An automated approach for developing a regional-scale 1-m forest canopy cover dataset using machine learning and Google Earth Engine cloud computing platform

This study presents an automated framework for developing a regional-scale 1-m forest canopy cover (FCC) dataset using a machine learning technique and Google Earth Engine (GEE) cloud computing platform. By leveraging the power of machine learning algorithms and the GEE platform, this study offers a reliable and efficient solution for generating high-resolution FCC maps. The developed methodology has significant implications for managing natural resources by providing valuable information for forest resource management, conservation, and land-use policies at a regional or national level. It also has the potential to support large-scale forest inventory and monitoring, aiding in assessing forest health over time.