Estimating fine-scale visibility in a temperate forest landscape using airborne laser scanning

Xin Zong*, Tiejun Wang, Andrew K. Skidmore, Marco Heurich

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)
    41 Downloads (Pure)

    Abstract

    Visibility is a key factor influencing animal behavior in forest ecosystems. Fine-scale visibility in forested areas has been measured by ground-based approaches at the plot level, using site-specific methods that have limited spatial coverage. Here we examine airborne laser scanning (ALS) as a novel tool to quantify fine-scale visibility in the temperate forests of Germany at a landscape scale. We validate the (vertically-derived) ALS-derived visibility measures using proven (horizontally-derived) terrestrial laser scanning (TLS) estimates of true visibility. Our results indicate that there is a good agreement between the visibility resulting from ALS and TLS with an R2 ranging from 0.53 to 0.84 and a normalized RMSE varying from 15.92% to 11.81% at various plot sizes, with the highest accuracy achieved using a plot size of 35 × 35 m. Our study demonstrates for the first time that ALS can be successfully applied to quantify fine-scale visibility in temperate forests at a landscape level. This approach holds potential for studying the spatial behavior of animals (e.g., habitat selection and predator–prey relationships) in forest ecosystems.

    Original languageEnglish
    Article number102478
    Pages (from-to)1-11
    Number of pages11
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Volume103
    DOIs
    Publication statusPublished - 1 Dec 2021

    Bibliographical note

    Copyright the Author(s) 2021. 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

    • Viewshed
    • Airborne LiDAR
    • Terrestrial LiDAR
    • Understory
    • Random forests

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