7. SDG 14 Life Below Water

Target: SDG 14.1 By 2025, prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution.

Since the large-scale production of plastics, their global production has shown an exponential growth trend (Borrelle et al. 2020), and the total global plastic production in 2022 reached 400 million tons (Plastics Europe 2023). The improper handling and fragmentation of environmental plastic waste have led to a continuous increase in microplastics (MPs) pollution (microplastics with particle sizes less than 5 mm). More and more studies have shown that microplastics are widely present in the global marine environment (Lima et al. 2021) and pose significant risks to the ecological environment and human health. Therefore, marine microplastics, as a new type of pollutant, have received high attention from scholars and the public both domestically and internationally, and the level of attention continues to rise. The Arctic Ocean is an important “window” of global environmental change. It also occupies a special strategic position in the “Polar Silk Road”. At the same time, there are famous fishing grounds and unique biological species. Researchers found that the branches of the Arctic hot salt circulation can bring plastic waste from other oceans to the marginal sea of the Arctic Ocean, and put forward the view that the seabed of the Arctic Ocean may be an important “sink” of plastic waste (Cózar et al. 2017; Obbard et al. 2014; Peeken et al. 2018). However, the environmental problems of microplastics in the Arctic Ocean are not limited to scientific problems, but also can be extended to one of the focuses of global governance, economy and politics. So far, we have little research on microplastics in the seawater of the Arctic-Pacific sector, and lack a comprehensive understanding of the pollution status and the fate of microplastics in the Arctic Ocean.

Within the framework of the SDGs of the 2030 Agenda, reducing marine debris is one of the important indicators for the prevention and control of “marine pollution”. Therefore, this case study focuses on the UN SDGs 14.1 and uses published data and scientific investigation data to carry out research on the current situation and distribution characteristics of microplastic pollution in different media in the Arctic-Pacific sector, so as to provide basic data for the assessment of microplastic pollution, migration and environmental impact in the Arctic-Pacific sector. This case study will also help to link up the relevant international policies on environmental protection in the polar regions and support the research on the SDGs of the UN.

Based on the data published in international journals and scientific investigation data, the occurrence state of microplastics in water, sediments and typical organisms in the Arctic-Pacific sector was analyzed by using the abundance, particle size and polymer types of marine microplastics. The correlation of microplastics in different media was studied, and the spatial distribution pattern of microplastics in the Arctic-Pacific sector was systematically analyzed. Combined with the action of ocean currents, the spatial pattern of microplastics in the sea area and the influence of ocean current transport were explored. In this case study, microplastic samples in seawater were mainly collected by pump and trawl. Microplastics in sediments were extracted by density flotation. Microplastics in biology were separated after digestion by weak oxidation. The polymer types of microplastics were identified by using a micro-Fourier Transform Infrared Spectroscopy.

The types of microplastics in the seawater of the Arctic-Pacific sector mainly included rayon, polyethylene terephthalate (PET), polyacrylamide (PAM), chlorinated polyethylene (CPE), polyethylene (PE), polyester fiber, poly(vinyl alcohol) (PVA), polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), polytetrafluoroethylene (PTFE), polyamide (PA), poly(acrylic acid) (PAA), polyvinylidene chloride (PVDC), polybutylene terephthalate (PBT) and acrylic, among which PA (56.21%) and PET (15.83%) accounted for a relatively high proportion. The proportions of all detected plastic particles with particle sizes of 0.0–1.0 mm and 1.0–2.0 mm were relatively high, which were 69.82% and 21.75% respectively. A total of 11 colors of microplastics were detected, of which black (38.46%), blue (25.15%) and gray (17.46%) accounted for a high proportion, followed by red (5.77%), green (5.18%) and transparent color (4.18%). The shape of microplastics was mainly fibrous, accounting for 94.53%, with minor fragment, granular and film (Fig. 7.1).

