Abstract
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
Abstract (translated)
环境破坏一直非常关注,特别是考虑到气候变化和污染和极端气候事件的强烈影响,因此珊瑚礁生态系统的管理和研究受到了重视。本文介绍了Reef- insight,一个 unsupervised 机器学习框架,它采用了先进的聚类方法和遥感数据来进行珊瑚礁社区 mapping。我们的框架比较了不同的聚类方法,利用遥感数据进行评估,以评估用于珊瑚礁社区 mapping 的遥感数据。我们评估了四种主要聚类方法,如 k 均值、层次聚类、高斯混合模型和密度聚类,基于定性和视觉评估。我们利用澳大利亚大堡礁的赫隆岛遥感数据进行了研究。我们的结果显示,使用遥感数据进行聚类方法可以很好地识别珊瑚礁中的海洋生物和岩石地形簇,与其他研究相比,这一点非常重要。我们的结果显示,Reef- insight 可以生成详细的珊瑚礁社区地图,列出独特的珊瑚礁栖息地,并可能为珊瑚礁恢复项目提供更深入的见解。我们将我们的框架发布为开源软件,以便将其扩展到世界各地的不同部分。
URL
https://arxiv.org/abs/2301.10876
