Deforestation remains a major concern with regard to climate change and the maintenance of biodiversity. Meanwhile, the development of new image processing techniques and the broad availability of high spatiotemporal resolution satellite imagery provide an unprecedented setup for the development of effective and scalable forest cover change monitoring systems. This is especially relevant in regions with large forested areas with high rates of deforestation, such as in the region of the Brazilian Amazon rainforest. In this context, existing forest cover change monitoring methods are based on a combination of visual inspection, spectral profiles, statistics, and machine learning techniques, which offer alternative backbones to deal with deforestation monitoring. Given the recent advances in the field of image processing by Fully Convolutional Neural Networks (FCNs), the objective of this study is to evaluate the performance of the U-Net architecture for the mapping of forest cover aimed at identifying deforestation polygons in multi-temporal satellite imagery. To this end, 10-m resolution imagery from the Sentinel-2 satellite covering portions of the Legal Amazon region were employed. The U-Net could identify and draw polygons of forest areas and forest fragments with high accuracy (0.9470), precision (0.9356), recall (0.9676), and F1-score (0.9513), thus outperforming largely applied and well-know supervised and unsupervised image classification methods. The results indicate and we further discuss that U-Nets have the potential to run as the backbone for efficient forest cover change monitoring initiatives and support the deployment of near real-time deforestation warning systems.

砍伐森林仍然是与气候变化和生物多样性维护有关的主要问题。同时，新图像处理技术的发展以及高时空分辨率卫星图像的广泛可用性为开发有效且可扩展的森林覆盖率变化监测系统提供了前所未有的条件。在森林面积大，森林砍伐率高的地区，例如在巴西亚马逊雨林地区，这一点尤为重要。在这种情况下，现有的森林覆盖变化监测方法是基于目视检查，光谱概况，统计数据和机器学习技术的组合，它们提供了替代性的主干来应对森林砍伐监测。鉴于全卷积神经网络（FCN）在图像处理领域的最新进展，本研究的目的是评估U-Net架构用于森林覆盖图绘制的性能，旨在识别多时相森林中的毁林多边形。卫星图像。为此，采用了来自Sentinel-2卫星的10米分辨率图像，该图像覆盖了合法亚马逊地区的部分区域。U-Net可以以较高的精度（0.9470），精度（0.9356），召回率（0.9676）和F1分数（0.9513）识别和绘制森林区域和森林碎片的多边形，从而胜过广泛应用且广为人知的监督和评估。无监督图像分类方法。