Coastal Ecological Environment Research Articles

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area, which is also of great significance for understanding coastal ecological environment changes and even global changes. Based on field measurements, combined with Linear Regression (LR) model and Inverse Distance Weighing (IDW) method, this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay. The shoreline faces a high erosion chance under the action of natural factors, while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities. The implication of change rule for ecological protection and recovery is also discussed. Measures should be taken to protect the coastal ecological environment. The models used in this paper show a high correlation coefficient between observed and modeling data, which means that this method can be used to predict the changing trend of shoreline and tidal flat. The research results of present study can provide scientific supports for future coastal protection and management.©2024 China Geology Editorial Office.

Biogenic element driving mechanism in the occurrence of Chinese coastal eco-environmental disasters and regulation for ecological environment health.

In recent decades, China's coastal ecological environment has undergone significant changes under multiple pressures such as climate change and high intensity of human activities. The occurrence and scale of harmful algal blooms represented by the red tide and the green tide are increasing rapidly. Moreover, environmental problems such as hypoxia and acidification in seawater have become increasingly prominent. It is urgent to find out the occurrence mechanism and prevention measures of marine eco-environmental disasters. Therefore, we explained the connotation of the concept of "marine eco-environmental disaster" for the first time, and systematically interpreted the biogenic element driving mechanisms in the occurrence of eco-environmental disasters in China nearshore area in the aspects of exogenous input of nutrients, the mineralization and decomposition of marine organic matters and nutrient regeneration, as well as the abnormal nutrient structure in offshore waters. We pointed out that the drastic increases of terrigenous nutrient discharge caused by enhancing human activities, together with the complex biogeochemical cycle process after biogenic elements entering the sea, led to the frequent occurrence of coastal eco-environmental disasters. On this basis, combined with the latest research progress in this field, we put forward the regulatory schemes for coastal ecological environment health based on the controlling of seawater biogenic element. That is, based on land-sea integration strategy, the reduction of land-source pollutants, especially inorganic nitrogen from land, coastal zone and into the sea, and the normalization of nutrient structure in coastal seawater through artificial regulation technology, are the key points to reduce and control the occurrence of disasters and improve the quality of the coastal ecosystems.

Hydrochemical Characteristics and Formation Mechanism of Groundwater in the Western Region of Hepu Basin, Beihai City

Beihai City is a typical coastal city where groundwater provides a strong support for social and economic development. Studies on the hydrochemical characteristics and formation mechanism in Beihai City play an important role in the scientific management of water resources and coastal ecological environment protection. In this study, we revealed the main hydrogeochemical processes controlling groundwater quality by means of groundwater survey and water sample collection in the western region of Hepu Basin, Beihai City, combined with hydrochemistry and isotope theories and methods. The results showed that groundwater had the remarkable features of low pH value and low mineralization degree. For pore water, hydrochemistry type by primarily NO3 type water and concentrations of Na+ and Cl- were modestly increased along the flow path. Ca-HCO3, Ca-Cl·HCO3, Ca·Na-HCO3, and Na-Cl·HCO3 types were predominant in fissure water. The groundwater was of meteoric origin, hydrogeochemical evolutions were mainly affected by water-rock interactions, cation exchange, and anthropogenic activities. Na+, K+, and Cl- were mainly derived from evaporite and silicate rocks; Ca2+, Mg2+, HCO3-, and SO42- were from carbonatite and evaporite; and NO3- principally arose from anthropogenic activities. This study suggests that the groundwater pollution prevention and control should be carried out as soon as possible in the area where the NO3 type water occurs to avoid the further deterioration of water quality.

