The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for Ulva pertusa and Sargassum thunbergii species (Ulva pertusa, $R_{Sentinel2}^2$ = 0.74, $R_{PlanetScope}^2$ = 0.8; Sargassum thunbergii, $R_{Sentinel2}^2$ = 0.88, $R_{PlanetScope}^2$ = 0.69). The average biomasses of Ulva pertusa and Sargassum thunbergii in the intertidal zone of Gouqi Island are 456.84 g/m² and 2606.60 g/m², respectively, and the total resources are 3.5 × 10⁸ g and 1.4 × 10⁹ g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.

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), V_re, A_be, R_g, L_re, L_g, and L_r 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.

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.

This study aimed to investigate the practical validity of the environmental DNA (eDNA) method for evaluating fish composition and diversity in different habitats. We evaluated the fish composition and diversity characteristics of seven different habitats in the Ma’an Archipelago Special Protected Area in April 2020. The results showed that a total of twenty-seven species of fishes belonging to six orders, eighteen families, and twenty-three genera of the Actinopterygii were detected in the marine waters of the Ma’an Archipelago Special Protected Area. The dominant species in each habitat were Larimichthys crocea, Paralichthys olivaceus, and Lateolabrax maculatus. The mussel culture area had the highest number of species, with 19 fish species, while the offshore bulk load shedding platform had the lowest number of species, with 12 fish species. The rest of the habitat was not significantly different. The results showed that the mussel culture area had the highest diversity index (average value of 2.352 ± 0.161), and the offshore bulk load shedding platform had the lowest diversity index (average value of 1.865 ± 0.127); the rest of the habitat diversity indices did not differ significantly. A comparison with historical surveys showed that the eDNA technique can detect species not collected by traditional methods such as gillnets and trawls. Our study demonstrates the role of eDNA technology in obtaining fish diversity in different habitats and provides a theoretical basis for the continuous monitoring and management of fish biodiversity in protected areas.

Seaweed biomass is mainly investigated using quadrat sampling. However, the spatial limitations of quadrat sampling pose significant challenges to the thorough evaluation of seaweed resources at a larger scale. We combined multispectral photography collected using an unmanned aerial vehicle with satellite imagery and in situ hyperspectral reflectance measurement to determine the biomass of Ulva pertusa and Sargassum spp. in the intertidal zone of Gouqi Island, Zhejiang Province, China. The statistical metrics derived from aerial imagery for the biomass estimation of Ulva pertusa and Sargassum spp. achieved coefficients of determination (R2) of 0.81 and 0.74, root mean square errors (RMSE) of 96.54 and 788.64 g/m2, and mean absolute errors (MAE) of 40.71 and 327.12 g/m2, respectively. For satellite imagery, the R2 values were 0.80 and 0.68, RMSE were 97.65 and 910.45 g/m2, and MAE were 71.78 and 719.69 g/m2, respectively. The biomass of U. pertusa and Sargassum spp. fluctuated monthly, reaching their highest values in June and July, respectively. The total biomass of U. pertusa and Sargassum spp. was 1.69×107 and 9.93×107 g, and the organic carbon content was 7.98×105 and 5.04×106 g, respectively. Our research offers a new perspective and methodology, supplying critical data and a scientific rationale for the management of marine assets and the safeguarding of coastal ecologies.