Coastal zones, where land meets ocean, are home to a large portion of the global population and play a crucial role in human survival and development. These regions are shaped by complex geological processes and influenced by both natural and anthropogenic factors, making effective management essential for addressing population growth, environmental degradation, and resource sustainability. However, the inherent complexity of coastal zones complicates their study, and traditional in situ methods are often inefficient. Remote sensing technologies have significantly advanced coastal zone research, with different sensors providing diverse perspectives. These sensors are typically used for classification tasks (e.g., coastline extraction, coastal classification) and retrieval tasks (e.g., aquatic color, wetland monitoring). Recent improvements in resolution and the advent of deep learning have led to notable progress in classification, while platforms like Google Earth Engine (GEE) have enabled the development of high-quality, global-scale products. This paper provides a comprehensive overview of coastal zone interpretation, discussing platforms, sensors, spectral characteristics, and key challenges while proposing potential solutions for future research and management.

Paeonia rockii has been a traditional medicinal material from time immemorial. It is an important plant resource of ethnic medicine and chinese herbal medicine. It has unique effects on heat and cool blood, promoting blood circulation and dispersing blood stasis, analgesia and promoting menstruational flow. It is also a first-class protected plant. With the increasing demand for the bark and roots of P. rockii in China’s medicinal market, the continuous high-intensity and disorderly excavation has led to a sharp decline in wild resources. P. rockii is in imminent danger and needs to be saved and protected urgently. In this study, the distribution records of P. rockii in China, combined with environmental factors, are used to simulate the current potential distribution area of P. rockii through MaxEnt model. The environmental factors affecting the distribution of P. rockii are obtained by using the contribution rate of environmental factors and a jackknife test analysis, and the appropriate value of environmental factors is determined by using the response curve. The results show that the AUC value of test set data is 0.985 and that of training set data is 0.987. The prediction accuracy of the Maxent model is extremely high and the simulation effect is very good. At present, the high suitable areas of P. rockii are mainly distributed in the shape of cake in Southern Ningxia, southern Gansu and southern Shanxi, accounting for 15.58% of the total suitable areas. The most important environmental factor affecting the geographical distribution of P. rockii is the altitude; Under the future climate change scenario, the total suitable area of P. rockii shows a decreasing trend, among which the area of high suitable area shows an obvious decreasing trend, the mass center of high suitable area of P. rockii generally tends to migrate to the northwest, and the migration range is large under the scenario of high concentration emission. The research results can provide scientific data support for P. rockii to to cope with the changes in suitable areas caused by climate change and the establishment of effective protection, clarify the change and heterogeneity of P. rockii in different regions of China in the future, and clarify its ecological needs, which has certain practical significance.

Inland waterway transport is an essential element of integrated transport systems, and the inland waterway freight volume accounts for about 50% of the total waterway freight volume in China. During the navigation, anchoring, and operation of ships, various water pollutants are generated, and the pollution generated by sewage is receiving more and more attraction. To prevent and control pollution from ships, it is important to estimate the amount of sewage and pollutants involved. In this study, the data preparation process is established to generate the Degree of Ship Activity (DSA) data pool after cleaning and thinning the massive original Automatic identification System (AIS) data, and then the data fusion method of a fine GIS grid is established to integrate the DSA data into each grid. The total DSA in the lower reaches of the Yangtze River is 37.14 million h/a. The sewage and pollutant generation inventories for the lower reaches of Yangtze River are estimated and analyzed spatiotemporally. It is estimated that the generations of sewage are 1,768,600 t/a in total. After spatial analysis, it is revealed that the water areas with a relatively large amount of pollutant generation are mainly related to ports distributed along the channel and the DSA density. Finally, based on the spatial distribution characteristics of the estimated inventories, the countermeasures of “zero discharge” for inland ships, the receiving facility system improving, and prevention and control at the river basin level are proposed.

With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosystems is becoming increasingly significant. This study proposes a high-throughput gridding algorithm (H-Grid) based on spatial hashing to rapidly generate ship emission inventories, which overcomes the inefficiency of traditional methods caused by complex index building and maintenance. The H-Grid algorithm achieves a constant processing time per data point and possesses inherent parallelism. Based on the H-Grid algorithm, taking the Yellow Sea area between China and Republic of Korea as a case study, the emissions of atmospheric pollutants from ships in 2024 were calculated, and their spatiotemporal distribution characteristics were analyzed. In our empirical study, the algorithm’s computational efficiency for processing millions of AIS records was improved by over 10 times compared to traditional geometric calculations, and by more than 4 times when compared to mainstream database spatial queries. Our findings provide an efficient tool for large-scale maritime emission analysis, strongly supporting the green development of global shipping.