Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests
نویسندگان
چکیده
Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, tracking mortality by this approach even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral hyperspectral to improve accuracy of at a study site French Guiana. We combined an ALS point cloud clustering method spectral deep learning model achieve 83% recognizing manually segmented reference crowns (with congruence >0.5). This outperformed two-step process that involved then using logistic regression distances correct oversegmentation. used map repeat surveys show number identified first intersected height loss clusters was good estimator dead trees these areas. Our results demonstrate multisensor fusion improves automatic individual presents promising avenue demography repeated acquisitions.
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2021
ISSN: ['2151-1535', '1939-1404']
DOI: https://doi.org/10.1109/jstars.2021.3069159