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The abundance of microplastics in all study areas ranged from N.D.-260.57 items/m³, with an average abundance of 34.31 items/m³ and a median of 21.20 items/m³. The average abundance of microplastics in the Arctic Ocean was 34.52 items/m³, and the average abundance of microplastics in the Pacific sector was 22.25 items/m³. On the whole, there were differences in the abundance distribution of microplastics between the Arctic Ocean and the Pacific sector. High microplastic abundance appeared in the Beaufort vortex area. It can be seen that the ocean current vortex convergence had an agglomeration-driving effect on microplastics (Ross et al. 2021) (Fig. 7.2).

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The abundance range of microplastics detected in the sediments of the study sea area was 5.30 items/kg-220.00 items/kg, and the average abundance was 83.24 items/kg. Seven types of microplastics were found, among which PET, PP and PS accounted for a relatively high proportion, accounting for 34%, 31% and 11% respectively, followed by rayon, PVC, PA and polyacrylonitrile (PAN). All detected microplastics were mainly fibrous (60%), fragment (22%) and film (18%). Microplastics with particle sizes ranging from 1.0 to 2.0 mm were relatively more, accounting for 47.0% of the total detected microplastics (Mu et al. 2019; Kanhai et al. 2019) (Fig. 7.3).

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From the spatial distribution characteristics of microplastics, it was found that the abundance of microplastics in the sediments close to the central Arctic Ocean was high, indicating that there was a “sink” phenomenon of microplastic deposition in the study area. In addition, there were many low-density microplastics in the sediments, which revealed the phenomenon of the vertical transport mechanism of water to microplastics in the area, indicating that it had a certain driving effect on the vertical migration of microplastics.

Six typical benthos in the Arctic-Pacific sector were analyzed (Fang et al. 2018, 2021), including whelks (L. hypolispus, R. daphnelloides and E. nana), shrimp (P. borealis), crab (C. opilio), starfish (A. rubens, C. crispatus and L. polaris), brittle star (O. sarsii) and bivalves (A. crenata and M. tokyoensis). The abundance of microplastics in all benthos ranged from 0.18 items/ind. to 0.83 items/ind., and the average abundance of microplastics was 0.58 items/ind. The main types of microplastics in organisms were PA, PE, PET and cellophane (CP), with percentages of 46.80%, 22.41%, 24.25% and 6.53% respectively. There were many microplastics with particle sizes ranging from 0.1 to 1.5 mm, accounting for about 69.53% (Fig. 7.4).

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It was found that there were some differences in the enrichment of microplastics by different individuals of benthos. The enrichment characteristics of microplastics in organisms of different species were not only affected by the distribution of microplastics in their habitat, but also related to their living habits and feeding patterns. The spatial distribution of high concentration microplastics in sediments and organisms in the study area was consistent, which were distributed in the ocean near the center of the Arctic Ocean. Combined with other studies, it was preliminarily inferred that Arctic sea ice and ocean currents had a certain impact on the migration and agglomeration of microplastics.

The surface current in the Arctic Ocean is mainly composed of Transpolar Drift and clockwise Beaufort Gyre, which control the direction of ice flow and modern sedimentary model in the Arctic Ocean. The Arctic Ocean communicates with the Atlantic Ocean and the Pacific Ocean through ocean currents. The seawater of the Pacific Ocean enters the Arctic Ocean through the Bering Strait, and the seawater flow is about 1 Sv. The Atlantic Ocean current is divided into two branches, which enter the Arctic Ocean through the Barents Sea and the Fram Strait, with a flow of about 3 Sv and 6.6 Sv respectively. Seawater from the Pacific Ocean and the Atlantic Ocean eventually flows out of the Arctic Ocean through the Davis Strait and Fram Strait (Rudels 2001; Polyakov et al. 2012) (Fig. 7.5).

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The spatial distribution characteristics of microplastics in the Arctic Ocean showed that the high concentration of microplastics in the seawater appeared in the Beaufort vortex area and in the intersection area between the Transpolar Drift and the West Spitsbergen Current from the Atlantic (the area near the Svalbard Islands). The results further proved that the vortex and ocean current accumulation area in the Arctic Ocean could lead to the agglomeration of microplastics, showing a high abundance of microplastics.

