex and time-consuming. In this study, we demonstrated the potential of applying cloud computing technology in the Google Earth Engine platform, in conjunction with remote sensing data to monitor forest land changes in Kon Tum province, Vietnam. The use of the javascript editor on Google Earth Engine (GEE) automated the process of collecting and processing remote sensing data to meet the specified criteria, while saving time, effort, and computer resources. Computing the normalized difference vegetation index and classifying land cover types using the Random Forest machine learning method on the GEE platform also showed accuracy in representing the distribution of vegetation cover and evaluating the status and changes in forest areas in Kon Tum province. The study showed that the policies of Kon Tum province administration in recent years have had a positive impact on restoring natural forest areas and reducing resource losses. So, the application of remote sensing data on the cloud computing platform of Google Earth Engine is a promising method for conserving and managing forest resources in Kon Tum province and throughout Vietnam