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Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

M. Aubry-Kientz,A. Laybros,Ben. G. Weinstein,James G. C. Ball,Toby D. Jackson,D. Coomes,Grégoire Laurent Vincent

Published 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ABSTRACT

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/JSTARS.2021.3069159
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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