Mangrove forests are vulnerable ecosystems that require broad-scale monitoring. Various solutions based on satellite imagery have emerged for this purpose but still suffer from the lack of methods to accurately delineate individual tree crowns (ITCs). Within-stand variability in crown size and shape, crown clumping and fragmentation, and understory vegetation hamper the delineation in these ecosystems. To cope with these factors, the proposed method combines a deep learning-based enhancement of ITCs with a marker-controlled watershed segmentation algorithm. The MT-EDv3 neural network is employed to compute the normalized Euclidean distance of crown pixels to treetops and a Laplacian of Gaussian filter is applied to the resulting image to enhance crown borders before segmentation. The method was applied to WorldView imagery over four mangrove sites worldwide and compared to previously published methods using standardized metrics. Accurate detection (Overall Accuracy ≥ 0.93 and Kappa ≥ 0.87) and area estimation (R² ≥ 0.66, NRMSE ≤ 12%) of crowns was achieved for all sites using either the panchromatic band or a combination of the pan-sharpened visible-near-infrared bands. Based on Precision, Recall, and F1-score, the proposed method outperformed previous watershed segmentation and software-based algorithms of crown delineation, as well as the Mask R-CNN segmentation framework. The viewing geometry of images and the forest heterogeneity were identified as important contributors to the delineation accuracy. This study is the first to achieve accurate delineation of ITCs in mangrove forests across sites, opening perspectives of applications to satellite-based monitoring. The method shows promising transferability to other very-high-resolution satellite sensors as well as to aerial and unmanned aerial vehicle imagery and could be improved by including more spectral information and LiDAR-derived canopy height models.

红树林是需要大范围监测的脆弱生态系统。为此目的出现了各种基于卫星图像的解决方案，但仍然缺乏准确描绘单个树冠 (ITC) 的方法。树冠大小和形状的林分内变异、树冠结块和破碎以及林下植被阻碍了这些生态系统的划分。为了应对这些因素，所提出的方法将基于深度学习的 ITC 增强与标记控制的分水岭分割算法相结合。MT-EDv3 神经网络用于计算树冠像素到树梢的归一化欧几里得距离，并将高斯拉普拉斯滤波器应用于生成的图像，以在分割前增强树冠边界。该方法应用于全球四个红树林站点的 WorldView 图像，并与使用标准化指标的先前发布的方法进行比较。准确检测（Overall Accuracy ≥ 0.93 and Kappa ≥ 0.87）和面积估计（² _ ≥ 0.66, NRMSE ≤ 12%) 使用全色波段或全色锐化可见-近红外波段的组合在所有部位实现了牙冠。基于 Precision、Recall 和 F1-score，所提出的方法优于以前的分水岭分割和基于软件的冠状勾画算法，以及 Mask R-CNN 分割框架。图像的观察几何形状和森林异质性被确定为描绘准确性的重要因素。这项研究首次实现了跨地点准确描绘红树林中的 ITC，为基于卫星的监测应用开辟了前景。