PDF(2043 KB)
PDF(2043 KB)
PDF(2043 KB)
浅海珊瑚礁遥感监测研究进展
Advances in remote sensing monitoring of coral reef in optically shallow waters
珊瑚礁生态系统具有重要的生态和社会经济价值,然而受到全球气候变化和人类活动影响,其退化态势持续加剧。光学遥感凭借大范围、高时效和低成本等优势,已成为珊瑚礁生态系统监测的重要技术手段。本文从地形(水深)、底质、水质(叶绿素a质量浓度、水体透明度)和珊瑚礁生态状况四个方面综述浅海珊瑚礁遥感监测的研究进展,梳理其理论基础、模型演进与应用进展,厘清其从物理机理创新到“机理+数据”双驱动的发展脉络与方法谱系。在此基础上,进一步总结当前研究在多参数协同反演、跨区域泛化能力和长期动态监测等方面的关键挑战,展望物理机理约束、人工智能融合和多源观测协同等重点发展方向,为未来珊瑚礁智能化遥感监测与生态保护等研究提供参考。
Coral reef ecosystems possess significant ecological and socioeconomic value; however, their risk of degradation has continued to intensify under the combined pressures of global climate change and anthropogenic disturbances. Owing to its advantages of extensive spatial coverage, high temporal efficiency, and relatively low cost, optical remote sensing has become an important technological approach for monitoring coral reef ecosystems. Focusing on key indicators including shallow-water depth, benthic substrates, water environmental conditions, and ecological status, this review systematically summarizes recent advances in the remote sensing monitoring of coral reef ecosystems in optically shallow waters. Particular emphasis is placed on the underlying theoretical foundations, model evolution, and application progress, thereby clarifying the developmental trajectory and methodological framework from innovations in physical mechanisms to the dual-driven paradigm integrating physical principles and data-driven approaches. Building on this review, we further summarize the key challenges currently faced in multi-parameter synergistic retrieval, cross-regional generalization, and long-term dynamic monitoring, and discuss future directions involving physical mechanism constraints, artificial intelligence integration, and multi-source observational synergy. This review provides a reference for the future development of intelligent remote sensing monitoring and ecological conservation of coral reef ecosystems.
珊瑚礁 / 光学浅海 / 水深 / 底质 / 水质参数 / 生态状况 / 遥感监测
coral reef / optically shallow waters / bathymetry / benthic substrates / water environmental conditions / ecological status / remote sensing monitoring
| [1] |
吕意华, 娄全胜, 吴鹏, 等. 珊瑚礁监测技术发展现状及原位在线监测建设[J]. 海洋技术学报, 2020, 39(1): 84-90.
|
| [2] |
|
| [3] |
|
| [4] |
We examined the development of coral reef science and the policies, institutions, and governance frameworks for management of coral reefs in China in order to highlight the wicked problem of preserving reefs while simultaneously promoting human development and nation building. China and other sovereign states in the region are experiencing unprecedented economic expansion, rapid population growth, mass migration, widespread coastal development, and loss of habitat. We analyzed a large, fragmented literature on the condition of coral reefs in China and the disputed territories of the South China Sea. We found that coral abundance has declined by at least 80% over the past 30 years on coastal fringing reefs along the Chinese mainland and adjoining Hainan Island. On offshore atolls and archipelagos claimed by 6 countries in the South China Sea, coral cover has declined from an average of >60% to around 20% within the past 10-15 years. Climate change has affected these reefs far less than coastal development, pollution, overfishing, and destructive fishing practices. Ironically, these widespread declines in the condition of reefs are unfolding as China's research and reef-management capacity are rapidly expanding. Before the loss of corals becomes irreversible, governance of China's coastal reefs could be improved by increasing public awareness of declining ecosystem services, by providing financial support for training of reef scientists and managers, by improving monitoring of coral reef dynamics and condition to better inform policy development, and by enforcing existing regulations that could protect coral reefs. In the South China Sea, changes in policy and legal frameworks, refinement of governance structures, and cooperation among neighboring countries are urgently needed to develop cooperative management of contested offshore reefs.© 2012 Society for Conservation Biology.
|
| [5] |
余克服. 南海珊瑚礁及其对全新世环境变化的记录与响应[J]. 中国科学:地球科学, 2012, 42(8): 1160-1172.
|
| [6] |
|
| [7] |
|
| [8] |
84% of the world’s coral reefs impacted in the most intense global coral bleaching event ever[EB/OL]. (2025-04-23)[2026-03-07]. https://icriforum.org/4gbe-2025/.
|
| [9] |
World’s fourth mass coral bleaching event likely ended in 2025[EB/OL]. (2026-06-02)[2026-06-07]. https://www.nesdis.noaa.gov/news/worlds-fourth-mass-coral-bleaching-event-likely-ended-2025.
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue.
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
Idris, Fakhrurrozi, Adiwijaya C. Assessment of coral reef health conditions in Juru Seberang Village, Tanjung Pandan District, Belitung Regency-Bangka-Belitung Province[J]. IOP Conference Series: Earth and Environmental Science, 2021, 744: 012030.
