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With its vast expanse, exceeding that of Western Europe by twice, the Amazonrainforest stands as the largest forest of the Earth, holding immenseimportance in global climate regulation. Yet, deforestation detection fromremote sensing data in this region poses a critical challenge, often hinderedby the persistent cloud cover that obscures optical satellite data for much ofthe year. Addressing this need, this paper proposes three deep-learning modelstailored for deforestation monitoring, utilizing SAR (Synthetic Aperture Radar)multitemporal data moved by its independence on atmospheric conditions.Specifically, the study proposes three novel recurrent fully convolutionalnetwork architectures-namely, RRCNN-1, RRCNN-2, and RRCNN-3, crafted to enhancethe accuracy of deforestation detection. Additionally, this research exploresreplacing a bitemporal with multitemporal SAR sequences, motivated by thehypothesis that deforestation signs quickly fade in SAR images over time. Acomprehensive assessment of the proposed approaches was conducted using aSentinel-1 multitemporal sequence from a sample site in the Brazilianrainforest. The experimental analysis confirmed that analyzing a sequence ofSAR images over an observation period can reveal deforestation spotsundetectable in a pair of images. Notably, experimental results underscored thesuperiority of the multitemporal approach, yielding approximately a fivepercent enhancement in F1-Score across all tested network architectures.Particularly the RRCNN-1 achieved the highest accuracy and also boasted halfthe processing time of its closest counterpart.

Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images