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

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.
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Contributor : Yannick Brohard Connect in order to contact the contributor
Submitted on : Wednesday, June 9, 2021 - 4:21:50 PM
Last modification on : Friday, August 5, 2022 - 2:38:11 PM


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Mélaine Aubry-Kientz, Anthony Laybros, Ben Weinstein, James G. C. Ball, Toby Jackson, et al.. Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2021, 14, pp.3927-3936. ⟨10.1109/jstars.2021.3069159⟩. ⟨hal-03255607⟩



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