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Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Modzelewska, Aneta; Fassnacht, Fabian Ewald ORCID iD icon 1; Stereńczak, Krzysztof
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.

Verlagsausgabe §
DOI: 10.5445/IR/1000118378
Veröffentlicht am 25.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 02.2020
Sprache Englisch
Identifikator ISSN: 0303-2434
KITopen-ID: 1000118378
Erschienen in International journal of applied earth observation and geoinformation
Verlag Elsevier
Band 84
Seiten Art.-Nr. 101960
Vorab online veröffentlicht am 27.09.2019
Externe Relationen Abstract/Volltext
Nachgewiesen in Dimensions
Web of Science
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