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Large‐scale characterization of horizontal forest structure from remote sensing optical images

Xin Xu,Martin Brandt,Xiaowei Tong,M. Mugabowindekwe,Yuemin Yue,Sizhuo Li,Qiu-e Xu,Siyu Liu,Florian Reiner,Kelin Wang,Zhengchao Chen,Yongqing Bai,Rasmus Fensholt

Published 2026 in Remote Sensing in Ecology and Conservation

ABSTRACT

Forest structure is an essential variable in forest management and conservation, as it has a direct impact on ecosystem processes and functions. Previous remote sensing studies have primarily focused on the vertical structure of forests, which requires laser point data and may not always be suited to distinguish plantations from old forests. Sub‐meter resolution remote sensing data and tree crown segmentation techniques hold promise in offering detailed information that can support the characterization of forest structure from a horizontal perspective, offering new insights in the tree crown structure at scale. In this study, we generated a dataset with over 5 billion tree crowns and developed a Horizontal Structure Index (HSI) by analyzing spatial relationships among neighboring trees from remote sensing optical images. We first extracted the location and crown size of overstory trees from optical satellite and aerial imagery at sub‐meter resolution. We subsequently calculated the distance between tree crown centers, their angles, the crown size and crown spacing, and linked this information with individual trees. We then used principal component analysis (PCA) to condense the structural information into the HSI and tested it in China, Rwanda and Denmark. Our result showed that the HSI has the potential to distinguish monoculture plantations from other forest types, which provides insights that extend beyond metrics derived from vertical forest structure. The proposed HSI is derived directly from tree‐level attributes and supports a deeper understanding of forest structure from a horizontal perspective, complementing existing remote sensing‐based metrics.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    Remote Sensing in Ecology and Conservation

  • Publication date

    2026-01-16

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.1002/rse2.70058
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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