MangroveSeg: Deep-Supervision-Guided Feature Aggregation Network for Mangrove Detection and Segmentation in Satellite Images

Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images. Although many deep-learning-based mangrove detection and segmentation algorithms have made notable progress, the complex regional structures and the great similarity between mangroves and the surrounding environment, as well as the diversity of mangroves, render the task still challenging. To cover these issues, we propose a novel deep-supervision-guided feature aggregation network for mangrove detection and segmentation called MangroveSeg, which is based on a U-shaped structure with ResNet, combining an attention mechanism and a multi-scale feature extraction framework. We also consider the detection and segmentation of mangroves as camouflage detection problems for the improvement and enhancement of accuracy. To determine more information from extracted feature maps in a hidden layer, a deep supervision model is introduced in up-sampling to enhance feature representation. The spatial attention mechanism with attention gates is utilized to highlight significant regions and suppress task-independent feature responses. The feature fusion module can obtain multi-scale information by binding each layer to the underlying information and update feature mappings. We validated our framework for mangrove detection and segmentation using a satellite image dataset, which includes 4000 images comprising 256 × 256 pixels; we used 3002 for training and 998 for testing. The satellite images dataset was obtained from the Dongzhaigang National Nature Reserve located in Haikou City, Hainan Province, China. The proposed method achieved a 89.58% overall accuracy, 89.02% precision, and 80.7% mIoU. We also used the trained MangroveSeg model to detect mangroves on satellite images from other regions. We evaluated the statistical square measure of some mangrove areas and found that the evaluation accuracy can reach 96% using MangroveSeg. The proposed MangroveSeg model can automatically and accurately detect the distribution and area of mangroves from satellite images, which provides a method for monitoring the ecological environment.

Exploring the effect of Ulva prolifera decay on the immune tissue of Paralichthys olivaceus based on transcriptomics and histopathological analysis

For 17 consecutive years, the outbreak of Ulva prolifera in the South Yellow Sea area of China has caused significant negative impacts on coastal ecological environment. However, its specific influence on fish immunity is rare. In this study, the juvenile Paralichthys olivaceus was exposed to fresh U. prolifera algae (FU) and decomposing algal effluent (DU). After short-term stress for 14 days, the histopathological and transcriptome analysis were performed to study the effect of U. prolifera decay on P. olivaceus. Histopathological analysis found that the liver, spleen and head kidneys of P. olivaceus were damaged after the short-term stress. The transcriptome results showed that the steroid biosynthesis signaling pathway and the PI3K-Akt signaling pathway were significantly enriched. Some immune related genes, including c1qc-like, dusp1, dusp16, HSP90 and metabolic related genes serotransferrin, were differentially expressed. These results highlighted the harmfulness of U. prolifera on marine fish, setting a solid foundation for further analyses.

What psychological factors lead to the abandonment of cultivated land by coastal farmers? An interpretation based on the psychological distance

For a long time, the research on the cultivated land abandonment behavior of farmers, caused by natural constraints such as coastal ecological environment vulnerability and frequent extreme climates, has received extensive attention. However, few scholars pay attention to the impact of psychological construction of farmers’ environmental risk perception on abandoned farming behavior, especially the use of psychological distance (PD) to quantify this abstract psychological process. Taking Shankou Town, Guangxi, China as an example, this study combines the PD in the field of sociology with the construal level theory to build a PD framework for coastal farmers. A structural equation model was used to explore the influence mechanism of the social, temporal, hypothetical, and spatial distances on farmers’ cultivated land abandonment behavior. We found that coastal land abandonment is affected by social trust and the probability of extreme weather, although the risk preference under the ‘discount rate’ is not significantly affected. At the same time, the closer the spatial distance between the residence and the coastline, the higher the abandonment of cultivated land, although with a different impact. In addition, population mobility and human-sea connection were found to play a mediating role in the effect of psychological distance on cultivated land abandonment. We confirmed that the direction of the effects of the four types of psychological distance on land abandonment behavior is not consistent. This study is helpful to understand the cultivated land abandonment behavior of farmers in coastal zone, and tries to provide reference for coastal zone planting planning.

