Peat Drainage Ditch Mapping from Aerial Imagery Using a Convolutional Neural Network

Abstract

This study trialled a convolutional neural net (CNN)-based approach to mapping peat ditches from aerial imagery. Peat ditches were dug in the last century to improve peat moorland for agriculture and forestry at the expense of habitat health and carbon sequestration. Both the quantitative assessment of drained areas and restoration efforts to re-wet peatlands through ditch blocking would benefit from an automated method of mapping, as current efforts involve time-consuming field and desk-based efforts. The availability of LiDAR is still limited in many parts of the UK and beyond; hence, there is a need for an optical data-based approach. We employed a U-net-based CNN to segment peat ditches from aerial imagery. An accuracy of 79% was achieved on a field-based validation dataset indicating ditches were correctly segmented most of the time. The algorithm, when applied to an 802 km2 area of the Flow Country, an area of national significance for carbon storage, mapped a total of 27,905 drainage ditch features. The CNN-based approach has the potential to be scaled up nationally with further training and could streamline the mapping aspects of restoration efforts considerably.

Keywords

• peatlands
• ditch-blocking
• CNN
• carbon