UAV remote sensing inversion is an efficient and accurate method for obtaining information on vegetation coverage, biomass and other parameters. It is widely used on forest, grassland and other terrestrial vegetation. However, it is rarely used on aquatic vegetation, especially in intertidal zones and other complex environments. Additionally, it is mainly used for inversion of coverage, and there have been few studies thus far on biomass assessment. In this paper, we applied multispectral UAV aerial photography data to evaluate the biomass of seaweed in an intertidal zone. During the ebb tide, UAV aerial photography and in situ sampling data were collected in the study area. After optimizing the spectral index and performing a multiple linearity test, the spectral parameters were selected as the input of the evaluation model. Combined with two machine learning algorithms, namely random forest (RF) and gradient boosting decision tree (GBDT), the biomasses of three species of seaweed (Ulva pertusa, Sargassum thunbergii and Sargassum fusiforme) in the intertidal zone were assessed. In addition, the input parameters of the machine learning algorithms were optimized by one-way ANOVA and Pearson’s correlation analysis. We propose a method to assess the biomass of intertidal seaweed based on multispectral UAV data combined with statistics and machine learning. The results show that the two machine learning algorithms have different accuracies in terms of biomass evaluation using multispectral images; the gradient boosting decision tree can evaluate the biomass of seaweed in the intertidal zone more accurately.

Seaweed plays an important role in energy production in marine, coastal, and island ecosystems. The protection of seaweed beds is a key point for coastal ecosystem health, but the community characteristics, dominant species, and distribution of seaweed beds in the coastal waters of China are still unknown. Dividing seaweed beds based on their ecological function is also required for coastal ecosystem conservation, marine development, and utilization. We conducted ecological surveys on various types of ecosystems at approximately 50 sites dedicated to the conservation of seaweed bed biodiversity in China from 2018 to 2019. These seaweed beds were classified into different flora by water temperature and the attributes of the dominant species. The study showed that Sargassum dominated the coast of China. The coverage of the genus Undaria and the genus Laminaria in the coastal waters of Liaoning and Shandong was high and gradually decreased from Zhejiang to the south. The mean biomass of the seaweed beds along the coast of China was 7.29 kg/m2, and the mean coverage was 41.25%. The height and fresh weight of the dominant species gradually decreased with the decreasing latitude. The seaweed beds were distributed from the shallow water zone to the profundal zone along the coast from north to south, and the bathymetry of seaweed beds in Hainan was below 6 m. Based on the water temperature, the attributes of the seaweed beds, the temperature attributes of the dominant species, and the seaweed’s distribution, the seaweed beds in China can be specifically divided into temperate warm water types, subtropical warm water types, and tropical warm water types. This study is relevant to the development of regulations and directives to ensure biodiversity conservation and environmental sustainability.

Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), Vre, Abe, Rg, Lre, Lg, and Lr and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.

This study was undertaken in order to explore the practical effectiveness of the environmental DNA (eDNA) metabarcoding approach in evaluating fish composition and diversity in a high heterogeneous rocky reef habitat. We assessed the fish composition and diversity characteristics of the rocky reef habitat at Dachen Islands, Taizhou and the Zhejiang Province in China in November 2020 by comparing two methods: multi-mesh gillnets and eDNA. A comparative analysis was carried out on the fish composition and diversity characteristics gained under the two methods by using taxonomy, ecotypes and diversity indices. The results showed that there were 28 species of fish collected through gillnets, distributed under 24 genera, 19 families, 6 orders and one class. Among them, 4, 18, and 6 species of near-surface, near groundfish and groundfish were found, respectively, with Thryssa mystax, Johnius belangerii, and Sebastiscus marmoratus being the dominant species in each water layer. A total of 81 species of fish detected by eDNA metabarcoding belonging to 67 genera, 46 families, 15 orders and 2 classes. The near-surface, near groundfish and groundfish species were 17, 42, and 22, with Thryssa vitrirostris, Benthosema pterotum, Harpadon nehereus, and Dasyatis akajei being the dominant species in each water layer. Twenty species (71.4%) and 41 species (50.6%) of reef fish were counted by gillnets and eDNA, respectively. The results showed that multi-mesh gillnets can accurately obtain information on fish composition in rocky reef habitats, but with some selectivity. The eDNA technology can detect species not collected by gillnets, but the number of species detected in areas with fast water velocity is significantly less than other eDNA stations where the water velocity is slow. In summary, the combination of traditional nets and eDNA will provide more information on taxonomic diversity and population biomass, transforming natural resource management and ecological studies of fish communities on a larger spatial and temporal scale.

Seaweed communities perform a variety of ecological services, including primary productivity supply, biological habitat construction, water purification, and acting as marine carbon sinks. The abundance of seaweed is the basis for the assessment of ecological services in communities. The Ma’an Archipelago, adjacent to the Yangtze River estuary in China, is an important and typical island group. In this study, the abundance of seaweed in the typical coastal islands of the Ma’an Archipelago, Zhejiang Province, was evaluated by means of sonar detection and scuba diving sampling methods. The organic carbon content of six dominant seaweed species was measured to estimate the carbon sequestration capacity of the dominant species in the Ma’an Archipelago. The results show that 27 species of Rhodophyta, 10 species of Ochrophyta, and two species of Chlorophyta were found in the Ma’an Archipelago. Seaweed was distributed in the coastal areas of the islands, with a distribution width of 2–60 m. Gouqi Island had the longest shoreline, and there, the distribution depth of the seaweed reached 15 m and the area of the seaweed community was the largest. The slope of the rocks in the Sanheng survey area was large and the width of the seaweed community was small. The distribution area of seaweed in the Ma’an Archipelago was 6.51–13.43 km2 and the organic carbon content of the seaweed was 33.16 ± 3.26%. The biomass of Ochrophyta in the Ma’an Archipelago was the largest, followed by Chlorophyta and Rhodophyta. Among the six dominant species, the carbon sequestration of Sargassum thunbergii was the largest, at 277.91–848.74 t per year, and that of Undaria pinnatifida was the smallest. This study provides scientific guidance for the assessment of the primary productivity supply, carbon sink, and conservation capacity of seaweeds in China.