Ecological Vulnerability and Ecological Risk Assessment of Shanghai Section of Hangzhou Bay North Shore

Vulnerability research and ecological risk assessment of coastal ecological environment are of great significance for the temporal and spatial evolution process and trend of coastal zone. In this paper, using the 4-phase GF-1 remote sensing data, the landscape fragility index and landscape artificial disturbance intensity index of the Shanghai section of the north bank of Hangzhou Bay are calculated, and the temporal and spatial variation characteristics and the response to human activities are analyzed. The prediction based on Kalman filtering is used. The area of the landscape vulnerability level of the Shanghai section of the north shore of Hangzhou Bay in 2022. The results show that: (1) The lower fragile area is the dominant type of vulnerable area between 2013 and 2016, and the low-vulnerable area rises to the dominant type in 2016-2018, and the area of higher and high vulnerable areas is on the rise, and the vulnerable areas in 2018-2019 The change is slower than the previous stage. High-level vulnerable areas continue to spread, mainly from urban to suburban, inland to coastal, land to port. (2) The landscape change in the study area has a significant response to human activities, and there is a significant positive correlation between landscape fragility and landscape artificial disturbance intensity. (3) Based on the Kalman filter method, the results of predicting the level of vulnerable areas in each landscape in 2022 show that the area of the low vulnerability level area and the higher vulnerability level area has increased from the previous moment, and the lower vulnerability level area and The area of the medium vulnerability level has increased from the previous moment, and the area of the high vulnerability level has increased from 2018, which is lower than that in 2019.

Analysis of the evolution and driving forces of tidal wetlands at the estuary of the Yellow River and Laizhou Bay based on remote sensing data cube

The rising of the “claiming land from tidal wetlands and sea” has gradually reduced tidal wetlands, resulting in a huge damage to the coastal ecological environment. It is of great significance to monitor tidal wetlands by remote sensing technology. However, due to the influence of tides and weather, monitoring tidal wetlands by remote sensing technology is often inaccurate. In this study, using dense time series images data cube constructed by the spatio-temporal data fusion algorithm, we monitored tidal wetlands at the estuary of the Yellow River and Laizhou Bay; and we analyzed their evolution and driving forces in detail. This study revealed several findings. Firstly, the area of tidal wetlands had shown a decreasing trend; the tidal wetland at the new estuary of the Yellow River had extended to the sea from 1976 to 2021; other tidal wetlands had shrunk year by year. Secondly, a more important finding was that sedimentation, erosion and reclamation had led to the changes of the tidal wetlands; and there were no obvious changing trends of the tidal wetlands' area caused by these three factors in each time period. Finally, we confirmed that salt field driving force, salt field and harbor driving force, aquaculture pond driving force and the erosion of natural factor driving force had reduced tidal wetlands; while the sedimentation of the natural factor driving force had made the tidal wetlands increase; and the newly added area of all driving forces had had no obvious regular changing trends. Our proposing to monitor tidal wetlands by using dense time series images data cube provides a general approach for tidal wetlands’ study and our results demonstrate the need for better coastal management of the estuary of the Yellow River and Laizhou Bay.

Activated abscisic acid pathway and C4 pathway, inhibited cell cycle progression, responses of Ulva prolifera to short term high temperature elucidated by multi-omics

The annual outbreak of green tides since 2007 has destroyed coastal waters' ecological environment and caused substantial economic losses. Ulva prolifera, known as the dominant species of green tides, is influenced by temperatures. Omics-based technology was used to analyze U. prolifera under 12 h of treatment at 30 °C in the work. High temperature has the following advantages, e.g., activating the abscisic acid signaling pathway, improving the heat tolerance of U. prolifera, up-regulating metabolites such as glycolipids, glyceroyl, and glutamic acid to maintain the stability and fluidity of cells, and reducing the stimulatory effect of external stress on cells. The key genes and proteins of the tricarboxylic acid cycle, glycolysis, and pentose phosphorylation pathways were inhibited; however, the key enzyme pyruvate phospho-dikinase of the C4 pathway was up-regulated. The C4 pathway was activated in U. prolifera in response to high-temperature stress and may play a key role in photosynthesis. Besides, U. prolifera metabolizing amino acids was active. High temperature inhibited genes and proteins related to DNA replication and cell cycle in the transcriptome and proteome as well as the growth and reproduction of U. prolifera.

Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.

Distribution characteristics of green tides and its impact on environment in the Yellow Sea

Large-scale outbreaks of green tides in the Yellow Sea affect the development of local tourism and aquaculture and significantly damage the coastal ecological environment. To study the distribution characteristics of green tides and to explore their impact on the environment, a coupled physical-ecological model (LTRANS-GT) based on the Lagrangian TRANSport model (LTRANS) was constructed in this paper to simulate the growth and dissipation process of Ulva prolifera and to obtain its drift trajectory and biomass. The results show that the tracks of the green tide are mainly divided into three categories, namely, northwestward, northward, and eastward. The green tide biomass showed a single-peak with seasonal variation in most years (entering a rapid proliferation period in May–June, reaching a peak biomass after developing for approximately 30 days, then dying out rapidly and basically disappearing by August), and showed a double-peak only in a few years due to extreme weather effects. In 2017, the biomass of U. prolifera was the lowest, with a maximum wet weight of only 24600 tons, while the largest biomass occurred in 2013, with a maximum wet weight of more than 560,000 tons. The interannual difference in the biomass of U. prolifera was mainly due to its initial biomass and the difference in nutrient concentration in the area where it was located. The year in which U. prolifera absorbed the most dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) was 2013, with absorption values of 3973 tons and 114 tons, respectively; and the year in which U. prolifera absorbed the least DIN and DIP was 2017, with absorption values of 172 tons and 5 tons, respectively. During the period of U. prolifera outbreak, the consumption of DIN and DIP in the sea area where it occurred accounted for approximately 43.65% and 0.6% of the total discharge of the rivers, and 26.86% and 6.1% of the atmospheric deposition, respectively. The impact of green tide outbreaks on the annual nitrogen and phosphorus nutrient budget of the entire Yellow Sea was relatively small, but the impact on the area where U. prolifera grows and decays can not be ignored. In dissipation period, the decay of U. prolifera may make DON and DOP double near Shandong coast.

The response of Chudao’s beach to typhoon “Lekima” (No. 1909)

Abstract The response of beaches to typhoons has always been a hot topic at home and abroad. The study of beach changes during typhoons is helpful to deepen the understanding of beach evolution and is important for the coastal ecological environment. Based on the observation results of ten profiles and the sediment samples in Chudao before and after the typhoon Lekima, this article explored the response characteristics of the beach to the typhoon. The results showed that the study area was located on the right side of the typhoon’s forward path, and the double superposition of onshore waves and storm surge resulted in erosion along the beach. In order to alleviate the energy brought by storm waves, the beach was transformed into a more dissipated state. The mean particle size of the beach sediments was coarse on the whole, with poor sorting ability, and that in the wash zone was particularly obvious. The results showed that different profiles responded to Lekima in different ways: while the profiles of N01–N05 in the southwest changed little, those of N06–N10 in the northeast changed from the type of beach shoulder to the type of sandbank as a result of morphological differences in different profiles.

Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations

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.

Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery

Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (Rrs). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis.

Increasing intrusion of high salinity water alters the mariculture activities in Zhanjiang Bay during the past two decades identified by dual water isotopes