Belitung has potential coral reefs as many as 244 species and 89 species of reef fish. However, the current condition of coral reefs in Belitung is under high pressure, caused by illegal tin mining activities. For this reason, need studies related to their impact on the health of marine life, especially on coral reefs so that the policy can be reviewed, to see the important role of coral reefs for other marine biotas. The aim of this study is focused on knowing the condition of substrate coverage, diversity, and biomass of target fish as well as assessing the health of coral reefs at the observation site. Data were collected with SCUBA diving equipment at 6 stations, at depth of 5–1 m. Retrieval of basic substrate data is done by the Underwater Photo Transect method and for reef fish using belt transect. The results showed substrate coverage was dominated by hard corals, dead corals, sand, and dead corals with algae. Fleshy seaweed cover ranges from 0.07–4.77%, coral fragment cover ranges from 0–2.6%, target fish biomass value obtained varies between 0.0006–0.0299 kg/ha, and coral reef health index at each observation station ranges from 3 to 6.
|
| [20] |
张飞飞, 任广波, 胡亚斌, 等. 融合地理空间认知的珊瑚礁地貌单元高分遥感分类方法[J]. 海洋技术学报, 2023, 42(1): 1-15.
|
| [21] |
熊媛, 黄荣永, 余克服. 基于多时相多光谱遥感影像的珊瑚礁面积估算方法研究——以西沙群岛羚羊礁为例[J]. 海洋学报, 2022, 44(8): 151-168.
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
Accurate shallow-water depth information for island areas is crucial for maritime safety, resource exploration, ecological conservation, and offshore economic activity. Traditional approaches such as shipborne sounding and airborne bathymetric light detection and ranging (lidar) surveys are expensive, time-consuming, and constrained in politically sensitive regions. Moreover, satellite-altimetry-predicted depths exhibit large errors over shallow waters. In contrast, satellite-derived bathymetry (SDB), estimated from multispectral imagery, provides a rapid, open-source, and cost-effective technique for comprehensively characterizing the bathymetry of a region. Given the scarcity of in situ water depth data for the South China Sea (SCS), a shallow-water depth model, HHU24SWDSCS (Hohai University 2024 Shallow-Water Depth Model of South China Sea), was developed using a linear band model by integrating 1298 Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) tracks with 70 Sentinel-2 multispectral images. The model covers over 120 islands and reefs in the SCS region at a resolution of 10 m. Validation against independent ICESat-2 depth data yielded a root mean square error for the model of 0.53–1.24 m (< 5 % of the maximum depth). Further validation using independent airborne lidar bathymetry data in the Lingyang Reef demonstrated an accuracy of 1.01 m. Comparisons with existing bathymetry models revealed the superior performance of the developed model. While traditional bathymetry models exhibit errors of up to tens of meters or larger over island regions and should therefore be used with caution, HHU24SWDSCS demonstrated good accuracy in shallow waters across the SCS. This model thus provides a reference for mapping shallow-water depth close to islands and provides fundamental support for research in oceanography, geodesy, and other disciplines. The HHU24SWDSCS data are freely available at https://doi.org/10.5281/zenodo.13852568 (Wu et al., 2024a).
|
| [34] |
|
| [35] |
温乐羽, 董志鹏, 刘焱雄, 等. 基于残差补偿的神经网络主被动卫星遥感数据浅海水深反演方法[J]. 海洋科学进展, 2026, 44(3): 654-669.
|
| [36] |
马珍妮, 宋妍, 邹亚荣, 等. 基于深度学习的西沙永乐群岛珊瑚礁遥感信息提取[J]. 应用海洋学学报, 2022, 41(4): 644-654.
|
| [37] |
董娟, 任广波, 胡亚斌, 等. 基于高分辨率遥感的珊瑚礁地貌单元体系构建和分类方法——以8波段Worldview-2影像为例[J]. 热带海洋学报, 2020, 39(4): 116-129.
