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Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning
Swedish University of Agricultural Sciences.
Swedish University of Agricultural Sciences.
Jonkoping University.
Umea University.
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2023 (English)In: Journal of irrigation and drainage engineering, ISSN 0733-9437, E-ISSN 1943-4774, Vol. 149, no 3, article id 04022051Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2023. Vol. 149, no 3, article id 04022051
Keywords [en]
Ditches, Channel, airborne laser scanning, Deep learning, Semantic segmentation
National Category
Forest Science Remote Sensing
Identifiers
URN: urn:nbn:se:bth-24303DOI: 10.1061/JIDEDH.IRENG-9796ISI: 000922209100003OAI: oai:DiVA.org:bth-24303DiVA, id: diva2:1739175
Funder
Vinnova, 2014-03319Swedish Research Council Formas, 2019-00173Swedish Research Council Formas, 2021-00115Available from: 2023-02-24 Created: 2023-02-24 Last updated: 2023-02-24Bibliographically approved

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Lavesson, Niklas

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