Global warming and sea-level rise (SLR) induced by rising atmospheric CO₂ concentrations can cause coral bleaching, death, and submergence of the world’s coral reefs. Adopting the GIS and RS methods, we modeled how these two stressors combine to influence the future growth of the atolls and table reefs of three archipelagoes in the South China Sea (SCS), based on geomorphic and ecological zones. A large-scale survey of the coral communities in Xisha Islands in 2014, Dongsha Islands in 2014–2016 and Nansha Islands in 2007 provided zone-specific process datasets on the range of reef accretion rates. Sea surface temperature and extreme (minimum and maximum) SLR data above 1985–2005 levels by 2100 in the SCS were derived from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) models forced with the Representative Concentration Pathways (RCPs). Our model projected that: (1) the Xisha Islands and Dongsha Islands may have a better growth status, because the reef flat biotic sparse zone may be recolonized with hard coral and become a biotic dense zone; (2) the southern Nansha Islands reefs have a risk of stopping growing due to their earlier annual bleaching years. The increasing of water depths of these reefs is stronger in the RCP with more emissions. Our approach offers insights into the best-case and worst-case impacts of two global environmental pressures on potential future reef growth under a changing climate.

Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.

The amount of live and dead coral is related to recruitment, but differentiating them by remote sensing techniques is difficult. We measured change in the amount of live, bleached, and recently (<6 months) dead coral cover (CCA6) for an island archipelago in the South China Sea. Six Landsat 4/7 ETM/8 OLI images from 1989–1990, 2005, and 2014 were analyzed to assess changes in CCA6 at 14 coral reefs in the western Xisha Islands. Satellite images were georectified and calibrated to remote sensing reflectance. Models for three shallow water (<6 m) geomorphic zones throughout the western Xisha Islands are proposed based on ground-truthed data collected in 2014 and satellite-determined spectral values for 2014 images. Nonlinear models based on Landsat image blue spectral bands for reef slope and lagoon habitats, and green bands for reef flat habitats, are determined as optimal models. Significant changes in CCA6 from 2005 to 2014, and changes in coral reefs and geomorphic zones, correlate with increased numbers of both crown-of-thorns starfish, and sea surface temperature in 2007. Detecting change in CCA6 by remote sensing can provide large-scale information of value for coral reef management, restoration, and protection.

精细波浪数据是研究岛礁珊瑚分布和地形地貌结构的基础。目前，珊瑚礁体内部及更小尺度上开展的长时间序列精细化波浪模拟研究还很缺乏，不足以获取岛礁区域精细化的波浪特征。论文以中国南海西沙群岛的永乐环礁为研究区，基于SWAN模型自嵌套波浪模拟和16 m分辨率地形数据，开展2017—2021年永乐环礁高分辨率精细化岛礁波浪模拟，并基于地貌、方位和岛礁分析了研究区有效波高、浪向、波浪能流密度等的精细空间分布特征。研究结果表明：模型模拟波浪与实测波浪Pearson相关系数为0.95，显著性水平P<0.01；永乐环礁5年平均有效波高和能流密度随月份呈现“U”型变化趋势，NE向的平均浪向频率为20%；深潟湖和礁坡的5年平均以及最大有效波高和能流密度最高；平均能流密度呈现南部暗礁最高、西南部次之、北部和西部最低的规律；自外海传播进潟湖的波浪，各方位上礁坡到礁坪的能流密度衰减高于30%，岛礁南部衰减最多达72.7%。研究可为珊瑚分布规律探讨、珊瑚礁保护与恢复和海岸工程建设等提供方法和理论支撑。

Most studies of marine heatwaves (MHWs) in coral reefs rely on coarse-resolution satellite sea surface temperature (SST) data, limiting the capture of fine-scale thermal variability critical to...