The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

As a developing country, China’s mangrove landscape pattern has undergone significant temporal and spatial changes over the last four decades. However, we know little about the changes in the mangrove landscape pattern characteristics other than the area at the national scale. The analysis of mangrove landscape pattern changes from different perspectives on a national scale can provide scientific support for mangrove protection and restoration. In this study, the temporal and spatial changes in the pattern of the mangrove landscape over the last 40 years in China were analyzed based on remote sensing data with high classification accuracy (99.3% of 2018). First, according to the natural geographical conditions of the coastal zone and the distribution of the mangroves, the distribution area of the mangroves in China was divided into 31 natural shores. Then, by selecting representative landscape indexes and constructing an integrated landscape index, the spatial-temporal changes in the landscape pattern of China’s mangroves over the last 40 years were analyzed based on five perspectives: Total area change, shape complexity, connectivity, fragmentation, and the integrated state of the landscape. From a temporal viewpoint, before 2000, the total area of each shore exhibited a downward trend, and the degree of connectivity deteriorated continuously, but the degree of fragmentation was stable at a low level. After 2000, although the total area improved, the degree of fragmentation gradually increased. The spatial changes are mainly reflected by the fact that the shores in Guangdong and Hainan exhibited significant differences within the same province. Based on the above analysis, corresponding scientific suggestions are proposed from temporal and spatial viewpoints to provide guidance for mangrove management and protection in China and to provide a reference for mangrove research in other regions of the world.

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R² = 0.73, p < 0.001) and remotely sensed functional traits (R² = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.

Accurate cultivated land parcel data are an essential analytical unit for further agricultural monitoring, yield estimation, and precision agriculture management. However, the high degree of landscape fragmentation and the irregular shapes of cultivated land parcels, influenced by topography and human activities, limit the effectiveness of parcel extraction. The visual semantic segmentation model based on the Segment Anything Model (SAM) provides opportunities for extracting multi-form cultivated land parcels from high-resolution images; however, the performance of the SAM in extracting cultivated land parcels requires further exploration. To address the difficulty in obtaining parcel extraction that closely matches the true boundaries of complex large-area cultivated land parcels, this study used segmentation patches with cultivated land boundary information obtained from SAM unsupervised segmentation as constraints, which were then incorporated into the subsequent multi-scale segmentation. A combined method of SAM unsupervised segmentation and multi-scale segmentation was proposed, and it was evaluated in different cultivated land scenarios. In plain areas, the precision, recall, and IoU for cultivated land parcel extraction improved by 6.57%, 10.28%, and 9.82%, respectively, compared to basic SAM extraction, confirming the effectiveness of the proposed method. In comparison to basic SAM unsupervised segmentation and point-prompt SAM conditional segmentation, the SAM unsupervised segmentation combined with multi-scale segmentation achieved considerable improvements in extracting complex cultivated land parcels. This study confirms that, under zero-shot and unsupervised conditions, the SAM unsupervised segmentation combined with the multi-scale segmentation method demonstrates strong cross-region and cross-data source transferability and effectiveness for extracting complex cultivated land parcels across large areas.

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.

Extremely hard-hit areas face frequent secondary geological hazards and difficulties in vegetation recovery, and subsequent effects have a significant impact on land cover changes. At present, there is a lack of research on the dynamic restoration of, and changes in, the ecological environment before and after an earthquake, and especially a lack of quantitative assessment of the impact of earthquakes on land cover at the microscopic scale of spatial distribution of landscape indices. Taking the Lushan earthquake in Sichuan Province as an example, this paper obtained land cover data from the study area between 2012 and 2020, and analyzes the spatial distribution characteristics and influencing factors of land cover change frequency by using a comprehensive land cover degree index, land cover transfer matrix and landscape ecology index. The results show that the types of cropland, forest, built-up and bare land have changed significantly in the study area. During the earthquake recovery period, the comprehensive land cover index of the study area showed an increasing trend, and land cover has been continuously improved under the effect of artificial measures and natural restoration. After 2013, patch density (PD) and landscape shape index (LSI) values decreased and aggregation index (AI) values increased for the vast majority of landscape land classes, indicating a benign ecological development across the region in the post-earthquake period. The research results are not only helpful to establish scientific ecological environmental management in the earthquake-stricken areas, but also helpful to formulate medium- and long-term ecological environmental monitoring and ecological restoration plans based on land cover change patterns.