Automatic forest cover classification using Sentinel-2 multispectral satellite data and machine learning algorithms in Google Earth Engine
Abstract
Forest cover plays an essential role in maintaining ecological equilibrium, mitigating climate change, and securing a sustainable future for both humanity and the planet. Most countries conduct forest inventory or remote sensing surveys every few years to monitor changes in forest cover.
However, only a few initiatives offer more frequent updates, typically weekly or monthly, focusing exclusively on areas experiencing high rates of deforestation or those of significant ecological value. The present study focuses on the classification of forest cover throughout Slovakia, covering the period 2017-2022, using Sentinel-2 multispectral satellite imagery along with the machine learning (ML) algorithms Random Forest (RF) and Support Vector Machine (SVM).
The computation was performed in the cloud-based Google Earth Engine (GEE) platform, which offers a versatile interface for a broad range of computational capabilities for geospatial analysis and landscape monitoring. Forest cover change processing and evaluation is based on the RF classification algorithm, which demonstrated higher accuracy than the SVM classifier.
The results indicate that RF outperformed SVM by 4% and 21% in 2017 and 2020, respectively. The RF algorithm achieved an overall accuracy (OA) of 95% in both classification cases (2017 and 2020) and F1 score of up to 0.95.
The selected RF algorithm revealed an increase in forest cover in Slovakia, particularly notable during the period 2017-2019, with a slight decrease detected between 2019 and 2020. Furthermore, it was determined that the current forest cover is lower than that reported in official state statistics and land cover databases.
Additionally, a user-friendly automatic tool for forest cover classification was developed and made freely available in GEE. This tool can benefit foresters, urban planners, and everyday users by detecting subtle changes in forest cover, crucial for forest sustainability and human well-being.