珊瑚礁生态系统是全球初级生产力最高的生态系统之一, 在维持海洋生物多样性、防浪固滩、资源供给等方面发挥着巨大作用。珊瑚礁遥感地貌分类体系是珊瑚礁保护、管理及可持续发展的必要基础, 但目前还未有结合珊瑚覆盖度进行分类的体系。本文基于WorldView-2高分辨率遥感影像, 以中国南海西沙群岛七连屿北部赵述岛和西沙洲所在礁盘作为研究区, 结合珊瑚覆盖度、区域地貌成分和水动力条件等指标, 建立了既适用于遥感监测又与珊瑚生存状况相关联的珊瑚礁地貌单元分类体系。同时, 利用面向对象的支持向量机(Support Vector Machine, SVM)和随机森林(Random Forest, RF)分类方法进行珊瑚礁地貌单元的信息提取, 并对分类结果进行精度评价。结果表明, SVM和RF两种分类方法均能较好地提取出珊瑚礁地貌单元, 分类精度分别为87.59%和79.81%。针对分类过程中出现的错分、漏分问题, 结合珊瑚礁成因和分布规律对分类结果进行修正, 修正后分类提取的精度达到91.3%, Kappa系数为0.9041, 表明本文构建的珊瑚礁地貌单元分类体系在一定程度上能满足当前珊瑚岛礁信息提取的需要。
Coral reef ecosystem is one of the ecosystems with the highest primary productivity in the world. It plays an important role in maintaining marine biodiversity, preventing waves and consolidating beaches, and it is great resources for mankind. At the same time, the remote sensing geomorphological classification system of coral reefs is the necessary basis for the protection, management and sustainable development of coral reefs. However, there is no corresponding classification system taking coral coverage into account. Based on WorldView-2 high-resolution remote sensing image, we selected the reef of Zhaoshu Dao and Xisha Zhou in the north of Qilianyu Island, Xisha Islands, South China Sea as our study area. In this paper, coral coverage, regional geomorphic composition and hydrodynamic conditions are used to establish a coral reef geomorphic unit classification system suitable for remote sensing monitoring and are related to the survival of coral reefs. The method of object-oriented support vector machine (SVM) and random forest (RF) were used to extract the coral reef geomorphic units, and the accuracy of the extraction results was evaluated. The results showed that both SVM and RF classification methods can extract reef geomorphic units, and the classification accuracy was 87.59% and 79.81%, respectively. Aiming at the problem of misclassification and omission in the process of classification, we modified the classification results according to the causes and distributions of coral reefs. And the accuracy of modified classification was 91.3%, and the kappa coefficient was 0.9041. They showed that the coral reef geomorphic unit classification system constructed in this paper can meet the need of information extraction of coral reefs to some extent. |
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
刘欣, 张华国, 马蕴涵, 等. 基于机器学习与半分析模型的珊瑚礁底栖类型遥感监测方法研究[J]. 海洋技术学报, 2025, 44(4): 1-12.
|
| [47] |
傅杨淦, 朱岚巍, 吴虹蓉, 等. 基于改进U-Net的珊瑚礁底栖物质信息提取方法[J]. 计算机工程, 2023, 49(12): 231-242.
珊瑚礁底栖物质信息提取在珊瑚礁遥感监测领域具有重要意义。SVM、最大似然法等传统珊瑚礁底栖物质信息提取方法存在精度不高、不够自动化、时间成本较高等问题。目前深度学习方法在语义分割领域已有广泛应用，且取得了较好的效果，为此，利用深度学习技术设计一种基于改进U-Net的分割网络模型，以进行珊瑚礁底栖物质信息提取。为了保留分割细节，对编码器的每个层级设置一种多输入的方式。将ResNet34的残差部分结构作为网络的编码器，以提取更丰富的特征。结合分解卷积、注意力机制和通道混洗操作设计一种新的特征提取块，并将其代替编码器、底层和解码器中的普通卷积层。同时，通过注意力机制来改善U-Net模型的远跳连接，对权重进行调整，以提高分割精度。在三亚地区的GF-2多光谱遥感影像上进行实验，提取的地物类别分别为健康珊瑚礁、白化珊瑚礁、藻类混合物、沙、浪花、深海区和陆地，通过面向对象方法并结合Google Earth影像进行目视解译以修订建立数据集。实验结果表明，该模型的平均交并比和平均F1值分别达到67.17%和78.7%，与常用的分割模型相比，其在视觉效果和评价指标上更优，消融实验结果也验证了改进模块的有效性。
The extraction of coral reef benthic material information is of great significance in coral reef remote sensing monitoring. Traditional information extraction methods for benthic organisms on coral reefs, such as Support Vector Machine(SVM) and the maximum likelihood method, have several drawbacks, including poor accuracy, low level of automation, and high time cost. At present, methods based on deep learning are being widely used in image segmentation, and satisfactory results have been obtained. Therefore, a segmentation network model based on improved U-Net and deep learning technology is designed to extract benthic material information from coral reefs. To improve segmentation details, a multi-input mode is set for each level of the encoder. The residual structure of ResNet34 is used as the encoder of the network to extract more abundant features. A new feature extraction block is designed by combining decomposition convolution, attention mechanism, and channel shuffle operation, to replace the common convolution layer in the encoder, bottom layer, and decoder; Concurrently, the attention mechanism is used to improve the far hop connection of the U-Net model, adjust the weight, and improve segmentation accuracy. Based on the GF-2 multispectral remote sensing image of Sanya, the extracted ground objects are classified as healthy coral reef, albino coral reef, algal mixture, sand, spray, deep sea area, and land. The dataset is established through visual interpretation and revision of an object-oriented method combined with Google Earth image. The experimental results show that mean Intersection over Union(mIoU)and average F1 score of the model in this study reached 67.17% and 78.7%, respectively. Compared with commonly used segmentation models, the proposed model performs better in visual effect and evaluation indicators. The ablation experimental results prove the effectiveness of the improved module. |
| [48] |
|
| [49] |
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.
