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Dec 20, 2022

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

Authors: William Lidberg, Ph.D. https://orcid.org/0000-0001-5780-5596 [email protected], Siddhartho Shekhar Paul, Ph.D. https://orcid.org/0000-0001-5243-8416, Florian Westphal, Ph.D. https://orcid.org/0000-0002-2161-7371, Kai Florian Richter, Ph.D. https://orcid.org/0000-0001-5629-0981, Niklas Lavesson, Ph.D., Raitis Melniks, Ph.D. https://orcid.org/0000-0002-1690-4915, Janis Ivanovs, Ph.D. https://orcid.org/0000-0002-1700-4433, Mariusz Ciesielski, Ph.D., Antti Leinonen, Ph.D. https://orcid.org/0000-0002-7074-9020, and Anneli M. Ågren, Ph.D. https://orcid.org/0000-0002-6758-3971Author Affiliations
Publication: Journal of Irrigation and Drainage Engineering
Volume 149, Issue 3
https://doi.org/10.1061/JIDEDH.IRENG-9796
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Abstract

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.

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Data Availability Statement

Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. The code and a reproducible example are available from github (Lidberg et al. 2021). The data are available from Mendely data (Ågren et al. 2022), doi: 10.17632/zxkg43jsx8.1.

Acknowledgments

We thank Liselott Nilsson, Eliza Hasselquist, Gudrun Norstedt, Lars Strand, Anders Hejnebo, Björn Lehto, Magnus Martinsson, Marcus Björsell, Tobias Johansson, Andrew Landström, and Catarina Welin for spending 513 h digitizing the ditches used to train the model in this study. Further, we thank Jerry Lidberg for lending us his gaming graphics card. The study was funded by KEMPE, WASP-HS, SMHI, Vinnova (2014-03319), Formas (2019-00173 and 2021-00115), and the Interreg project: Water Management in Baltic Forests.

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Information & Authors

Information

Published In

Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 149 • Issue 3 • March 2023

Copyright

This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.

History

Received: Oct 26, 2021
Accepted: Sep 25, 2022
Published online: Dec 20, 2022
Published in print: Mar 1, 2023
Discussion open until: May 20, 2023

Authors

Affiliations

William Lidberg, Ph.D. https://orcid.org/0000-0001-5780-5596 [email protected]
Assistant Professor, Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå 90183, Sweden (corresponding author). ORCID: https://orcid.org/0000-0001-5780-5596. Email: [email protected]
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Siddhartho Shekhar Paul, Ph.D. https://orcid.org/0000-0001-5243-8416
Postdoctoral, Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå 90183, Sweden. ORCID: https://orcid.org/0000-0001-5243-8416
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Florian Westphal, Ph.D. https://orcid.org/0000-0002-2161-7371
Assistant Professor, Dept. of Computing, School of Engineering, Jönköping Univ., Gjuterigatan 5, Jönköping 55111, Sweden. ORCID: https://orcid.org/0000-0002-2161-7371
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Kai Florian Richter, Ph.D. https://orcid.org/0000-0001-5629-0981
Associate Professor, Dept. of Computing Science, Umeå Univ., Umeå 90187, Sweden. ORCID: https://orcid.org/0000-0001-5629-0981
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Niklas Lavesson, Ph.D.
Professor, Dept. of Software Engineering, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona 37142, Sweden.
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Raitis Melniks, Ph.D. https://orcid.org/0000-0002-1690-4915
Ph.D. Candidate, Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ 111 Rigas St., Salaspils LV-2169, Latvia. ORCID: https://orcid.org/0000-0002-1690-4915
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Janis Ivanovs, Ph.D. https://orcid.org/0000-0002-1700-4433
Ph.D. Candidate, Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ 111 Rigas St., Salaspils LV-2169, Latvia. ORCID: https://orcid.org/0000-0002-1700-4433
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Mariusz Ciesielski, Ph.D.
Ph.D., Dept. of Geomatics, Forest Research Institute, Sękocin Stary, ul. Braci Leśnej 3, Raszyn 05-090, Poland.
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Antti Leinonen, Ph.D. https://orcid.org/0000-0002-7074-9020
Specialist, Finnish Forest Centre, Kauppakatu 25a, Kajaani FI-87100, Finland. ORCID: https://orcid.org/0000-0002-7074-9020
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Anneli M. Ågren, Ph.D. https://orcid.org/0000-0002-6758-3971
Associate Professor, Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå 90183, Sweden. ORCID: https://orcid.org/0000-0002-6758-3971
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