Note: The green arrow indicates warm North Atlantic water. The purple arrow is cold, low-salt polar water. The red arrow is low-salt denatured water. AC: Anadyr Current, ACC: Alaskan Coastal Current, BC: Baffin Current, BIC: Bear Island Current, BG: Beaufort Gyre, EGC: East Greenland Current, EIC: East Iceland Current, ESC: East Spitsbergen Current, IC: Irminger Current, JMC: Jan Mayen Current, MC: Murman Current, NAD: North Atlantic Drift, NAC: Norwegian Atlantic Current, NCC: Norwegian Coastal Current, SB: Siberian branch (of the Transpolar Drift), SCC: Siberian Coastal Current, TPD: Transpolar Drift, WGC: West Greenland Current, WSC: West Spitsbergen Current.

Highlights

As the global climate warms and the Arctic sea ice melts, the Arctic ecological environment is an important issue of concern to all stakeholders. Arctic environmental issues, including Arctic climate, marine life and environmental protection, are still in the initial stage of cognition, exploration and discussion. The Arctic Ocean and the Pacific sector are closely connected as a whole through the circulation system. Because the density of the incoming seawater from the Pacific Ocean is less than that from the Atlantic Ocean, the seawater from the Pacific sector will mainly enter the upper layer of the Arctic Ocean, which will have a more direct impact on the climate and environment of the Arctic Ocean. The environmental problems of the Arctic Ocean are related to the common interests of mankind. The environmental protection of the Arctic Ocean has the characteristics of borderlessness and globalization. The governance and protection of the Arctic Ocean environment require global solidarity and cooperation. Therefore, knowing and understanding the environmental status and development trend of the Arctic Ocean is the premise of effectively participating in Arctic environmental governance and safeguarding environmental rights and interests.

In order to promote and apply the spirit of the “Silk Road” in the Arctic region and the Arctic Ocean, the construction of the “Ice Silk Road” is the deepening and extension of the connotation of the BRI. From the perspective of global environmental protection and the maintenance of the common interests of mankind, it is of great significance to prevent, reduce and control the pollution of the Arctic marine environment. The research showed that the source, distribution and harm of marine microplastics had global significance. The irreversibility of environmental pollution, ecological risk and health risk is uncertain. Therefore, studying the occurrence form and spatial distribution characteristics of microplastics in the Arctic environment, and exploring the spatial migration process and migration law of microplastics, can predict the convergence and fate of marine microplastics in the environment, which could provide theoretical guidance for the treatment of marine microplastic pollution. In addition, in the future, we need to join hands with near-Arctic countries to build an environmental community and jointly explore effective new mechanisms for Arctic environmental governance.

Target: SDG 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems to avoid significant adverse impacts, including by strengthening their resilience, and take action for their restoration in order to achieve healthy and productive oceans.

As the importance of the marine economy in global economic development continues to grow, the contradiction between marine development and ecological environment protection has become increasingly prominent. Algal blooms (here refers to marine phytoplankton blooms), which are induced by the explosive proliferation and aggregation of marine phytoplankton caused by water eutrophication, are one of the most common ecological disasters when the environment is overloaded. Marine phytoplankton blooms resulting from the outbreak of toxic algae will release toxins, leading to the death of marine organisms and further exacerbating the deterioration of the marine environment. While the excessive reproduction of non-toxic algae will cause a decrease in the oxygen content in the ocean, resulting in asphyxiation of marine organisms such as fish due to a lack of oxygen, which also triggers the deterioration of the marine environment. Therefore, the outbreak of phytoplankton blooms will bring serious harm to the health of coastal ecosystems, fishery development, the safety of cooling sources for coastal nuclear power plants, and even human health and social development. Under the combined influence of human activities and climate change, the frequency and scale of nearshore marine phytoplankton blooms have shown a trend of increasing year by year (Dai et al. 2023).

