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Monitoring changes in tree cover for rapid assessment of deforestation isconsidered the critical component of any climate mitigation policy for reducingcarbon. Here, we map tropical tree cover and deforestation between 2015 and2022 using 5 m spatial resolution Planet NICFI satellite images over the stateof Mato Grosso (MT) in Brazil and a U-net deep learning model. The tree coverfor the state was 556510.8 km$^2$ in 2015 (58.1 % of the MT State) and wasreduced to 141598.5 km$^2$ (14.8 % of total area) at the end of 2021. Afterreaching a minimum deforested area in December 2016 with 6632.05 km$^2$, thebi-annual deforestation area only showed a slight increase between December2016 and December 2019. A year after, the areas of deforestation almost doubledfrom 9944.5 km$^2$ in December 2019 to 19817.8 km$^2$ in December 2021. Thehigh-resolution data product showed relatively consistent agreement with theofficial deforestation map from Brazil (67.2%) but deviated significantly fromyear of forest cover loss estimates from the Global Forest change (GFC)product, mainly due to large area of fire degradation observed in the GFC data.High-resolution imagery from Planet NICFI associated with deep learningtechnics can significantly improve mapping deforestation extent in tropics.

Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021