论文标题
从2015年到2021
Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021
论文作者
论文摘要
监测树木覆盖的变化以快速评估森林砍伐是减少碳的任何气候缓解政策的关键组成部分。在这里，我们使用5 M空间分辨率行星NICFI卫星图像在巴西的Mato Grosso（MT）和U-NET深度学习模型上绘制2015年至2022年之间的热带树覆盖物和森林砍伐。该州的树木覆盖率为2015年的556510.8 km $^2 $（占MT州的58.1％），在2021年底将其减少到141598.5 km $^2 $^2 $^2 $（占总面积的14.8％）。2016年12月在2016年12月的最低限制区域中，2016年12月的最低限度较高，一定$ 6632.05 $^2 $^2 $^2 2 $^2 2 $^2 2 $^2 2 $^2 2 $^2 2 $^2 2 2和2019年12月。一年之后，森林砍伐的领域从2019年12月的9944.5 km $^2 $翻了一番，到19817.8 km $ $^2 $ 2021年12月。高分辨率数据产品与巴西官方森林损失的官方森林损失区域（67.2％）的官方森林损失区域相对一致的一致性相对一致，但范围较大的森林范围范围的变化是GF的全球变化。在GFC数据中观察到火灾降解。与深度学习技术相关的NICFI星球的高分辨率图像可以显着改善热带地区的砍伐程度。
Monitoring changes in tree cover for rapid assessment of deforestation is considered the critical component of any climate mitigation policy for reducing carbon. Here, we map tropical tree cover and deforestation between 2015 and 2022 using 5 m spatial resolution Planet NICFI satellite images over the state of Mato Grosso (MT) in Brazil and a U-net deep learning model. The tree cover for the state was 556510.8 km$^2$ in 2015 (58.1 % of the MT State) and was reduced to 141598.5 km$^2$ (14.8 % of total area) at the end of 2021. After reaching a minimum deforested area in December 2016 with 6632.05 km$^2$, the bi-annual deforestation area only showed a slight increase between December 2016 and December 2019. A year after, the areas of deforestation almost doubled from 9944.5 km$^2$ in December 2019 to 19817.8 km$^2$ in December 2021. The high-resolution data product showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from year 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 learning technics can significantly improve mapping deforestation extent in tropics.
