Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km² region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale.

中文翻译：

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使用U-net卷积网络以高分辨率图像绘制大西洋雨林中的森林类型和干扰

在区域范围内绘制森林类型和树种的地图，为生态学家和森林管理者提供信息，这是遥感界面临的新挑战。在这里，我们评估了最近深度学习算法U-net卷积网络识别和分割（1）天然林和桉树人工林以及（2）造成森林干扰的指标-树种Cecropia hololeuca的潜力来自巴西大西洋雨林地区的WorldView-3卫星的高分辨率图像（0.3 m）。森林类型网络和Cecropia树网络分别用7611和1568红绿蓝（RGB）图像及其密集的标记蒙版进行了训练。百分之八十的图像用于训练，百分之二十用于验证。U-net网络对林类型进行了细分，总体精度> 95％，联合交叉点（IoU）为0.96。对于C. hololeuca，整体准确性为97％，IoU为0.86。预测是在1600 km ^2的区域内使用在0.3 m处全景放大的WorldView-3 RGB波段产生的。天然和桉树林占该地区森林总面积（82 250公顷）的79％和21％。切克罗皮亚冠覆盖了天然林冠层的1％。提出了一种基于切克拉皮树的空间分布来描述天然林碎片扰动程度的指标。我们的工作演示了深度学习算法如何支持区域规模的应用，例如植被，树木物种分布和干扰映射。