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
逄今朝, 任广波, 施祺, 等. 基于底质类型变化监测的2005—2018年西沙永乐群岛珊瑚礁白化分析[J]. 海洋科学, 2021, 45(6): 92-106.
|
| [55] |
吴柯, 杨帆, 徐莹. 基于多时间序列Landsat-8遥感影像的珊瑚礁白化监测[J]. 地质科技通报, 2022, 41(5): 181-189.
|
| [56] |
潘德炉. 海洋遥感基础及应用[M]. 北京: 海洋出版社, 2017.
|
| [57] |
|
| [58] |
Ratio processing methods are reviewed, and a new method is proposed for extracting water depth and bottom type information from passive multispectral scanner data. Limitations of each technique are discussed, and an error analysis is performed using an analytical model for the radiance over shallow water.
|
| [59] |
|
| [60] |
|
| [61] |
张振兴, 郝燕玲. 卫星遥感多光谱浅海水深反演法[J]. 中国航海, 2012, 35(1): 13-18.
|
| [62] |
|
| [63] |
邸凯昌, 丁谦, 陈薇, 等. 南沙群岛海域浅海水深提取及影像海图制作技术[J]. 国土资源遥感, 1999, 41(3): 59-64.
|
| [64] |
陈本清, 杨燕明, 罗凯. 基于高分一号卫星多光谱数据的岛礁周边浅海水深遥感反演[J]. 热带海洋学报, 2017, 36(2): 70-78.
高分一号卫星作为我国首颗对地观测高分辨率卫星, 充分挖掘其在海洋领域的应用潜力具有重要意义。以西沙群岛晋卿岛周边浅海水域为研究区域, 应用国产高分一号卫星多光谱数据, 在开展图像几何校正、大气校正和耀斑校正预处理的基础上, 应用常用的双波段线性和对数比值模型开展晋卿岛周边浅海水深反演, 并利用实测水深数据开展精度评价, 对比分析不同模型反演结果, 探讨影响岛礁浅海水深反演精度的可能因素。研究表明, 双波段线性模型的反演精度要明显优于对数比值模型, 更适合应用于晋卿岛周边浅海水深反演, 其20m以浅水深反演均方根误差为1.8m, 在5m以浅区域的均方根误差为1.14m, 达到了目前浅海水深卫星遥感反演的精度水平。
It is of great significance to fully exploit the oceanic application potential of the GaoFen-1 data, which is the first domestic high-resolution satellite of Earth observation in China. In this study, a GaoFen-1 multi-spectral image is used to retrieve the island shallow water depth of Jinqing Island in Xisha sea area by adopting the dual-band linear model and log-ratio model, after serials of image processing including image geometric correction, atmospheric correction and sun-glint correction. The accuracy of the two models is evaluated by using actual water depth data. Meanwhile, possible influence factors of retrieval accuracy of island shallow water depth from satellite data are discussed. It is shown that the result from the dual-linear model is obviously better than that from the log-ratio model, and the dual-band linear model is more suitable for the retrieving shallow water depth of Jinqing Island. For water depth less than 20 m, the overall root mean square (RMS) error is 1.8 m with RMS error of 1.14 m within 5 m, which reaches the level of accuracy of water depth inversion by satellite remote sensing. |
| [65] |
|
| [66] |
Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a novel approach for quantitatively inverting the bottom reflectance at 550 nm without the dependence of in situ bottom reflectance data or any other priori knowledge. By linking different pixels in the same image and utilizing the strong linear relationship between their water depths and the spectral related parameters, the global situation of the radiative transfer model was constrained, and an exponential relationship between the log-transformed ratio of the blue–green band reflectance and the bottom reflectance was established. The proposed model was checked by comparing the Hydrolight input bottom reflectance with that inverted from Hydrolight simulated spectrum, resulting in correlating well. Our method has successfully performed using WorldView-2 and Landsat-8 in Midway Island in the North Pacific Ocean, with the cross- and indirectly checking and obtained reliable and robust results. In addition, we assessed the potential of the quantitative bottom reflectance in benthic classification and inversion ranges under different bottom reflectance. These results indicated that compared with those methods relying on in situ data or hyperspectral imagery, our algorithm is more likely to efficiently improve the parameterization of bottom reflectance, which can be very useful for benthic habitat mapping and transferred to large-scale regions in clean reef waters, as well as monitor time-series dynamics of oceanic bottom types to forecast coral reef bleaching.
|
| [67] |
To achieve high accuracy bathymetry retrieval using remote sensing images with robust performance in a 0 to 25 m-deep lagoon with sharp bottom depth variations, a new Iterative Multiple Band Ratio (IMBR) algorithm is tested against known Multiple Band Ratio (MBR) and Single Band Ratio (SBR) algorithms. The test was conducted using the five multispectral bands, at 10 to 60 m resolution, of a Sentinel-2 image of the 25 km2 Poe lagoon, a UNESCO World Heritage Area. The IMBR approach requires training datasets for the definitions of depth threshold at which optimal band ratios vary. IMBR achieved accuracy, quantified with an original block cross-validation procedure across the entire depth range reached a mean absolute error of 46.0 cm. It compares very favorably against MBR (78.3 cm) and the various SBR results (188–254 cm). The method is suitable for generalization to other sites pending a minimal ground-truth dataset crossing all the depth range being available. We stress that different users may need different precisions and can use MBR or SBR algorithms for their applications. For the hydrodynamic modelling applications that are developing in New Caledonia, the IMBR solutions applied to Sentinel imagery are optimal.
