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• A framework is proposed to integrate machine learning, ecological footprints and PLUS models. • Coupling of multiple data sources with machine learning algorithms improves classification accuracy. • The random forest algorithm is optimal for comparison accuracy. • Improved ecological footprint models to improve the accuracy of ECC. • Restoration of forest land with the highest ECC potential. Ecological carrying capacity is key to evaluating sustainable development capacity, and land use change has varying degrees of impact on ecosystems. This study explores the ecological carrying capacity based on land use change, which is important for the conservation and scientific and rational use of ecosystems. Therefore, in this study, three machine learning classification 3algorithms, namely support vector machine, random forest and artificial neural network, were selected for accuracy comparison and analysis based on multi-source data, using Southwest Guangxi Karst-Beibu Gulf as the study area. The Patch-generating Land Use Simulation Model is used to dissect the spatial and temporal changes in ecological carrying capaciyt from 1990 to 2040 and future trends. The results showed that (1) Coupling multiple sources of data and machine learning classification algorithms can ultimately improve land use classification accuracy. The RF is compared and found to be the optimal classification algorithm for this study. (2) The area of arable and grassland gradually decreases between 1990 and 2040, mainly encroached upon by build land; the area of forested land gradually recovers, the watershed fluctuates up and down, and the area of build land gradually increases. (3) The ecological carrying capacity from 1990 to 2040 shows a trend of decreasing and then increasing, and the area of low and medium–low decreases from 2020 to 2040, with arable land and forest land having a stronger potential to increase ecological carrying capacity. Nanning City, Qinzhou City and Chongzuo City are severely damaged. The framework provides an effective tool for exploring future ecological carrying capacity to support the ecological conservation and future sustainable development of the region. [ABSTRACT FROM AUTHOR]

• Improved ecological footprint model for assessing ECC. • Constructed an ecological safety pattern based on the "history-present-future" concept. • Constructed an integrated ecological security network of points, lines and surfaces. • Proposed an ecological security optimization scheme of "two belts, three screens, four zones and multiple centres". The construction of ecological security pattern and ecological security network and reasonable ecological security planning are of great significance for ecosystem protection and restoration and optimal regulation. This study takes the Southwest Guangxi Karst - Beibu Gulf as the study area. Based on the improved ecological footprint model and the Patch-generating Land Use Simulation (PLUS) model, the ecological carrying capacity changes and driving mechanisms from 1990 to 2040 were analysed. The study proposed the idea of "history-present-future" to construct an ecological security pattern, We have constructed an integrated "points, lines and surfaces" ecological security network and carried out ecological security optimization planning. The results show that (1) The overall 1990–2040 belongs to the medium-high carrying capacity grade, showing a trend of rising and then falling, and the spatial changes are dominated by the type of unchanging ecological carrying capacity. Nanning is the most severely damaged. Slope and temperature are the main driving factors. (2) A total of 17 ecological source patches were identified in this study. The ecological source patches account for the overall area in general, and their distribution is relatively concentrated, so the ecological security of the central city needs to be focused on. (3) This study extracts 17 ecological corridors and 11 karst ecological corridors, generating an integrated ecological security network structure of "points, lines and surfaces". The ecological optimization planning proposal of "two belts, three screens, four zones and multiple centers" is proposed. [ABSTRACT FROM AUTHOR]

• A three-dimensional balance model of resource and environment bearing capacity was established. • The key technical breakthrough of resource and environment carrying capacity from classification to synthesis was realized. • Integrated resource and environment carrying capacity based on the perspective of SDGs. Resources and environment carrying capacity is the key to achieving Sustainable Development Goals (SDGs). This study takes SDGs as the research perspective, taking the Southwest Guangxi Karst-Beibu Gulf as a typical research area, takes counties as the research unit, creates a three-dimensional balance model of resource and environmental carrying capacity, and carried out a comprehensive evaluation and analysis of resource and environmental carrying capacity in this area. It reveals the warning grade of different regions in the transitional space of mountains, rivers and seas, and provides a scientific basis for the construction of ecological civilization and implementation of sustainable development goals in China. Results showed that the natural factors of the human settlement environment will greatly affect the resources and the environment carrying capacity. Water and soil resources and the risk of natural disasters have already restricted resources and the environment. A low level of social and economic development will also reduce the improvement of resources and environmental carrying capacity. More than half of the warning levels of resource and environmental carrying capacity in the study area are in a balanced state. Although the comprehensive carrying capacity of resources and environment has declined, it is in good condition. The focus areas are still the karst mountains in Baise and Chongzuo. Coastal areas of the Beibu Gulf should continue to pay attention to the rational use of land resources and the prevention of natural disasters. Human beings can adjust factors such as water and soil resources, ecological environment, economic framework and social foundation to achieve continuous utilization of regional resources and environment and continuous progress of composite systems. [ABSTRACT FROM AUTHOR]

