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.

中文翻译：

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多传感器数据融合用于改善密集热带森林中单个树冠的分割

在茂密，多样和多层的热带雨林冠层中，从遥感数据中自动进行树冠分割尤其具有挑战性，而采用这种方法来追踪死亡率则更加困难。在这里，我们研究了法属圭亚那一个研究地点将机载激光扫描（ALS）与多光谱和高光谱数据相结合以提高树冠分割精度的潜力。我们将ALS点云聚类方法与光谱深度学习模型相结合，可在识别手动分割的参考冠（全等> 0.5）时达到83％的准确性。该方法的性能优于两步过程，该过程涉及对ALS点云进行聚类，然后使用高光谱距离的逻辑回归来校正过度分割。我们使用这种方法来绘制重复调查中的树木死亡率图，并表明在第一个与高度损失簇相交的树中确定的树冠数量可以很好地估计这些区域中死树的数量。我们的结果表明，多传感器数据融合改善了单个树冠的自动分割，并为通过重复遥感采集研究森林人口统计学提供了有希望的途径。