Department of Geography, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok 16424, Indonesia.
Sensors (Basel). 2024 Jan 12;24(2):466. doi: 10.3390/s24020466.
This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel-2, and 78.28% with Multispectral Aerial Photos. Our findings indicate that the classification accuracy is significantly influenced by the geographic resolution and the quality of the field and satellite/aerial image data. Over the two decades, there was a notable decrease in the coral reef area from 2003 to 2011, with a reduction to 16 hectares, followed by a slight increase in area but with more heterogeneous densities between 2011 and 2021. The study underscores the dynamic nature of coral reef habitats and the efficacy of machine learning in environmental monitoring. The insights gained highlight the importance of advanced analytical methods in guiding conservation efforts and understanding ecological changes over time.
本研究旨在利用先进的机器学习分类技术，了解达拉湾岛二十年来（2003 年、2011 年和 2021 年）珊瑚礁生境的动态变化。研究的动机源于对准确、详细的环境监测的迫切需求，以便为保护策略提供信息，特别是在珊瑚礁等生态敏感地区。我们采用了非参数机器学习算法，包括随机森林（RF）、支持向量机（SVM）和分类回归树（CART），以评估珊瑚生境的时空变化。我们的分析利用了来自 Landsat 9、Landsat 7、Sentinel-2 和多光谱航空照片的高分辨率数据。RF 算法被证明是最准确的，使用 Landsat 9 的准确率为 71.43%，使用 Sentinel-2 的准确率为 73.68%，使用多光谱航空照片的准确率为 78.28%。我们的研究结果表明，分类精度受到地理分辨率以及现场和卫星/航空图像数据质量的显著影响。在过去的二十年中，珊瑚礁面积从 2003 年到 2011 年显著减少，减少到 16 公顷，随后在 2011 年至 2021 年期间面积略有增加，但密度更加不均匀。该研究强调了珊瑚礁生境的动态性质和机器学习在环境监测中的功效。所获得的见解突出了先进分析方法在指导保护工作和随着时间的推移了解生态变化方面的重要性。