|
| [68] |
|
| [69] |
For analytical or semianalytical retrieval of shallow-water bathymetry and/or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance (K(d)), one for the upwelling radiance of the water column (K(u)(C)), and one for the upwelling radiance from bottom reflection (K(u)(B)). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above (R(rs)) and below (r(rs)) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r(rs) of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption (a) and backscattering (b(b)). For remote-sensing inversion, parameters connecting R(rs) and r(rs) are also derived. It is found that r(rs) values determined by the SA model agree well with the exact values computed by Hydrolight (~3% error), even for Hydrolight r(rs) values calculated with different particle phase functions. The Hydrolight calculations included b(b)/a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.
|
| [70] |
Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accurately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inversion algorithms are available, there has been limited assessment of performance and no work has been published comparing their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational efficiencies of one empirical and five radiative‐transfer‐based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative‐transfer—based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/ substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re‐analysis of these same or additional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches.
|
| [71] |
|
| [72] |
|
| [73] |
|
| [74] |
|
| [75] |
|
| [76] |
|
| [77] |
|
| [78] |
|
| [79] |
Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.
|
| [80] |
|
| [81] |
Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.
|
| [82] |
|
| [83] |
|
| [84] |
|
| [85] |
In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.
|
| [86] |
|
| [87] |
|
| [88] |
|
| [89] |
|
| [90] |
|
| [91] |
|
| [92] |
|
| [93] |
于俊, 陈辉, 朱大明, 等. 基于软硬协作决策的半监督珊瑚礁底质分类方法[J]. 海洋学报, 2023, 45(4): 154-164.
|
| [94] |
|
| [95] |
|
| [96] |
|
| [97] |
|
| [98] |
|
| [99] |
|
| [100] |
吴虹蓉, 朱岚巍, 施冬. 面向对象的三亚珊瑚礁底质信息提取[J]. 测绘通报, 2022(9): 63-67.
珊瑚礁对于海洋生态环境研究具有重要意义,通过分析珊瑚礁底栖物质的分布及健康状况,可以对珊瑚礁生态环境进行评估。本文提出了一种基于面向对象的图像分类方法,通过试验确定不同地貌的最优分割尺度,其中陆地和深海的最优分割尺度为150,各类底栖物质的最优分割尺度为30。以Sentinel-2A卫星遥感影像为例,提取海南三亚珊瑚礁自然保护区的珊瑚礁底栖物质,并使用混淆矩阵对提取结果进行精度评估。结果表明,底栖物质提取总体分类精度为87.91%,Kappa系数为0.83。面向对象分类方法可有效结合珊瑚礁底栖物质的纹理特征和光谱特征,并充分利用遥感影像不同波段的组合特性,可为三亚珊瑚礁保护管理提供方法支撑。
Coral reefs are of great significance to the study of marine ecological environment. The ecological environment of coral reefs can be evaluated by analyzing the distribution and health status of benthic materials. In this paper, an object-based image classification method is used to determine the optimal segmentation scale of different landforms through experiments. Among them, the optimal segmentation scale of terrestrial and deep-sea is 150, and the optimal segmentation scale of various benthic substances is 30. Furthermore, Sentinel-2A satellite remote sensing images are used to extract benthic materials from coral reefs in Sanya Coral Reef Nature Reserve, Hainan province, and confusion matrix is used to evaluate the accuracy of extraction results. The results show that the overall classification accuracy of benthic material extraction is 87.91%, and the Kappa coefficient is 0.83. The object-oriented classification method can effectively combine the texture characteristics and spectral characteristics of benthic material of coral reef and make full use of the combination characteristics of different bands of remote sensing image, and the extraction results can provide methodological support for reef protection and management in Sanya.
|
| [101] |
|
| [102] |
Australia’s Great Barrier Reef (GBR) is a globally unique and precious national resource; however, the geomorphic and benthic composition and the extent of coral habitat per reef are greatly understudied. However, this is critical to understand the spatial extent of disturbance impacts and recovery potential. This study characterizes and quantifies coral habitat based on depth, geomorphic and benthic composition maps of more than 2164 shallow offshore GBR reefs. The mapping approach combined a Sentinel-2 satellite surface reflectance image mosaic and derived depth, wave climate, reef slope and field data in a random-forest machine learning and object-based protocol. Area calculations, for the first time, incorporated the 3D characteristic of the reef surface above 20 m. Geomorphic zonation maps (0–20 m) provided a reef extent estimate of 28,261 km2 (a 31% increase to current estimates), while benthic composition maps (0–10 m) estimated that ~10,600 km2 of reef area (~57% of shallow offshore reef area) was covered by hard substrate suitable for coral growth, the first estimate of potential coral habitat based on substrate availability. Our high-resolution maps provide valuable information for future monitoring and ecological modeling studies and constitute key tools for supporting the management, conservation and restoration efforts of the GBR.
