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.

To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product (ESRI2020), and the European Space Agency world cover 2020 product (ESA2020). However, most previous validations lack field collection points in large regions, especially in Southeast Asia, which has a cloudy and rainy climate, creating many difficulties in land cover mapping. In 2018 and 2019, we conducted a 56-day field investigation in Southeast Asia and collected 3326 points from different places. By combining these points and 14,808 other manual densification points in a stratified random sampling, we assessed the accuracy of the three land cover products in Southeast Asia. We also compared the impacts of the different classification standards, the different sample methods, and the different spatial distributions of the sample points. The results show that in Southeast Asia, (1) the mean overall accuracies of the FROM-GLC10, ESRI2020, and ESA2020 products are 75.43%, 79.99%, and 81.11%, respectively; (2) all three products perform well in croplands, forests, and built-up areas; ESRI2020 and ESA2020 perform well in water, but only ESA2020 performs well in grasslands; and (3) all three products perform badly in shrublands, wetlands, or bare land, as both the PA and the UA are lower than 50%. We recommend ESA2020 as the first choice for Southeast Asia’s land cover because of its high overall accuracy. FROM-GLC10 also has an advantage over the other two in some classes, such as croplands and water in the UA aspect and the built-up area in the PA aspect. Extracting the individual classes from the three products according to the research goals would be the best practice.

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.