Abstract: Mangroves are crucial ecosystems with significant ecological and economic roles, providing habitat for diverse species, storm protection, and carbon sequestration. While many mangroves worldwide have faced substantial losses, China has seen notable recovery. Understanding the expansion patterns of mangroves necessitates high-resolution, large-scale monitoring of their coverage over time. In this study, we divided coastal zones based on geomorphic and sedimentary environments and generated annual mangrove coverage from 2016 to 2022 using multi-source high-resolution data and a U-Net model. By analyzing morphological changes of mangrove patches in different coastal zones of the Guangxi Beibu Gulf, we summarized expansion patterns and calculated expansion and degradation rates from area changes. Our findings reveal a 1,413 ha increase in mangrove area from 2016 to 2022. Estuarine mangroves experienced an initial increase followed by a decrease, while gulf mangroves showed an initial decrease followed by an increase. Three distinct expansion patterns were identified: (1) regular mangroves expanding parallel to coastlines; (2) mangroves expanding in estuarine areas where freshwater and seawater meet; and (3) independent mangrove patches expanding into surrounding wetlands. In summary, stable and changing mangrove areas were in a ratio of 7:3, with expansion areas at 70.5% and degradation areas at 29.5%. The loss of mangroves in Guangxi Beibu Gulf is primarily due to intense human activities, with minimal contributions from sea level rise and marine dynamics.

The “Two Mountains” concept of “green water and green mountains are gold and silver mountains” plays an important value-oriented role in the ecological transformation of land consolidation. In this study, Guangxi was divided into five consolidation zones in combination with relevant policies, and the evolution characteristics and change intensity of ecological-economic values before and after the three phases of land consolidation in Guangxi and each consolidation zone in 2010, 2015 and 2020 were explored by bivariate spatial autocorrelation, standard deviation ellipse, and linear regression equation. Finally, the ecological-economic values of each consolidation area, which were obtained separately, were standardized by z-score, and the standardized results were matched by dividing quadrants for analysis. The ecological-economic value matching states of each consolidation area are ecological-economic value coordinated development type (central karst basin area of Guangxi), ecological value imbalance type (southeast plain area and coastal hilly plain area of Guangxi), economic value imbalance type (northwest mountain area of Guangxi) and ecological-economic value low imbalance type (northeast hilly mountain area of Guangxi). The study aims to provide a theoretical basis for the planning and differentiated management of land consolidation in Guangxi and promote the ecological-economic value transformation of the region.

Enhancing the spatio-temporal dynamic monitoring of the construction land of emerging major cities provides the basis for understanding the effects of human activities on the earth’s ecological environment changes, which is regarded as an important way to improve human wellbeing and to achieve the UN SDGs. This paper, which uses Nanning as an example, adopts 30 m annual remote sensing land cover datasets from 1990–2021 to elaborately analyze the temporal evolution and spatial expansion of construction land and to evaluate the sustainable development level based on SDG 11.3.1. The conclusions of this paper are as follows: (1) For Nanning City, during 1990–2021, construction land area increased from 54.77 km² to 326.33 km²—a nearly five-fold increase. Spreading expansion and finger-like expansion along roads played a leading role during the development of the construction land in Nanning. Liangqing and Yongning are the future directions of urban development. The LCRPGR of Nanning declined with one peak period around 1995, and the LCRPGR was relatively stable with a high intensity of land use. (2) For the other emerging major cities, continuous long duration data are more suitable for urban construction land monitoring compared with traditional sparse time-series monitoring. The rich information derived from continuous long duration data can help decision makers to formulate and implement more comprehensive measures. The research paradigm adopted in this study can be applied in other cities. The newly developed urban districts will have foreseeable high sustainable development risks, and urban development strategies at the international, national, and urban levels could reduce those risks and promote the realization of UN SDGs from different angles and with different intensities.

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.