|
| [103] |
|
| [104] |
Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological processes and lead management actions. In this study, we propose a semiautomated framework for the detection and mapping of benthic habitats and seagrass species using convolutional neural networks (CNNs). Benthic habitat field data from a geo-located towed camera and high-resolution satellite images were integrated to evaluate the proposed framework. Features extracted from pre-trained CNNs and a “bagging of features” (BOF) algorithm was used for benthic habitat and seagrass species detection. Furthermore, the resultant correctly detected images were used as ground truth samples for training and validating CNNs with simple architectures. These CNNs were evaluated for their accuracy in benthic habitat and seagrass species mapping using high-resolution satellite images. Two study areas, Shiraho and Fukido (located on Ishigaki Island, Japan), were used to evaluate the proposed model because seven benthic habitats were classified in the Shiraho area and four seagrass species were mapped in Fukido cove. Analysis showed that the overall accuracy of benthic habitat detection in Shiraho and seagrass species detection in Fukido was 91.5% (7 classes) and 90.4% (4 species), respectively, while the overall accuracy of benthic habitat and seagrass mapping in Shiraho and Fukido was 89.9% and 91.2%, respectively.
|
| [105] |
|
| [106] |
|
| [107] |
High spatial resolution benthic habitat information is essential for coral reef protection and coastal environmental management. Satellite-based shallow benthic composition mapping offers a more efficient approach than traditional field measurements, especially given the advancements in high spatial and temporal resolution satellite imagery. The Planet Dove satellite constellation now has more than 150 instruments in orbit that offer daily coverage at high spatial resolution (3.7 m). The Dove constellation provides regularly updated imagery that can minimize cloud in tropical oceans where dense cloud cover persists. Daily image acquisition also provides an opportunity to detect time-sensitive changes in shallow benthic habitats following coral bleaching events, storms, and other disturbances. We developed an object-based coral reef habitat mapping approach for Dove and similar multispectral satellites that provides bathymetry estimation, bottom reflectance retrieval, and object-based classification to identify different benthic compositions in shallow coastal environments. We tested our approach in three study sites in the Dominican Republic using 18 Dove images. Benthic composition classification results were validated by field measurements (overall accuracy = 82%). Bathymetry and bottom reflectance significantly contributed to identifying benthic habitat classes with similar surface reflectance. This new object-based approach can be effectively applied to map and manage coral reef habitats.
|
| [108] |
|
| [109] |
|
| [110] |
|
| [111] |
|
| [112] |
|
| [113] |
|
| [114] |
|
| [115] |
|
| [116] |
|
| [117] |
|
| [118] |
|
| [119] |
|
| [120] |
|
| [121] |
|
| [122] |
|
| [123] |
|
| [124] |
|
| [125] |
|
| [126] |
|
| [127] |
|
| [128] |
|
| [129] |
|
| [130] |
|
| [131] |
|
| [132] |
|
| [133] |
|
| [134] |
|
| [135] |
|
| [136] |
Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image pixels may represent mixed proportions of coverage. In order to address this, we describe the development, application, and testing of a spectral index for mapping live coral cover using CASI-2 airborne hyperspectral high spatial resolution imagery of Heron Reef, Australia. Field surveys were conducted in areas of varying depth to quantify live coral cover. Image statistics were extracted from co-registered imagery in the form of reflectance, derivatives, and band ratios. Each of the spectral transforms was assessed for their correlation with live coral cover, determining that the second derivative around 564 nm was the most sensitive to live coral cover variations(r2 = 0.63). Extensive field survey was used to transform relative to absolute coral cover, which was then applied to produce a live coral cover map of Heron Reef. We present the live coral cover index as a simple and viable means to estimate the amount of live coral over potentially thousands of km2 and in clear-water reefs.
|
| [137] |
|
| [138] |
|
| [139] |
|
| [140] |
黄晖, 俞晓磊, 黄林韬, 等. 珊瑚礁生态学研究现状和展望[J]. 热带海洋学报, 2024, 43(3): 3-12.
|
| [141] |
|
| [142] |
This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal correlation with satellite-based turbidity indicators across seven Chinese port areas. Results indicate that both Sentinel-2 and Landsat 8, using a three-band combination, achieved comparable performance in SDB estimation, with R2 values exceeding 0.85. However, turbidity showed a negative correlation with SDB accuracy, and higher turbidity levels limited the maximum retrievable water depth, resulting in SDB variances ranging from 0 to 15 m. Landsat 8 was more accurate in low to moderate turbidity environments (12–15), where SDB variance was lower, while higher turbidity (above 15) led to greater SDB variance and reduced accuracy. Sentinel-2 outperformed Landsat 8 in moderate to high turbidity environments (36–203), delivering higher R2 values and more consistent SDB estimates, making it a more reliable tool for areas with variable turbidity. These findings suggest that SDB is a viable method for bathymetric and turbidity mapping in diverse port settings, with the potential for broader application in coastal monitoring and marine management.
|
| [143] |
|
| [144] |
段正贤, 左秀玲, 余克服, 等. 地貌分带下基于ICESat-2与GF-1的珊瑚礁水深遥感优化反演方法[J]. 地理科学, 2023, 43(9): 1640-1648.
