First automatic size measurements for the separation of dwarf birch and tree birch pollen in MIS 6 to MIS 1 records from Northern Germany ...

• Main Authors: Theuerkauf, Martin, Nehring, Elias, Gillert, Alexander, Bodien, Philipp Morten, Hein, Michael, Urban, Brigitte
• Format: Article in Journal/Newspaper
• Language: English
• Published: Medien- und Informationszentrum, Leuphana Universität Lüneburg 2024
• Subjects: Pollen; Automatic Recognition; Convolutional Neural Networks; Dwarf Birch; Holocene; Machine Learning; Tree Birch; Dwarf birch; Tundra
• Online Access: https://dx.doi.org/10.48548/pubdata-1429; https://pubdata.leuphana.de/handle/20.500.14123/1498

During 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 ...