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Sökning: onr:"swepub:oai:DiVA.org:umu-201888" > Mapping drainage di...

Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning

Lidberg, William (författare)
Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden
Paul, Siddhartho Shekhar (författare)
Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden
Westphal, Florian (författare)
Dept. of Computing, School of Engineering, Jönköping Univ., Jönköping, Sweden
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Richter, Kai-Florian (författare)
Umeå universitet,Institutionen för datavetenskap
Lavesson, Niklas (författare)
Dept. of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
Melniks, Raitis (författare)
Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ Salaspils, Latvia
Ivanovs, Janis (författare)
Dept. of Forest Operations and Energy, Latvian State Forest Research Institute ‘Silava,’ Salaspils, Latvia
Ciesielski, Mariusz (författare)
Dept. of Geomatics, Forest Research Institute, Sękocin Stary, Raszyn, Poland
Leinonen, Antti (författare)
Finnish Forest Centre, Kajaani, Finland
Ågren, Anneli M. (författare)
Dept. of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden
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Dept of Forest Ecology and Management, Swedish Univ. of Agricultural Sciences, Umeå, Sweden Dept. of Computing, School of Engineering, Jönköping Univ., Jönköping, Sweden (creator_code:org_t)
American Society of Civil Engineers (ASCE), 2023
2023
Engelska.
Ingår i: Journal of irrigation and drainage engineering. - : American Society of Civil Engineers (ASCE). - 0733-9437 .- 1943-4774. ; 149:3
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

LANTBRUKSVETENSKAPER  -- Lantbruksvetenskap, skogsbruk och fiske -- Skogsvetenskap (hsv//swe)
AGRICULTURAL SCIENCES  -- Agriculture, Forestry and Fisheries -- Forest Science (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Ditches
Channel
airborne laser scanning
Deep learning
Semantic segmentation
Computer Science
datalogi
Earth Sciences with Specialization Environmental Analysis
geovetenskap med inriktning mot miljöanalys

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