Tree species classification using plant functional traits from LiDAR and hyperspectral data

Yifang Shi*, Andrew K. Skidmore, Tiejun Wang, Stefanie Holzwarth, Uta Heiden, Nicole Pinnel, Xi Zhu, Marco Heurich

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    81 Citations (Scopus)


    Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.

    Original languageEnglish
    Pages (from-to)207-219
    Number of pages13
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Publication statusPublished - Dec 2018


    • Tree species classification
    • Plant functional traits
    • Airborne LiDAR
    • Airborne hyperspectral
    • Natural forest


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