Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

Seafloor habitat mapping plays an important role in marine environment monitoring and marine geological research. Optic and acoustic remote sensing are becoming common survey tools in seafloor habitat mapping. However, a single acoustic or optic technique may have a limited detection range and be more susceptible to the impact of image quality. Additionally, it is challenging to satisfy the requirements for accurate detection since single-source data cannot fully reflect the substrate distribution characteristics. This paper developed a method for detecting coastal seafloor habitats through the fusion of multi-scale optics and acoustics data. First, the original feature set was composed of multispectral satellite data and bathymetric data by multibeam echosounder (MBES) and airborne light detection and ranging (LiDAR) at different scales, which improved the capacity to represent feature information. Then, a ReliefF–mRMR method was implemented to select optimal features with appropriate scales and remove redundant features. Finally, the optimal features were employed in model training and classification of several supervised classifiers to verify the effectiveness of the strategy. The developed method was applied to the Ganquan Island survey in the South China Sea. The results demonstrated that, after integrating multisource data, the accuracies were up to 3.31% and 17.28% higher than those obtained using multispectral data or bathymetric data alone, respectively. ReliefF–mRMR exhibited better performance than other feature selection methods. The average coral coverage in the study area was estimated to range from 70.85% to 80.33%. This research highlights the greater potential of multisource data for precisely detecting seafloor habitats.

The distribution of the Critically Endangered tree Manglietia longipedunculata, of which there are only 11 known wild individuals, is restricted to the Nankunshan Nature Reserve in South China. The species is threatened with extinction because of its small number of individuals and the impediments to its reproduction (a combination of protogyny, a short period of stigma receptivity, and a lack of efficient pollinators). To reduce the risk of extinction we conducted two conservation translocation trials: one to augment the sole extant population, and the other 202 km north of the current range. The latter trial was a conservation introduction in which the goals were to increase the population and to buffer against the effects of climate change. We used emerged and grafted seedlings as translocation materials. We compared the survival, growth, and eco-physiological properties of emerged and grafted seedlings at the two sites. The survival rate and growth were higher for grafted seedlings than for emerged seedlings at both sites. Eco-physiological data indicated that grafted seedlings at both sites were as efficient or more so in light and water usage than wild individuals, whereas emerged seedlings were less efficient. Grafted seedlings attained the flowering stage sooner than emerged seedlings. Our study suggests that grafting can facilitate the augmentation and establishment of new populations of M. longipedunculata and perhaps of new populations of other threatened species facing reproductive difficulties and climate change.