Please use this identifier to cite or link to this item: https://doi.org/10.48548/pubdata-1429
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FieldValue
Resource typeJournal Article
Title(s)First automatic size measurements for the separation of dwarf birch and tree birch pollen in MIS 6 to MIS 1 records from Northern Germany
DOI10.48548/pubdata-1429
Handle20.500.14123/1498
CreatorTheuerkauf, Martin  0000-0002-4033-3040
Nehring, Elias
Gillert, Alexander  1331357764
Bodien, Philipp Morten
Hein, Michael  0000-0002-5500-4020
Urban, Brigitte  0000-0003-0071-3388
AbstractDuring past glacial periods, the land cover of Northern Eurasia and North America repeatedly shifted between open steppe tundra and boreal/temperate forest. Tracking these changes and estimating the coverage of open versus forested vegetation in past glacial and interglacial landscapes is notoriously difficult because the characteristic dwarf birches of the tundra and the tree birches of the boreal and temperate forests produce similar pollen grains that are difficult to distinguish in the pollen record. One objective approach to separating dwarf birch pollen from tree birch pollen is to use grain size statistics. However, the required grain size measurements are time-consuming and, therefore, rarely produced. Here, we present an approach to automatic size measurement based on image recognition with convolutional neural networks and machine learning. It includes three main steps. First, the TOFSI algorithm is applied to detect and classify pollen, including birch pollen, in lake sediment samples. Second, a Resnet-18 neural network is applied to select the birch pollen suitable for measurement. Third, semantic segmentation is applied to detect the outline and the area and mean width of each detected birch pollen grain. Test applications with two pollen records from Northern Germany, one covering the Lateglacial-Early Holocene transition and the other covering the Mid to Late Pleistocene transition, show that the new technical approach is well suited to measure the area and mean width of birch pollen rapidly (>1000 per hour) and with high accuracy. Our new network-based tool facilitates more regular size measurements of birch pollen. Expanded analysis of modern birch pollen will help to better understand size variations in birch pollen between birch species and in response to environmental factors as well as differential sample preparation. Analysis of fossil samples will allow better quantification of dwarf birch versus tree birch in past environments.
LanguageEnglish
KeywordsPollen; Automatic Recognition; Convolutional Neural Networks; Dwarf Birch; Holocene; Machine Learning; Tree Birch
Year of publication in PubData2024
Publishing typeParallel publication
Publication versionPublished version
Date issued2024-06-14
Creation contextResearch
Faculty / departmentFakultät Nachhaltigkeit
NotesThis publication was funded by the German Research Foundation (DFG).
Date of Availability2024-11-08T07:16:53Z
Archiving Facility Medien- und Informationszentrum (Leuphana Universität Lüneburg  02w2y2t16)
Published byMedien- und Informationszentrum, Leuphana Universität Lüneburg
  Information regarding first publication
FieldValue
Resource typeJournal
Title of the resource typeEcology and Evolution
IdentifierDOI: 10.1002/ece3.11510
Publication year2024
Volume14
Issue6
Numbere11510
Number typeArticle
Place of publicationJohn Wiley & Sons
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