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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.
Vise andre og tillknytning
2023 (engelsk)Inngår i: Journal of irrigation and drainage engineering, ISSN 0733-9437, E-ISSN 1943-4774, Vol. 149, nr 3, artikkel-id 04022051Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
American Society of Civil Engineers (ASCE), 2023. Vol. 149, nr 3, artikkel-id 04022051
Emneord [en]
Ditches, Channel, airborne laser scanning, Deep learning, Semantic segmentation
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-24303DOI: 10.1061/JIDEDH.IRENG-9796ISI: 000922209100003OAI: oai:DiVA.org:bth-24303DiVA, id: diva2:1739175
Forskningsfinansiär
Vinnova, 2014-03319Swedish Research Council Formas, 2019-00173Swedish Research Council Formas, 2021-00115Tilgjengelig fra: 2023-02-24 Laget: 2023-02-24 Sist oppdatert: 2023-02-24bibliografisk kontrollert

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Lavesson, Niklas

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