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.

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.

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.