Deforestation detection using a spatio-temporal deep learning approach with synthetic aperture radar and multispectral images

Abstract

Deforestation is a global change driver that contributes to atmospheric carbon emissions, causes biodiversity loss and ecosystem services degradation. Usually, this process has been quantified and monitored using remote sensing. The development of deep learning algorithms, as well as satellite images with a higher spatial and temporal resolution has improved the capabilities to monitor deforestation. The objective of this study was to evaluate the potential of a spatio-temporal deep learning algorithm, the U-Net 3D, with multispectral and synthetic aperture radar images to detect deforestation in a tropical rainforest in Southeast Mexico between 2019 and 2020. The U-Net 3D inputs were 147 squared areas with 256 pixels by side that contained, at least, one deforested area. These inputs consisted of: (1) four cloudless composites (Feb–Apr 2019, May–Sep 2019, Oct 2019–Jan 2020 and Feb–Apr 2020) with six bands each (four Sentinel-2 bands and two Sentinel-1), and (2) deforested area polygons delineated by visual interpretation. This classification had three classes: no deforestation, old-growth forest loss and secondary forest / plantation loss. The classification map obtained an overall accuracy of 0.97 and an average F1-score of 0.94. The corrected area estimates were 3 195.26 ± 1 132.88 ha (0.48 ± 0.17 %) for the old-growth forest loss and 4 234.85 ± 1 912.01 ha (0.64 ± 0.29 %) for the secondary forest / plantation loss. Most of the classification errors were found in borders between classes, caused by the confusion of secondary forest / plantation loss with herbaceous cover loss or associated with artifacts in the images.