Compared with traditional on-site monitoring methods, satellite remote sensing has the advantages of wide coverage and short revisit cycle. Satellite remote sensing monitoring can achieve large-area synchronous observation of marine phytoplankton blooms at a relatively low cost. The identification methods of marine phytoplankton blooms based on the abnormal concentration of chlorophyll a (Chla) retrieved by remote sensing have been widely applied in ocean waters (Wang et al. 2021; Dai et al. 2023). In addition, spectral index algorithms such as normalized fluorescence line height (nFLH), red tide index (RI), algal bloom index (RBI), red band difference (RBD), and the newly proposed commission internationale del’éclairage (CIE) method have all been successfully applied to the identification of marine phytoplankton blooms and achieved good retrieval results (Hu et al. 2005; Ahn and Shanmugam 2006; Amin et al. 2009; Hu and Feng 2017; Liu et al. 2022; Dai et al. 2023). The key to the above methods lies in how to determine the thresholds. Especially in sea areas lacking measured data, the application of these methods is subject to certain limitations. Recently, some studies have applied the 90th percentile threshold method, which is used for identifying heatwaves (i.e., extreme high-temperature events) in physical oceanography, to the identification of strong (high-concentration) phytoplankton bloom events. It has been applied to the identification of the climatic change of remote sensing chlorophyll a concentration data to identify strong (high-concentration) phytoplankton bloom events in the South China Sea (Lu et al. 2022). Herein, this case study proposes a novel approach integrating the 90th percentile threshold method with the typical colorimetry method, where intense phytoplankton bloom signals identified by the former serve as the basis for calculating thresholds in the latter for large-scale phytoplankton bloom detection. This strategy enables automated threshold determination in regions lacking prior bloom knowledge, thereby paving the way for automated dynamic bloom monitoring.

The North Indian Ocean, a critical sea area under the BRI, hosts extensive international collaboration among multiple partner nations. It is affected by dust storms and tropical cyclones all year round, thus facing great pressure on the marine environment and frequently suffering from phytoplankton bloom disasters. Clarifying the long-term dynamic changes of marine phytoplankton blooms in the North Indian Ocean is an important guarantee for the ecologically sustainable development and construction of the “Belt and Road”. It is also a key link in the dynamic monitoring, prevention and control of marine ecological disasters (algal blooms) as well as the health evaluation of marine ecosystems, which meets the indicator requirements of SDG 14.2. Currently, there are relatively few studies on the identification and long-term analysis of phytoplankton blooms in the North Indian Ocean. The spatiotemporal distribution characteristics of phytoplankton blooms in the North Indian Ocean remain to be revealed, and there is also a lack of the release of relevant achievement progress. Therefore, from the perspective of promoting the SDGs through scientific and technological innovation, the research on the dynamic distribution and causes of large-scale phytoplankton blooms in the North Indian Ocean has scientific value in aspects such as the formation mechanism of algal blooms and withstanding the impact of meteorological disasters on the marine ecological environment. It conforms to the goal of strengthening disaster resilience in SDG 14.2 and is of great significance for the achievement of the marine SDGs.

Due to the difficulty in obtaining measured data of phytoplankton blooms in the North Indian Ocean, the 90th percentile threshold method (Lu et al. 2022) was adopted to extract phytoplankton bloom data, which was used as a representation of the occurrence of strong phytoplankton bloom events. These data were then used to determine the angular thresholds required by the colorimetry method for retrieving phytoplankton blooms (Liu et al. 2022; Dai et al. 2023), in order to achieve the joint retrieval of phytoplankton blooms in the North Indian Ocean. Since the spatial resolution of the products used in this case study is 4 km and the determination of the chromatic angle thresholds is based on the information of strong phytoplankton blooms, the phytoplankton blooms identified in this case study are regarded as large-scale (large-area and high-concentration) phytoplankton blooms.

The specific calculation of the 90th percentile threshold method for retrieving strong phytoplankton blooms is as follows: Collect the Chla concentration data of all years in adjacent 11-day periods for each grid in the MODIS satellite data (including the data from 2003 to 2022), and form sample sets for each grid from these data grid by grid; Calculate the values located at the 90th percentile of the probability distribution in each sample set, and use them as a threshold for the occurrence of phytoplankton blooms in each grid; Subsequently, use a 31-day moving window to smooth the threshold sequence of each grid for each year; Finally, determine whether the Chla concentration at each grid point is continuously higher than the smoothed threshold for more than 3 days. If so, it is determined that a phytoplankton bloom event occurred at that grid (Lu et al. 2022).where, \({\text{Chla}}_{11\text{ days of all years}}\) represents the samples composed of the Chla concentration data in an adjacent 11-day window of each day for all years.