研究以中国南海西沙群岛华光礁西部剖面为试验区，采用高分一号（GF-1）遥感影像及 “冰、云和陆地高程2号” （ICESat-2）星载激光测高数据，基于珊瑚礁地貌分带建模优选策略构建了试验区25 m以浅水深遥感反演模型，并将模型外推应用至西沙群岛有实地调查水深数据的珊瑚礁剖面研究区。建模结果显示，礁坡和深潟湖地貌采用波段比值二项式回归模型，礁坪、浅潟湖和点礁地貌均采用多波段比值回归模型，可以使得地貌单元内基于GF-1影像的水深遥感反演达到最优效果。模型反演水深数据与实地调查水深数据相关系数为0.95；实地调查水深数据与ICESat-2深水区数据综合验证结果显示，模型均方根误差为1.34 m，平均绝对误差为1.08 m，平均相对误差为17.10%。研究表明，本文提出的基于地貌的主被动遥感水深反演模型显著改进了珊瑚礁区中分辨率遥感水深反演模型的精度，可为大范围珊瑚礁区高精度的水深反演提供方法支持。
|
| [145] |
|
| [146] |
汪万智, 邹欣庆, 李海宇. 南海珊瑚礁光学浅水海域多光谱遥感水深反演[J]. 高校地质学报, 2022, 28(5): 758-767.
|
| [147] |
|
| [148] |
|
| [149] |
|
| [150] |
The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (Rrsλ) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which Rrsλ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from Rrsλ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R2) was greater than 0.98, and the root mean squared error (RMSE) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.
|
| [151] |
|
| [152] |
龚剑明, 朱国强, 杨娟, 等. 面向对象的南海珊瑚礁地貌单元提取[J]. 地球信息科学学报, 2014, 16(6): 997-1004.
南海珊瑚礁地貌信息的提取是珊瑚礁资源利用、生态保护与管理及可持续发展的重要地学基础。本文提出了面向对象的珊瑚礁地貌单元提取模型,针对不同的地貌单元,以不同地貌单元的最优分割尺度、光谱参数、形状参数来分割影像并合并成不同对象,从而获得相应的地貌单元。通过大量实验得出自然地貌的最优分割尺度区间为[140,600],其中附礁生物稀疏带及丛生带、礁坑发育带的光谱参数和形状参数分别为0.9和0.1,其他自然地貌单元的光谱参数和形状参数分别为0.8和0.2;人工地貌的最优分割尺度区间为[25,170],其光谱参数和形状参数分别为0.8和0.2。进一步以南沙群岛簸箕礁WorldView-2高分辨率遥感影像为例提取地貌单元,并结合混淆矩阵和Kappa系数对分类结果进行了精度评价,地貌单元提取总体精度达到了85.75%,Kappa系数为0.8349。结果表明,该方法可有效运用南海珊瑚礁遥感影像的光谱特征、纹理特征,以及影像数据不同波段的组合特性,综合了影像和珊瑚礁地貌的关联特性,充分利用了珊瑚礁不同地貌相带的异质性,获得了理想的南海珊瑚礁地貌的整体信息,满足了我国南海珊瑚礁地貌信息提取和地貌数字产品生成的需求。
|
| [153] |
胡蕾秋, 刘亚岚, 任玉环, 等. SPOT5多光谱图像对南沙珊瑚礁信息提取方法的探讨[J]. 遥感技术与应用, 2010, 25(4): 493-501.
珊瑚礁在海洋生态系统中具有重要的作用,国外对珊瑚礁遥感信息提取研究较早,但国内起步较晚,目前仍以目视判读为主。使用SPOT5 10 m多光谱数据对我国南沙群岛中弹丸礁和光星礁进行了分类实验,并以QuickBird融合影像判读结果及岛礁调查资料为参考,探讨了SPOT5对珊瑚礁水下信息的辨识能力。首先利用阈值分割方法将水下和水上信息分离；对于水下信息,尝试进行Deglint纠正并引入衍生波段,用4种波段组合方案分别进行最大似然法分类后进行结果比较。结果表明,Deglint纠正能够有效消除水面噪声,从而提高分类精度；近红外波段对水下信息提取有辅助作用,可帮助纠正一些由水深导致的错分；衍生波段替代近红外波段分类效果略差。总之,SPOT5 10 m多光谱数据经过图像纠正后能够有效提取珊瑚岛礁水下成分信息,其分类精度可达80%以上。
|
| [154] |
左秀玲, 苏奋振, 赵焕庭, 等. 南海珊瑚礁高分辨率遥感地貌分类体系研究[J]. 地理科学进展, 2018, 37(11): 1463-1472.
