Obtaining spatial distribution information on mariculture in a low-cost, fast, and efficient manner is crucial for the sustainable development and regulatory planning of coastal zones and mariculture industries. This study, based on the Segment Anything Model (SAM) and high-resolution remote sensing imagery, rapidly extracted mariculture areas in Liaoning Province, a typical northern province in China with significant mariculture activity. Additionally, it explored the actual marine ownership data to investigate the marine use status of Liaoning Province’s mariculture. The total area of mariculture we extracted in Liaoning Province is 1052.89 km². Among this, the area of cage mariculture is 27.1 km², while raft mariculture covers 1025.79 km². Through field investigations, it was determined that in the western part of Liaodong Bay, cage mariculture predominantly involves sea cucumbers. In the southern end of Dalian, the raft mariculture focuses on cultivating kelp. On the other hand, around the islands in the eastern region, the primary crop in raft mariculture is scallops, showing a significant geographical differentiation pattern. In the planned mariculture areas within Liaoning Province’s waters, the proportion of actual development and utilization is 11.2%, while the proportion approved for actual mariculture is 90.2%. This indicates a suspicion that 9.8% of mariculture is possibly in violation of sea occupation rights, which could be due to the untimely updating of marine ownership data. Based on SAM, efficient and accurate extraction of cage mariculture can be achieved. However, the extraction performance for raft mariculture is challenging and remains unsatisfactory. Manual interpretation is still required for satisfactory results in this context.

Multiple datasets related to pond and marine aquaculture have been published using diverse remote sensing technologies, yet a comprehensive dataset detailing spatial distribution on both land and sea sides is lacking. Firstly, a meticulous comparison of datasets which we selected related to aquaculture ponds and marine, ensuring consistency in trends. Subsequently, the datasets published by our team were edited and integrated to illustrate aquaculture activities on both sides of China’s coastal zone. Finally, a spatial differentiation of coastal aquaculture in major provinces was analyzed. This analysis also utilizes the types of coastline and statistical data, guiding coordinated resource management efforts. The results unveil a distinctive spatial distribution pattern, concentrating aquaculture in the northern regions—Bohai Sea, Jiangsu, Fujian, and Pearl River coasts in Guangdong. The provinces rich in aquaculture resources, such as Shandong, Guangdong, and Liaoning, exhibit extensive coastlines. However, remote sensing monitoring suggests an underestimation of Liaoning’s marine aquaculture compared to statistical yearbook data. Furthermore, southern provinces like Guangdong and Fujian exhibit significantly higher aquaculture output than Liaoning. Zhejiang leads in fishing output. The paper outlines the future development direction of coastal aquaculture, emphasizing a strategic, integrated land–sea approach for sustainable development.

A fluorescence probe is designed to serve as a novel tool for the clinical diagnosis of hepatocellular carcinoma with 4-piperidinopiperidine as a donor that shows improved brightness and water solubility.

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points.

From 2000 to 2020, storm surges occurred 397 times in China, resulting in direct economic losses of up to CNY 220.64 billion. Storm surges not only threaten safety but also cause property damage; hence, it is necessary to assess the changes in vulnerability to storm surges in order to understand how to reduce said vulnerability. Fifteen coastal cities of four types were chosen, with different levels of urban development, rapid expansion of impervious surface, high extent of agricultural land, and high fishery output value. Viewing vulnerability through the dimensions of exposure, sensitivity, and adaptability, a GIS and RS were used to evaluate and assess the vulnerability in 15 coastal cities in China over the past 30 years. The results indicated that the vulnerability of these 15 Chinese cities presented the characteristics of a continuous downward trend from 1990 to 2020, and the average rate of reduction in vulnerability over the 10 years from 2005 to 2015 was the highest, at 2.23%. The areas of high vulnerability shifted from the southern region to the northern region. The vulnerabilities in the southern region, with Shanghai, Shenzhen, and Dongguan, changed significantly, to 3.30%, 3.20%, and 3.45%, respectively. An important factor in determining vulnerability reductions is a city’s ability to adapt to storm surges. Coastal cities can improve their adaptability to storm surge disasters through general public budget expenditure, investment in fixed assets, GDP, and medical and health services, thereby alleviating their vulnerability. Due to China’s frequent storm surge disasters during the 2005–2015 period, government departments have strengthened the investment of relevant resources in adaptive indicators, ultimately causing the cities’ vulnerability to rapidly decrease during this period.

China is one of the countries that suffers severe damage from storm surges. Assessing the vulnerability to storm surges holds great significance for promoting sustainable development and minimizing disaster losses in coastal areas. This study first developed a vulnerability index by integrating 15 indicators from three components (exposure, sensitivity, and adaptability) that provide a comprehensive portrayal of the multidimensional structure of vulnerability. Subsequently, the vulnerability of Chinese coastal areas was comprehensively evaluated from the perspective of prefecture-level cities using a weight combination strategy. Furthermore, spatial statistical techniques were utilized to analyze the spatial heterogeneity of vulnerability. The results show that 64% of coastal cities are classified as being in the very high and high vulnerability categories, with Zhanjiang, Lingao, Dalian, Yancheng, and Shanwei exhibiting the highest vulnerability levels. Among the provinces, Guangxi and Hainan Provinces demonstrate the highest vulnerability, with more than 90% of their coastal cities facing high vulnerability. Additionally, the vulnerability of Chinese coastal cities exhibits significant spatial heterogeneity. Specifically, coastal cities located in the Yangtze River Delta and the Pearl River Delta regions are identified as low–low (LL) vulnerability clusters, whereas high–high (HH) vulnerability clusters are observed in coastal cities within the Beibu Gulf region. These results provide valuable insights for the formulation of disaster reduction policies at the provincial level and the focus for action at the local level.