The specific steps of the CIE methods for phytoplankton bloom identification are as follows:where, R, G, and B respectively represent the red band (667 nm), green band (531 nm), and blue band (443 nm) of MODIS remote sensing data; \(x\) and \(y\) respectively denote the coordinates after the conversion of the R, G, and B color coordinate systems into the chromaticity angle coordinate system; \(\alpha \) represents the chromaticity angle threshold judged according to the 90th percentile threshold method (\(\alpha =100^\circ \)).

The spatial resolution of each element was resampled to be consistent with the spatial resolution (4 × 4 km) of the retrieval results of large-scale phytoplankton blooms. For each element, the climatological monthly mean value of each corresponding grid was subtracted to remove the seasonal signals. After standardizing the data with the seasonal signals removed, a multiple linear regression between each element and the retrieval results of large-scale phytoplankton blooms was established, and a qualitative analysis of the impact magnitude of each element on the algal blooms was conducted.

Using the L3 Chla concentration data integrated from MODIS Terra and Aqua, the spatiotemporal distribution maps of large-scale phytoplankton blooms in the North Indian Ocean from 2015 to 2022 were jointly extracted based on the colorimetry method and the 90th percentile threshold method (Fig. 7.6). The northern part of the Arabian Sea is the most prominent area of large-scale phytoplankton blooms in the North Indian Ocean, where large-scale phytoplankton blooms exist all year round. Significant large-area phytoplankton blooms also exist in the Arabian Sea basin. Narrow strip-shaped phytoplankton blooms successively appear in the sea areas such as the east coast of Somalia, the Gulf of Mannar, and the east coast of the Bay of Bengal. Large-scale phytoplankton blooms also exist in the Arabian Sea basin. By multiplying the number of phytoplankton bloom pixels by the corresponding resolution (4 × 4 km), it is estimated that the annual area of large-scale phytoplankton blooms in the North Indian Ocean is approximately 4.7 × 10¹² m². Meanwhile, the annual variations between the area and the intensity (i.e., Chla concentration) of these blooms are not completely synchronized. The intensity of large-scale phytoplankton blooms in the North Indian Ocean presents a gradually increasing trend, while the area of these blooms presents a weak decreasing trend (Fig. 7.6).

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There is a high similarity in the annual variation trends between the number of days and the intensity of large-scale phytoplankton blooms in the North Indian Ocean (Fig. 7.7). Specifically, along the northern and eastern coasts of the Arabian Sea, the number of days and the intensity of phytoplankton blooms show a decreasing trend year by year, while in the offshore basin area of the Arabian Sea, there is an increasing trend in the number of days and the intensity of phytoplankton blooms year by year. In the upwelling areas affected by river runoff, the increasing trends in both the number of days and the intensity of phytoplankton blooms are stronger than those in the upwelling areas with weaker runoff. The results of this case study are similar in trend to the latest results by Dai et al. (2023) along the coast of the North Indian Ocean (Dai et al. 2023). As a supplement, the results in this case study retain the original spatial resolution of MODIS, which can further reveal that there are also alternating increasing signals in the overall weakened spatial distribution of coastal phytoplankton blooms in the North Indian Ocean. Six key regions are selected to analyze their area variations of the large-scale phytoplankton blooms (Fig. 7.8). The area of large-scale phytoplankton bloom in the northern Gulf of Oman presents a decreasing trend year by year, while an increasing trend can be found in the Arabian Sea basin with the smallest occurrence area. The area of the large-scale phytoplankton blooms in the Gulf of Mannar is relatively small. The areas of the large-scale phytoplankton blooms on the east coast of Somalia, and on the north and east coasts of the Bay of Bengal are comparable, lying between those of the Gulf of Oman and the Gulf of Mannar (Fig. 7.8).