• Explore the comprehensive evaluation of multi-risk sources ecological risk in complex ecosystem. • Multi-model based multi-perspective analysis of ecological risks. • The OWA-GIS method overcomes the shortcomings of the traditional single evaluation method. The transitional space is a complex ecosystem, which has a certain impact on the survival and development of human beings. However, with the aggravation of resource utilization and the acceleration of urbanization, the ecological environment is more damaged and the ecological risk is increased. Therefore, it is necessary to establish a reasonable and scientific ecological risk assessment system. In order to reveal the comprehensive ecological risk situation in the transitional space of Guangxi, China, the representative southwest Guizhou-Beibu Gulf region was used as the study area. This research was based on multi-source data and DPSIR model, using a non-dimensional quantitative model for data standardization, and using subjective and objective evaluation methods to assign weights to 18 indicators. Finally, the OWA-GIS method was used to conduct a comprehensive ecological risk assessment of the region under different decision risks. The results show that: (1) The risk level was at high risk in karst areas and coastal areas of Beibu Gulf as well as areas with intensive human activities. The forest areas in Baise show low-risk levels under different risk perspectives. (2) Vulnerability-resources-disasters affect each other's ecological risks. The Chongzuo Karst area, Nanning urban area, Beihai and Fangchenggang have higher-risk levels. The comprehensive ecological risk presents a transitional distribution from a low-risk in the northwest mountainous area to a high- risk in the southeast coastal area. (3) The decision attitude ranges from optimistic to pessimistic, with a gradual increase in risk level as the risk factor increases. The central and southern regions are higher than those in the north, and the areas with better socio-economic development and frequent human activities have higher risk levels. In reality, decision makers must adjust the decision risk coefficients according to different decision goals to obtain reasonable decision results. [ABSTRACT FROM AUTHOR]

• Spatiotemporal evolution and destruction degree of wetlands from 1990 to 2018. • Comparison and Selection of Four Intelligent Algorithms for Land Cover Classification. • CA-Markov, support vector machine algorithm, wetland evolution prediction in 2018–2035. Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS). [ABSTRACT FROM AUTHOR]

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development. [ABSTRACT FROM AUTHOR]

• Exploring the change of ESV pattern under "Production-Living-Ecological Space". • Analysis of ESV distribution characteristics in multi-characteristic regions. • Spatial autocorrelation analysis reveals the regional ESV spatial aggregation state. • A standard deviation ellipse inscribes the regional ESV center of gravity migration. • Natural, economic, and social variables all have a role in regional ESV development. In order to investigate the changing patterns and influencing factors of ecosystem service values (ESV) provided by the "production-life-ecological space" (PLES) in the karst-Beibu Gulf of southwest Guangxi under the geographical characteristics of the mountain-river-sea transition zone, this study selected land use data at four time points from 1990 to 2020, clarified the characteristics of regional ESV evolution through spatial autocorrelation analysis and standard deviation ellipse, and explored its formation mechanism with the help of geographic probes. The results showed that: (1) The overall spatial variation of the total ESV in the study area was obvious, with "high in the southwest and low in the northeast" and a decreasing trend in general. (2) The ESV in PLES perspective showed positive spatial autocorrelation overall and exhibited obvious spatial aggregation characteristics. Their center of gravity as a whole shifts to the southeast, the ellipse area keeps expanding, and the center of gravity positions all remain in Nanning City. (3) ESV evolution is closely related to natural, economic, and social factors, with rocky desertification area and Normalized Difference Vegetation Index (NDVI) having the most significant spatial differentiation effect on ESV provided by production space and ecological space, average altitude contributing more to ESV provided by living space, and interaction between various elements improving ESV differentiation from the perspective of PLES. The results of the study provide a scientific basis for optimizing regional territorial spatial planning and improving regional ESV. [ABSTRACT FROM AUTHOR]

• Compared with intelligent classification algorithms of remote sensing, the algorithm with the highest accuracy was selected. • Explore the impact of land use change on ecosystem service value. • Analyze implications for SDGs reporting and regional sustainable development. Ecosystem services provide a variety of benefits for mankind, and their sustainable use plays an important role in achieving the Sustainable Development Goals (SDGs). This study takes Guangxi as the research object and compares multiple intelligent classification algorithms for land cover based on multi-source data. Selects Random Forest (RF) classification algorithms for land cover classification, analyses their temporal and spatial changes, and the extracted results produce SDG 15.1.1, 15.1.2, 15.1.4 indicators, and assess the degree of completion of Guangxi's SDGs 15.2 indicators. On this basis, the equivalent factor method was used to evaluate the change of ecosystem value caused by land use change, and the direct impact of land use change on SDG15.9 was evaluated. The results indicate that:(1) From 1990 to 2020, forest was the most important land use mode in Guangxi, and the most serious wetland shrinkage was in Beihai and Nanning; The overall trend of land use changes is that wetlands, forests, and grasslands have decreased, dry land and construction land have increased, and bare land has remained basically remained unchanged. (2) From 1990 to 2020, the SDG 15.1.1 indicator showed a slow rise and then a rapid decline. The SDG 15.1.2 indicator showed a continuous downward trend. The SDG 15.1.4 indicator showed a steady upward trend. Guangxi failed to achieve the target of SDG15.2 by 2020. (3) From 1990 to 2020, the overall ecosystem service value in Guangxi showed a decreasing trend, and the one-way ecosystem service value in Guangxi was dominated by hydrological regulation and climate regulation. The ecosystem service value showed a spatial pattern of "low in the central region and high in the surrounding areas." (4) Grassland to forest and dryland to forest were the main types of ecological service income; Forest conversion to dryland is the most important type of ecological service loss. (5) As a whole, only dryland sensitivity index less than 0 during 2010–2015 had a negative impact on Ecosystem services values (ESV), while other time and land use type sensitivity index were all greater than 0 had a positive impact on ESV. [ABSTRACT FROM AUTHOR]