The decrease of river runoff caused by the intensified human activities (e.g. artificial dams) and increasing intrusion of high salinity water in the coastal bays have become a worldwide environmental problem. However, the mixing can hardly be identified by traditional method with temperature and salinity due to the complicated water sources in the coastal area. Thus, it is difficult to quantify the impact of intrusion of high salinity water on coastal ecological environment. Here, seasonal dual water isotopes (δD and δ18O), hydrographic parameters, and nutrients were investigated in a typical semi-enclosed mariculture bay in South China Sea (SCS), to quantify the intrusion of high salinity water and its impact on the water environment. The results showed that salinity in the bay has increased significantly (18%) over the past two decades due to the decrease of runoff and dredging activity. Zhanjiang Bay is mainly affected by the seawater from the SCS in outer bay, and the seawater from the outer bay (89%) was significantly higher than that of freshwater (7%) in summer, despite the increase in freshwater input from the river during this period. In winter, the intrusion of high salinity water increased (accounting for 94%) due to the decrease of runoff input. However, the contribution of groundwater was similar in summer (4%) and winter (5%). The estimation results from the relationship of δ18O-salinity and δD-salinity showed that the intrusion of high salinity water has increased significantly for the past two decades (increased by 23%). This resulted in the area suitable for oyster breeding is decreasing, and the oyster breeding activities have been gradually moving to the inner bay. Moreover, the nutrients in Zhanjiang Bay were mainly originated from freshwater input in summer (54%–90%), while it changed to the SCS input from the outer bay in winter (40%–97%). This study suggests that the intrusion of high salinity water significantly increases the salinity, and seriously retains the pollutants of freshwater in the bay, which poses a great threat to the oyster breeding activities in the semi-enclosed bay.

Analysis of the underlying mechanisms of green tide with a perspective of algae ecophysiology

Green tide in the sea is an abnormal ecological phenomenon caused by the explosive proliferation or high aggregation of some green macroalgae under particular environmental conditions. Since 2007, green tides have occurred in the Yellow Sea for 15 consecutive years, resulting in extremely serious damage to coastal ecological environment, social development, and economic construction. Therefore, it is urgent to solve the green tide problem, which is driven by external and internal factors. Global researchers have scientifically recognized the external causes (environmental factors) of the green tide blooms, and have carried out a series of studies on the physiological response of green tide algae to some environmental factors and obtained some achievements. However, the internal causes of green tide blooms, which is the intense ecophysiological adaptability of green tide algae in response to drastic fluctuation of environmental factors, has not yet been sufficiently addressed. From the perspective of algae ecophysiology, we reviewed the response mechanisms of green tide forming species to the fluctuations of various ecological factors, including light intensity, carbon dioxide, temperature, salinity, desiccation, nutrient, heavy metals, and biotic factors. Moreover, we summarized the adaptive regulation mechanisms of green tide algae dealing with fluctuating environmental factors from the aspects of photosynthetic and growth physiology, nutritional physio-logy, and reproductive physiology, which help reveal the internal mechanisms of green tide blooming.

Long-Term Benefits of Coastline Ecological Restoration in China

Coastline ecological restoration is a critical approach for improving the coastal ecological environment and natural disaster mitigation capacity. Zhejiang Province, with the longest coastline in China, has carried out a three-year action of coastline remediation and restoration, expecting to complete the 300-km coastline restoration by 2020 as part of China’s coastline restoration project. We developed a cost–benefit framework synthesizing the ecosystem services (ES) analysis pattern and input-output approach to evaluate Zhejiang’s coastline ecological restoration projects. The results showed that the average value of ecosystem services (VES) of coastline restoration in Zhejiang was approximately 11,829 yuan/m, which was lower than the average VES of natural coastline. Obviously, damaged natural coastline could cause VES degradation, which cannot make up for the loss in a short time. In an optimistic scenario, coastline ecological restoration projects would deliver enormous well-being to Zhejiang and even China. Our results suggested that making decisions should be based on the net value after accounting for costs (NES), because ignoring costs might mislead planners to overstate the perceived benefits. The study provided a realistic data basis and method for coastline restoration projects in terms of the design, implementation and effectiveness evaluation to achieve sustainable development.

