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.

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.

The Above Ground Biomass (AGB) of seaweeds is the most fundamental ecological parameter as the material and energy basis of intertidal ecosystems. Therefore, there is a need to develop an efficient survey method that has less impact on the environment. With the advent of technology and the availability of popular filming devices such as smartphones and cameras, intertidal seaweed wet biomass can be surveyed by remote sensing using popular RGB imaging sensors. In this paper, 143 in situ sites of seaweed in the intertidal zone of GouQi Island, ShengSi County, Zhejiang Province, were sampled and biomass inversions were performed. The hyperspectral data of seaweed at different growth stages were analyzed, and it was found that the variation range was small (visible light range < 0.1). Through Principal Component Analysis (PCA), Most of the variance is explained in the first principal component, and the load allocated to the three kinds of seaweed is more than 90%. Through Pearson correlation analysis, 24 parameters of spectral features, 9 parameters of texture features (27 in total for the three RGB bands) and parameters of combined spectral and texture features of the images were selected for screening, and regression prediction was performed using two methods: Random Forest (RF), and Gradient Boosted Decision Tree (GBDT), combined with Pearson correlation coefficients. Compared with the other two models, GBDT has better fitting accuracy in the inversion of seaweed biomass, and the highest R² was obtained when the top 17, 17 and 11 parameters with strong correlation were selected for the regression prediction by Pearson’s correlation coefficient for Ulva australis, Sargassum thunbergii, and Sargassum fusiforme, and the R² for Ulva australis was 0.784, RMSE 156.129, MAE 50.691 and MAPE 28.201, the R² for Sargassum thunbergii was 0.854, RMSE 790.487, MAE 327.108 and MAPE 19.039, and the R² for Sargassum fusiforme was 0.808, RMSE 445.067 and MAPE 28.822. MAE was 180.172 and MAPE was 28.822. The study combines in situ survey with machine learning methods, which has the advantages of being popular, efficient and environmentally friendly, and can provide technical support for intertidal seaweed surveys.

To investigate the ecological impacts of macroalgae in the framework of shifting global CO₂ concentrations, we conducted a study utilizing Ulva fasciata and Sargassum horneri specimens sourced from the Ma’an Archipelago in Zhejiang Province on how ocean acidification (OA) and temperature changes interact to affect the photosynthetic physiological responses of macroalgae. The results of the study showed that OA reduced the tolerance of U. fasciata to bright light at 20 °C, resulting in more pronounced photoinhibition, while 15 °C caused significant inhibition of U. fasciata, reducing its growth and photosynthetic activity, but OA alleviated the inhibition and promoted the growth of the alga to a certain extent. The tolerance of S. horneri to bright light was also reduced at 20 °C; the inhibition was relieved at 15 °C, and the OA further improved the algal growth. The Relative Growth Rate (RGR), photosynthetic pigment content, and the release of the dissolved organic carbon (DOC) of U. fasciata were mainly affected by the change in temperature; the growth of the alga and the synthesis of metabolites were more favored by 20 °C. A similar temperature dependence was observed for S. horneri, with faster growth and high metabolism at 15 °C. Our results suggest that OA reduces the tolerance of macroalgae to high light at suitable growth temperatures; however, at unsuitable growth temperatures, OA effectively mitigates this inhibitory effect and promotes algal growth.