Classification of tree species as well as standing dead trees using triple wavelength ALS in a temperate forest

Nina Amiri, Peter Krzystek*, Marco Heurich, Andrew Skidmore

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)
    30 Downloads (Pure)

    Abstract

    Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m2. To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8-17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

    Original languageEnglish
    Article number2614
    Pages (from-to)1-19
    Number of pages19
    JournalRemote Sensing
    Volume11
    Issue number22
    DOIs
    Publication statusPublished - 2 Nov 2019

    Bibliographical note

    Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • segmentation
    • forest structure analysis
    • dead wood
    • ALS point cloud
    • feature importance analysis

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