Quantifying understory vegetation density using multi-temporal Sentinel-2 and GEDI LiDAR data

Yanbiao Xi, Qingjiu Tian, Wenmin Zhang*, Zhichao Zhang, Xiaoye Tong, Martin Brandt, Rasmus Fensholt

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

    11 Citations (Scopus)
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    Abstract

    Understory vegetation contributes considerably to biodiversity and total aboveground biomass of forest ecosystems. Whereas field inventories and LiDAR data are generally used to estimate understory vegetation density, methods for large-scale and spatially continuous estimation of understory vegetation density are still lacking. For an evergreen coniferous forest area in southern China, we developed and tested an effective and practical remote sensing-driven approach for mapping understory vegetation, based on phenological differences between over and understory vegetation. Specifically, we used plant area volume density (PAVD) calculations based on GEDI data to train a support vector regression model and subsequently estimated the understory vegetation density from Sentinel-2 derived metrics. We produced maps of PAVD for the growing and non-growing season respectively, both performing well compared against independent GEDI samples (R2 = 0.89 and 0.93, p < 0.01). Understory vegetation density was derived from the differences in PAVD between the growing and non-growing season. The understory vegetation density map was validated against field samples from 86 plots showing an overall R2 of 0.52 (p < 0.01), rRMSE = 21%. Our study developed a tangible approach to map spatially continuous understory vegetation density with the combination of GEDI LiDAR data and Sentinel-2 imagery, showing the potential to improve the estimation of terrestrial carbon storage and better understand forest ecosystem processes across larger areas.

    Original languageEnglish
    JournalGIScience and Remote Sensing
    Volume59
    Issue number1
    Pages (from-to)2068-2083
    Number of pages16
    ISSN1548-1603
    DOIs
    Publication statusPublished - 2022

    Bibliographical note

    Publisher Copyright:
    © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

    Keywords

    • GEDI LiDAR data
    • plant area volume density
    • support vector regression
    • Understory vegetation

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