Al-Huqail Asma A, Islam Zubairul, Al-Harbi Hanan F, Khan Faheema
Chair of Climate Change, Environmental Development and Vegetation Cover, Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia.
Department of Geography and Environmental Management, University of Abuja, Abuja, Nigeria.
Sci Rep. 2025 Jun 2;15(1):19285. doi: 10.1038/s41598-025-03280-5.
Mangroves provide essential ecological benefits, and accurate classification is vital for their protection. This study used 2023 Landsat 8 SR data within the Google Earth Engine (GEE) platform to classify mangrove and non-mangrove areas in the Farasan Islands Protected Area in Saudi Arabia. Machine learning models, Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boost (GB), and an ensemble approach were employed using spectral indices such as NDVI, MNDWI, SR, GCVI, and LST. The ensemble model achieved an overall accuracy (OA) of 92.2% and a kappa coefficient (KC) of 0.84. The models, RF had an OA of 91.4% and KC of 0.82, SVM had 88.3% OA and 0.76 KC, and GB recorded 86.7% OA and 0.73 KC. Ground truth cross-validation was conducted using high-resolution satellite imagery from Google Earth, combined with an NDVI overlay derived from Landsat 8 data. This approach confirmed the accuracy of the models in detecting dispersed mangrove patches, which are often missed in global datasets. This workflow can enhance conservation efforts and support sustainable mangrove management.
红树林提供了重要的生态效益，准确分类对其保护至关重要。本研究利用谷歌地球引擎（GEE）平台内的2023年陆地卫星8号地表反射率（SR）数据，对沙特阿拉伯法拉桑群岛保护区内的红树林和非红树林区域进行分类。使用了机器学习模型，随机森林（RF）、支持向量机（SVM）、极端梯度提升（GB）以及一种集成方法，并利用归一化植被指数（NDVI）、改进型归一化差异水指数（MNDWI）、地表反射率（SR）、绿度植被指数（GCVI）和陆地表面温度（LST）等光谱指数。集成模型的总体准确率（OA）达到92.2%，kappa系数（KC）为0.84。各模型中，随机森林的总体准确率为91.4%，kappa系数为0.82；支持向量机的总体准确率为88.3%，kappa系数为0.76；极端梯度提升的总体准确率为86.7%，kappa系数为0.73。利用谷歌地球的高分辨率卫星图像并结合从陆地卫星8号数据得出的归一化植被指数叠加图进行地面真值交叉验证。这种方法证实了模型在检测分散的红树林斑块方面的准确性，而这些斑块在全球数据集中常常被遗漏。此工作流程可加强保护工作并支持可持续的红树林管理。
