The automatic classification of tree species using terrestrial laser scanning (TLS) point clouds is key in forestry research. This study aims to develop a robust framework for tree species classification by integrating advanced feature extraction and machine learning techniques. Such a framework is of great significance for investigating and monitoring forest resources, sustainable forest management, and biodiversity research. To achieve this, point cloud data from 360 trees of four species were collected at the Northeastern Forestry University in Harbin City, Heilongjiang Province. Three types of tree point cloud features were extracted: tree structure, bark texture, and bark color. In addition, to repair and optimize the bark point cloud data, improved bark texture features were generated using the kriging interpolation method. These four features were combined into seven classification schemes and input into a random forest classifier, which was used to accurately classify the tree species. The results showed that the classification scheme combining tree structure features, improved bark texture features, and bark color features performed the best, with an overall classification accuracy of 94.17% and a kappa coefficient of 0.92. This study highlights the effectiveness of integrating point cloud data with machine learning algorithms for tree species classification and proposes a feature extraction and classification framework that significantly enhances classification accuracy.

Coastal development such as sea reclamation, port terminals, and breakwater construction has significantly altered the southwestern coastline of Laizhou Bay, changing the regional hydrodynamic environment. To explore how tidal range and tidal prism respond to these 20-year coastline changes, this paper selected the southwestern coastline data of Laizhou Bay in 2000 and 2020, established a 2-D tidal model, and studied the impact of the 20-year coastline changes on tidal range and tidal prism in this sea area. The results show that during the 20 years from 2000 to 2020, most of the bay area’s coastline was in a growth trend of advancing toward the sea, 85.3% of the cross-sections were growth areas, 11.9% were dynamic stable areas, and there was almost no erosion area. Affected by reclamation activities, the area of the southwestern part of Laizhou Bay decreased by 11.66%, the coastline increased by 63.27%, and the center of mass moved 2.22 km to the northeast. The reclamation of tidal flats caused the potential energy of tidal waves in the bay to increase, the maximum possible tidal wave in the southwestern part of the bay showed an increasing trend, and the bay top between Weifang Port and Guangli Port increased particularly significantly, with a maximum increase of 22 cm. The spring tide period, neap tide period and average tidal prism in the southwestern bay decreased by 4.79%, 3.29%, and 4.24%, respectively. The reclamation of tidal flats is the main reason for the decrease in tidal prism in the southwestern part of the bay.

Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R² = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management.