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.

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.

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world.

Country-level resource and environmental carrying capacity (RECC) assessments can reveal which countries are off-track on ongoing adaptive management towards the sustainability goals. However, fewer effective methods exist to conduct a comprehensive assessment of RECC at the country-level. We implemented the Analytic Hierarchy Process (AHP) to comprehensively evaluate the spatial and temporal evolution of RECC from 1990 to 2020, based on the construction of evaluation indicators of regional characteristics derived from remote sensing (RS) and statistical data for 11 Southeast Asian countries. The results show that: (1) In terms of per capita level, most countries in Southeast Asia show a trend of increasing and then decreasing RECC, with lower RECC levels in the east and north, such as Myanmar and Vietnam, and higher levels in the west and south, such as Indonesia and Brunei. (2) In terms of absolute total, most countries in Southeast Asia show a slow increase in RECC, except for Thailand, which slightly decreases, with lower RECC in northern and central counties, such as Laos and Singapore, and higher in other regions, such as Indonesia and the Philippines. Therefore, we recommend that policymakers pay more attention to the control of population size and adhere to a green economic growth model to alleviate the declining trend of recent RECC. This study proposed a comprehensive evaluation method of RECC that address the challenge of assessing different countries with resource and ecological imbalance, which provides potential GIS solutions for in-depth RECC assessment of other countries in the world. Meanwhile, this paper provides insights for Southeast Asian countries to achieve better sustainable development from the perspective of RECC.

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.