珊瑚礁遥感地貌分类体系在珊瑚礁遥感地貌制图中具有重要的指导作用。目前,珊瑚礁遥感地貌分类体系仍存在构建标准不统一、部分重要地貌类型不突出以及涵盖地貌类型不完备等问题,影响了珊瑚礁遥感地貌制图应用于珊瑚礁科学和管理。本文采用中国南海46个珊瑚礁(环礁、台礁)的高分辨率遥感影像(WorldView-2、Quickbird),并结合西沙群岛15个岛礁的地貌实地调查数据进行南海高分辨率遥感地貌分类体系的构建研究。以各地貌类型所处礁体位置、动力特征、出露程度和沉积类型为划分标准,将相似尺度和重要性相当的地貌类型归为同等级别,共构建了3级19类南海珊瑚礁遥感地貌分类体系。该分类体系划分标准统一、地貌类型数量最多且完备,新命名了内礁坪生物稀疏带、内礁坪生物丛生带,补充了水下礁脊、潮间带浅滩、浅水礁塘等地貌类型,便于直观理解和推断不同尺度的海洋生态相互作用及重要性。同时,其多等级多尺度性适用于分辨率由低到高的南海珊瑚礁遥感地貌制图,有助于珊瑚礁态势演变分析、管理和保护,为维护国家海洋权益作出应有的贡献。
Coral reef geomorphic classification scheme plays an important role in coral reef geomorphic mapping based on remote sensing images. At present, there are various problems in the coral reef geomorphic classification schemes for remote sensing, such as inconsistent construction standards, unreasonable levels of some important geomorphic types, and incomplete geomorphic type coverage, which limit the application of geomorphic mapping for coral reef science and management. This articel advocates a systematic and hierarchical geomorphic classification scheme for reefs in the South China Sea for remote sensing, which concludes three levels and 19 classes from the sea to the lagoon or island. It is constructed based on high-resolution satellite images (WorldView-2, Quickbird) of 46 reefs (atolls, table reefs) and field survey data of 15 reefs in the Xisha Islands. Geomorphic types are derived and described according to the criteria of reef location, hydrodynamic characteristics, exposure degree and sediments. Geomorphic types are placed at the same level when they possess similar scale and importance. Cay and island are placed in the first level to show their importance. The scheme has consistent standardization, the highest hierarchical structure, and the largest number of geomorphic types. In the scheme, biotic sparse zone of the inner reef flat, biotic dense zone of the inner reef flat are named; submerged reef ridge, intertidal sand zone, and shallow reef pond are supplemented. The classification scheme facilitates the intuitive understanding and inference of the processes and importance of marine ecological interaction across different scales. This hierarchical geomorphic classification scheme with multi-scale characteristic is applicable to the coral reef geomorphic mapping from low-resolution to high-resolution satellite images, contributing to the change analysis, management, and protection of China’s coral reefs in the South China Sea. |
| [155] |
李晓敏, 马毅, 吕喜玺. 南海珊瑚岛礁遥感分类体系和解译标志[J]. 海洋科学, 2021, 45(5): 23-30.
|
| [156] |
|
| [157] |
Coral islands and reefs are formed by the cementation of the remains of shallow water reef-building coral polyps and other reef dwelling organisms in tropical oceans. They can be divided into coral islands, coral sandbanks, coral reefs, and coral shoals, of which, Coral shoals are located below the depth datum and are not exposed even at low tide, and sometimes are distributed at water depths exceeding 30 m. Satellite images with wide spatial–temporal coverage have played a crucial role in coral island and reef monitoring, and remote sensing data with multiple platforms, sensors, and spatial and spectral resolutions are employed. However, the accurate detection of coral shoals remains challenging mainly due to the depth effect, that is, coral shoals, especially deeper ones, have very similar spectral characteristics to the sea in optical images. Here, an optical remote sensing detection method is proposed to rapidly and accurately detect the coral shoals using a deep belief network (DBN) from optical satellite imagery. The median filter is used to filter the DBN classification results, and the appropriate filtering window is selected according to the spatial resolution of the optical images. The proposed method demonstrated outstanding performance by validating and comparing the detection results of the Yinli Shoal. Moreover, the expected results are obtained by applying this method to other coral shoals in the Xisha Islands, including the Binmei Shoal, Beibianlang, Zhanhan Shoal, Shanhudong Shoal, and Yongnan Shoal. This detection method is expected to provide the coral shoals’ information rapidly once optical satellite images are available and cloud cover and tropical cyclones are satisfactory. The further integration of the detection results of coral shoals with water depth and other information can effectively ensure the safe navigation of ships.
|
| [158] |
|
| [159] |
|
| [160] |
|
| [161] |
|
| [162] |
|
| [163] |
|
| [164] |
刘白露, 管磊. 南海珊瑚礁白化遥感热应力检测改进方法研究[J]. 自然资源遥感, 2021, 33(4): 136-142.
|
| [165] |
陆永强, 陈正华, 余克服, 等. 1985—2019年南海诸岛珊瑚礁区热压力时空变化研究分析[J]. 海洋学报, 2022, 44(11): 179-190.
|
| [166] |
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.
|
| [167] |
|
| [168] |
|
| [169] |
李天驰, 王道儒, 赵亮, 等. 基于Landsat8遥感数据的西沙群岛永乐环礁底质分类与变化分析[J]. 自然资源遥感, 2023, 35(2): 70-79.
|
| [170] |
周雅君, 何明郡, 刘聪, 等. 基于Sentinel-2卫星影像的海南西岛珊瑚礁识别和变化分析[J]. 海洋与湖沼, 2024, 55(1): 65-76.
|
/
| 〈 |
|
〉 |