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In the six key regions, nutrients play a crucial role in the occurrence of phytoplankton blooms compared to the dynamic parameters of the sea surface. Besides nutrients, SST is the main influencing factor for large-scale phytoplankton blooms in most sea areas of the North Indian Ocean. In the offshore basin area of the North Indian Ocean, the contribution of the wind field to phytoplankton blooms gradually exceeds that of SST, which is, to some extent, related to the fact that the region is affected by strong winds from dust storms and the nutrients brought by dust deposition induce phytoplankton blooms (Fig. 7.9). In several major upwelling areas in the North Indian Ocean, temperature plays a more important role in phytoplankton blooms compared to PAR and the wind field. With the intensification of global warming, in the North Indian Ocean, where temperature is an important influencing factor for large-scale phytoplankton blooms, phytoplankton blooms may show an annual variation trend of increasing in intensity but decreasing in area.

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Highlights

By combining the 90th percentile threshold method and the colorimetry method, this case study identified the large-scale phytoplankton blooms in the North Indian Ocean from 2015 to 2022 using the ocean color satellite remote sensing datasets, wind field datasets and nutrient reanalysis datasets. The spatiotemporal distribution characteristics of the large-scale phytoplankton blooms in the North Indian Ocean were revealed, and the possible causes of these blooms were explored. The results show that the Northern Arabian Sea is the main region for large-scale phytoplankton blooms in the North Indian Ocean. In addition, a significant annual increasing trend of both the number of days and intensity of large-scale phytoplankton blooms were found in the Arabian Sea basin, while a significant annual decreasing trend occurred along the eastern coast of the Arabian Sea. SST is the main influencing factor for most phytoplankton blooms in the North Indian Ocean besides nutrients, especially in several major upwelling areas. Therefore, with the intensification of global warming, large-scale phytoplankton blooms in the North Indian Ocean may show a trend of increasing in intensity but decreasing in area. The methods adopted in this case study can provide important technical support for aspects including algal bloom monitoring, prevention, and control, as well as the sustainable development of fisheries. It can also provide a decision-making basis and scientific services for the countries participating in the BRI to enhance the sustainable management and protection capabilities of coastal and marine ecosystems. Moreover, these results can provide important information support along the BRI for achieving the marine SDGs that are in line with SDG 14.2, which aims to improve marine management and protection capabilities.

To meet international demands, relevant research can incorporate more ecological and physical parameters in the future, and build an effective physical-ecological coupling model as well as a systematic ecological impact assessment system for phytoplankton blooms. In addition, in combination with artificial intelligence, it is possible to further achieve refined monitoring and prediction of algal blooms in the countries participating in the BRI. This will enhance the capabilities of institutions such as the Group on Earth Observations (GEO), the State Oceanic Administration (SOA), and the International Centre for Ocean Development (ICOD) in terms of algal bloom monitoring, prevention, and control. These advances will provide data support and decision-making services for improving the sustainable management and protection capabilities of marine and coastal ecosystems and reducing marine pollution, serving SDG 14.2.

Target: SDG 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems to avoid significant adverse impacts, including by strengthening their resilience, and take action for their restoration in order to achieve healthy and productive oceans.

Against the backdrop of the rapid degradation of coral reefs worldwide, the coral reefs in the Xisha Islands have not been spared either, with their rate of degradation even exceeding the global average. For instance, the average coral coverage on Yongxing Island of the Xisha Islands dropped from 90% in 1980 to 20% between 2008 and 2009. In recent years, with the rapid development of China’s economy, society, and national defense, the demand for space in the South China Sea’s coral reefs has been increasing. Affected by global climate change, environmental pollution, overfishing, and unreasonable development activities, the degradation of coral reef ecosystems has become increasingly severe. This not only leads to a decline in biodiversity but also causes frequent natural ecological disasters, making it difficult to maintain the habitats of some islands and reefs.

