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Automatic Mapping of Standing Dead Trees after an Insect Outbreak Using the Window Independent Context Segmentation Method
Stockholms universitet. (Kulturgeografiska institutionen)
Bavarian Forest National Park.
Stockholms universitet. (Kulturgeografiska institutionen)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0002-4405-6888
2014 (English)In: Journal of forestry, ISSN 0022-1201, E-ISSN 1938-3746, Vol. 112, no 6, 564-571 p.Article in journal (Refereed) Published
Abstract [en]

Since the 1980s, there has been an increase in the spruce bark beetle population in the Bavarian Forest National Park in southeastern Germany. There is a need for accurate and time-effective methods for monitoring the outbreak, because manual interpretation of image data is time-consuming and expensive. In this article, the window independent context segmentation method is used to map deadwood areas. The aim is to evaluate the method's ability to monitor deadwood areas on a yearly basis. Two-color infrared scenes with a spatial resolution of 40 × 40 cm from 2001 and 2008 were used for the study. The method was found to be effective with an overall accuracy of 88% for the 2001 scene and 90% for the 2008 scene.

Place, publisher, year, edition, pages
2014. Vol. 112, no 6, 564-571 p.
Keyword [en]
forest health; image analysis; insects and disease; remote sensing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-238724DOI: 10.5849/jof.13-050ISI: 000344981800003OAI: oai:DiVA.org:uu-238724DiVA: diva2:772029
Available from: 2014-12-15 Created: 2014-12-15 Last updated: 2017-12-05

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Brun, Anders

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