Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning-based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

Automated summary

Extensive use of drainage ditches in European boreal forests and North America has changed wetland and soil hydrology, impacting ecosystem functions. Mapping forest ditches is a priority for sustainable land use management. A deep learning-based methodology using airborne laser scanning data was developed and tested in Sweden, accurately mapping 86% of ditch channels. This technique provides a significant contribution to regional hydrology and ecosystem dynamics assessment.