However, current investigations of live coral cover (LCC) mainly rely on standard transect or photoquadrat methods, which are only suitable for surveys at typical sections and points. Due to the limited number of samples collected in actual surveys and the significant impact of the selection of transect and quadrat locations on the results, the uncertainty of investigation and monitoring outcomes has increased, making it difficult to adapt to comprehensive surveys of coral reefs. Therefore, it is all the more difficult to conduct regular on-site investigations of LCC, especially for large-scale coral reefs. As a result, countries urgently need to develop advanced coral reef ecological remote sensing monitoring methods to address the need for monitoring the ecological conditions of typical coral reef ecosystems.

With the increasing abundance of medium- and high-resolution multi-spectral satellite resources, the use of these satellite remote sensing technologies for dynamic monitoring of LCC has shown great potential and wide application prospects (Liao et al. 2021). In this case study, on the basis of systematic analysis of spectral reflectance characteristics of typical corals (Zhao et al. 2013), the second-order difference operator of blue wave segments was considered, and the regression analysis of field ecological survey data and band ratio (Souter et al. 2021) was used to construct an LCC model. By analyzing the space scalability, time scalability and sensor scalability of the model, the accuracy of the LCC inversion model that we have established is relatively high, and its root-mean-square error and average absolute value error are 8.84% and 5.79%, respectively. This method can effectively extract LCC information and provide accurate reference for the health analysis and protection management of the coral reef ecosystem.

Using bands 1, 2 and 3 of Landsat5 images, bands 1, 2, 3 and 4 of Sentinel-2 and Landsat 8 images, LCC was estimated according to the following formula:

When the image is Landsat8, B₁, B₂, B₃ and B₄ are the first, second, third and fourth bands of the image, respectively. When the image is Landsat5, B₂, B₃ and B₄ are the first, second and third bands of the image respectively, B₁ = B₂ + 200, and LCC is the live coral cover.

The LCC of typical islands and reefs is in a decreasing state. Using satellite data such as Landsat OLI and China’s “Haiyang 1C/D” (HY-1C/D), the LCC of the Hawaii Reef (Kure Reef) (Fig. 7.10) and Great Barrier Reef (Nameless Reef) (Fig. 7.11) in 2015, 2020 and 2022 was calculated. Remote sensing monitoring results show that the reefs and coral reefs in these two typical regions generally have a trend of degradation, and from 2015 to 2020, the LCC showed a significant decline, which was clearly reflected in the fact that the live coral distribution area was shrinking, with the Hawaii Reef (Kure Reef) decreasing from 24.00% to 18.63%, a decrease of 5.37%, which is basically consistent with the results of the field ecological survey in the same period, a decrease of 6.00%. The Great Barrier Reef (Nameless Reef) decreased from 18.16% to 10.85%, a decrease of 7.31%, which is basically consistent with the results of the field ecological survey in the same period, a decrease of 6.80%. From 2020 to 2022, the Great Barrier Reef (Nameless Reef) showed weak signs of recovery, and the Hawaii Reef (Kure Reef) was in a decline, but the reduction slowed down.

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Highlights

LCC is considered to be the most important index for measuring coral reef health, and remote sensing inversion of LCC is an important part of coral reef remote sensing monitoring. LCC can reflect the health status of the coral reef ecosystem. Global climate change, the outbreak of crown-of-thorns starfish and storm surge may cause changes in the health status of the coral reef ecosystem, and lead to changes in LCC. The inversion of LCC using multi-spectral satellite remote sensing images has good accuracy, and the model exhibits excellent scalability in terms of time, space, and sensors. However, the spectrum of coral reefs is different in different periods of time. Therefore, we need to develop advanced remote sensing monitoring methods for coral reef ecology to meet the monitoring needs of typical coral reef ecosystems.

China has successfully launched a series of marine remote sensing satellites covering ocean color, power, monitoring and so on, which provide a good opportunity for the research and application of coral reef ecological remote sensing. However, the existing remote sensing monitoring methods do not have corresponding spectral segments in coral reef research, so we are considering using the multi-source data fusion method for inversion research. With the close combination of ecology and remote sensing, the application of remote sensing monitoring of coral reefs can be systematically developed to provide information support for global change research.