发表机构
University of French Polynesia（法属波利尼西亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合Pléiades影像与XGBoost分类器的自动化海岸线监测方法，可高精度监测珊瑚岛礁动态，揭示传统指标未检测到的岛屿变化，助力太平洋环礁的复原力研究与适应框架构建。
AI 中文摘要
全球尺度研究常将太平洋环礁小岛描述为稳定状态，这类结论通常基于植被线等长期海岸线替代指标或平面表面积等形态测量数据。尽管这些信息具有一定参考价值，但会掩盖短期、局部的海岸动态，包括对生态系统功能、文化实践及沿海基础设施复原力至关重要的岛屿形状与位置变化。本研究提出一种可迁移的自动化海岸线监测方法，采用极高分辨率的Pléiades影像与XGBoost分类器，该方法整合光谱指数与纹理特征，用于划定出露陆地的外部边界，涵盖植被区、海滩、人造表面及海滩岩。此海岸线定义支持对珊瑚岛礁动态进行精细、空间明确的监测，即便在形态复杂的环境中也适用。该模型在法属波利尼西亚的多个环礁（泰蒂亚罗阿环礁、提克豪环礁、豪环礁及普卡普卡环礁）上完成开发与测试，取得高精度表现：平均交并比约为0.99，平均绝对位置误差约为1.28米，且在训练站点与预留站点均展现出优异性能，验证了其空间可迁移性。提取的海岸线揭示了岛屿范围、构型及空间位置的细微但显著变化，而这些变化是传统海岸线替代指标与表面度量无法检测到的。该方法通过实现高精度、可扩展的海岸线监测，为理解环礁变化过程提供了更细致的视角，助力太平洋地区摆脱被动流失的叙事，转向复原力与适应框架，并提供适配低海拔岛屿实际情况的空间工具。
英文摘要
Pacific atoll islets are often described as stable in global-scale studies, typically based on long-term shoreline proxies such as vegetation line or morphometrics like planform surface area. While informative, these approaches can obscure short-term, localized coastal dynamics -including changes in island shape and position -that are critical for ecosystem function, cultural practices, and coastal infrastructure resilience. This study presents a transferable, automated approach to shoreline monitoring using very high-resolution Pl{é}iades imagery and a XGBoost classifier. The method integrates spectral indices and textural features to delineate the outer limit of emerged land, including vegetated areas, beaches, man-made surfaces, and beach rock. This shoreline definition supports finescale, spatially explicit monitoring of reef island dynamics, even in morphologically complex environments. Developed and tested on multiple atolls in French Polynesia (Tetiaroa, Tikehau, Hao, and Puka Puka), the model achieves high accuracy (mean Intersection over Union $\approx$ 0.99; Mean Absolute Positional Error $\approx$ 1.28 m) and demonstrates strong performance on both training and held-out sites, validating its spatial transferability. The extracted shorelines reveal subtle but significant island-scale changes in extent, configuration, and spatial position that remain undetected by conventional shoreline proxies and surface metrics. By enabling highprecision, scalable shoreline monitoring, this method provides a more nuanced understanding of atoll change processes. It supports Pacific efforts to move beyond narratives of passive loss toward frameworks of resilience and adaptation, while providing spatial tools tailored to low-lying island realities.
Journal ref6th PIURN Conference 2025, PNG University of Technology, Lae, Papouasie Nouvelle-Guin{é}e, Jul 2025, Lae, Papouasie Nouvelle-Guin{é}e, Papua New Guinea. pp.249-254