Coastal Ecological Environment Monitoring and Protection System Based on Multisource Information Fusion Decision

With the problem of nuclear leakage being concerned by more and more industries, the research of coastal ecological environment monitoring has become more and more important. Therefore, it is necessary to study the current unsystematic coastal ecological environment monitoring and protection system. Aiming at the accuracy of feature fusion and representation of single short environment information, this paper compares the classification effects of the three fusion methods on four classifiers: logistic regression, SVM, random forest, and naive Bayes, to verify the effectiveness of LDA and DS model fusion and determine the consistency vector representation method of short environment information data. This paper collects and analyzes the coastal data in recent years using multisource information fusion decision-making. In this paper, DS (Dempster Shafer) evidence algorithm is used to collect the data of coastal salinization degree and air relative humidity, and then, the DS feature matching model is introduced to fuse the whole index system. The method in the article completes the standardized and standardized processing of monitoring data digital conversion, quality control, and data classification, forms interrelated four-dimensional spatiotemporal data, and establishes a distributed, object-oriented, Internet-oriented dynamic management real-time and delayed database. Finally, this paper carries out tree decision processing on the coastal ecological environment monitoring data of multisource information fusion, to achieve the extraction and intuitive analysis of special data, and puts forward targeted protection strategies for the coastal ecological environment according to the data results of the DS algorithm. The research shows that the number of indicators in multisource information fusion in this paper is 16, a total of 3251 data, 2866 meaningful information, and 1869 data including ecological cycle. These data are the results of the collection of multi-information data. Based on the multilevel nature of the existing marine environment three-dimensional monitoring system, the study established a comprehensive resource-guaranteed framework and divided it into four levels according to the level of the marine monitoring system: country, sea area, locality, and data access point. In specific analysis, the guarantee resources involved in each level are introduced. On the basis of in-depth analysis of the requirements of the marine environment three-dimensional monitoring system operation guarantee and the guarantee resource structure, the marine environment three-dimensional monitoring operation comprehensive guarantee system is described from the internal structure and the external connection. The DS algorithm extracts the status information resources of various marine environment three-dimensional monitoring systems, through the interaction of various subsystems, realizes the operation and maintenance of the monitoring system, and provides various technical supports such as system evaluation and failure analysis. After multisource information fusion and decision-making, it is obtained that the index equilibrium module in the DS algorithm in this paper is 0.52, the sensitivity is 0.68, and the independence is 0.42. Among them, the range of sensitivity is the largest. In the simulation results, the eco-economic coefficient can be increased from 12% to 36%. Therefore, using the method of multisource information fusion for quantitative index analysis can provide data support for coastal ecological environment detection, to establish a more perfect protection system.

Review on unmanned aerial vehicle remote sensing and its application in coastal ecological environment monitoring

Unmanned aerial vehicles (UAV) and UAV-based remote sensors are becoming more technologically advanced and widely used in the ecological environment monitoring. The analyses of UAV remote sensing data can help to improve the input efficiency in ecological environment assessment, which can greatly improve the monitoring efficiency and reduce the impact on environmental pollution. This review introduces the common types of UAV, types of UAV-based remote sensors as well as UAV communication, and their practical application in different coastal ecological environment monitoring scenes: terrestrial pollution source, marine debris, algae disaster, which can provide fresh perspective and thought for producer and researcher to develop the UVA remote sensing application in this field.

Shoreline Change Detection and Forecast along the Yancheng Coast Using a Digital Shoreline Analysis System

Shorelines are the dynamic interfaces of both terrestrial and marine environments and are sensitive to climate and anthropogenic influences. The change in the shoreline plays a critical role in intertidal mudflat resources and coastal ecological environment. This paper extracts shoreline along the coast of Yancheng from 1983 to 2020 using a new procedure. The proposed procedure is based on a combination of object-oriented technology and thresholding the wetness components of the tasseled cap transformation (TCT). Then, we studied the temporal and spatial variation in the shoreline and predicted shore movement using the Digital Shoreline Analysis System (DSAS). The results indicated that we yielded a better extraction of shoreline results using the new approach. The shoreline change analysis for the coast of Yancheng over 40 years indicated that 89% was accretion, 3% was under stable coast and erosion was 8%. The coast showed a significant trend of expanding to the sea. Finally, we forecasted shoreline position for the years 2019 and 2020 based on the historical shoreline position data from 1983 to 2009 and 1983–2010 by using the Kalman filter. The results of this study may help coastal researchers and decision makers to guide the use, management, and planning of coastal zones in further study.