ABSTRACT

Coastal communities, land covers, and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid, and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. This paper develops and applies an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet’s 3 to 5 m) remote-sensing imagery, training a very deep convolutional neural network (holistically nested edge detection), to predict sequential vegetation line locations on annual to decadal timescales. Red, green, and near-infrared (RG-NIR) was found to be the optimum image spectral band combination during neural network training and validation. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (P_A) at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, United Kingdom, has identified a landward retreat rate >3 m year⁻¹ (2010–2020). Plausible vegetation lines were successfully retrieved from images in The Netherlands and Australia, which were not used to train the neural network, although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalizing to estimate retreat of sandy coastlines from Planet imagery in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, incorporating future shoreline change.

摘要

沿海社区，土地覆盖物和潮间带生境是侵蚀，洪水或两者兼而有之的脆弱受体。在未来十年的时间范围内，此脆弱性可能会随着海平面上升和更大的暴风雨而增加。因此，准确，快速，大规模地确定海岸线的位置及其迁移对于将来的沿海风险适应和管理至关重要。本文开发并应用了自动工具VEdge_Detector，以从高分辨率（Planet的3至5 m）遥感影像中提取沿海植被线，训练了非常深的卷积神经网络（整体嵌套边缘检测），以预测顺序每年到十年时间尺度上的植被线位置。红色，绿色，发现近红外（RG-NIR）是神经网络训练和验证期间的最佳图像光谱带组合。将VEdge_Detector的输出与从地面参考的位置测量值和人工数字化的航拍照片得出的植被线进行比较，这些植被线用于确定平均距离误差<6 m（两个图像像素）和> 84％的生产者精度（P _A）在七个地点中的六个地点。从英国萨福克Covehithe迅速退缩的悬崖海岸线的Planet影像中提取植被线，已确定陆地退缩速率> 3 m年^-1（2010–2020年）。尽管在荷兰和澳大利亚的影像中成功地找到了有条理的植被线，但这些植被线并未用于训练神经网络，尽管VEdge_Detector证明对裸露的岩石海岸线的重要区域收效较差。因此，该方法有望实现在缺乏地面参考测量值的其他数据贫乏地区普遍从行星影像估算沙质海岸线退缩的可能性。与更传统的非自动化方法相比，可以快速，有效地产生源自VEdge_Detector的植被线输出。这些产出还可能为将来的一系列沿海风险管理决策（包括未来的海岸线变化）提供依据。