Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Xi Zhu*, Andrew K. Skidmore, Roshanak Darvishzadeh, K. Olaf Niemann, Jing Liu, Yifang Shi, Tiejun Wang

*Corresponding author for this work

    Research output: Contribution to journalArticle

    28 Citations (Scopus)

    Abstract

    Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest.

    Original languageEnglish
    Pages (from-to)43-50
    Number of pages8
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Volume64
    DOIs
    Publication statusPublished - Feb 2018

    Keywords

    • Classification
    • Terrestrial laser scanning
    • Radius search
    • Radiometric feature
    • Geometric feature

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