Environmental damage has been of much concern, particularly coastal areas and the oceans given climate change and drastic effects of pollution and extreme climate events. Our present day analytical capabilities along with the advancements in information acquisition techniques such as remote sensing can be utilized for the management and study of coral reef ecosystems. In this paper, we present Reef-insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef community mapping. Our framework compares different clustering methods to evaluate them for reef community mapping using remote sensing data. We evaluate four major clustering approaches such as k- means, hierarchical clustering, Gaussian mixture model, and density-based clustering based on qualitative and visual assessment. We utilise remote sensing data featuring Heron reef island region in the Great Barrier Reef of Australia. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters that are found in reefs when compared to other studies. Our results indicate that Reef-insight can generate detailed reef community maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. We release our framework as open source software to enable its extension to different parts of the world
翻译:由于气候变化以及污染和极端气候事件的剧烈影响,环境损害一直引起人们的极大关注,特别是沿海地区和海洋,特别是气候变化以及污染和极端气候事件的剧烈影响,环境损害一直引起人们的极大关注。我们目前的分析能力以及遥感等信息获取技术的进步,可以用来管理和研究珊瑚礁生态系统。我们在本文件中介绍了一个不受监督的机器学习框架,其特点是先进的集群方法和珊瑚礁社区绘图遥感。我们的框架比较了不同的集群方法,以利用遥感数据来评价珊瑚礁社区绘图;我们评估了四种主要集群方法,例如K-手段、等级集群、高山混合模型和基于质量和视觉评估的密度集群。我们利用澳大利亚大堡礁海隆岛区域的遥感数据。我们的结果显示,利用遥感数据进行集群的方法可以很好地貌和地貌群落,而与其他研究相比,珊瑚礁群落群群群是发现的。我们的框架可以产生详细的珊瑚礁社区地图,概述不同的珊瑚礁生境生境,并有可能为珊瑚礁恢复项目提供进一步的洞察力。我们把框架作为开放源软件发布,以便将其推广到世界不同地区。