Knowledge of forest structure is key to understanding, managing, and preserving forest biodiversity and function. With the well-established need within the remote sensing community for better understanding of canopy structure, in this paper, the effectiveness of Sentinel-2 imagery for broad-leaved and coniferous forest classification within the Google Earth Engine (GEE) platform has been assessed. Here we used Sentinel-2 image collection from the summer period over North Macedonia when the canopy is fully developed. For the sample collection of the coniferous areas and the accuracy assessment of the classification, we used imagery from the spring period when the broad-leaved forests are in early green stage. Support Vector Machines (SVM) classifier has been used for discriminating forest cover groups, namely broadleaved and coniferous forests. According to the results more than 90% of the canopy in North Macedonia are broad-leaved, while less than 10% are conifers. The results in this study showed that with the use of Google Earth Engine, Sentinel-2 data alone can be effectively used to obtain rapid and accurate mapping of main forest types (conifers-broadleaved) with fine resolution.