Sustainable development (SD) evaluations have attracted considerable attention from governments and scientific communities around the world. The objective and quantitative calculation of the importance of sustainable assessment indicators is a key problem in the accurate evaluation of SD. Traditional methods fail to quantify the coupling effects among indicators. This paper presents a weight determination approach based on the global sensitivity analysis algorithm known as the extended Fourier amplitude sensitivity test (EFAST). This method is efficient and robust and is not only able to quantify the sensitivity of the evaluation indictors to the target, but can also quantitatively describe the uncertainties among the indictors. In this paper, we analyze the sensitivity of 18 indicators in a multi-index comprehensive evaluation model and weigh the indicators in the system according to their importance. To verify the feasibility and advantages of this new method, we compare the evaluation result with the traditional entropy method. The comparison shows that the EFAST algorithm can provide greater detail in an SD evaluation. Additionally, the EFAST algorithm is more specific in terms of quantitative analysis and comprehensive aspects and can more effectively distinguish the importance of indicators.

The Sustainable Development Goals (SDGs) of the United Nations cover all living things on Earth. However, downscaling the SDGs to regional scales for implementation is challenging. In the paper, we convert the general SDGs into tangible and actionable goals, targets and indicators for use in integrated river basin management (IRBM). Further, we propose a decision support framework that can be used to support IRBM implementation based on the SDGs. The framework offers a context for open thinking in which IRBM decision makers envision socioeconomic and ecosystem goals and the development tracks of a river basin and explore the various paths that can be followed to reach the goals. In particular, indicators are proposed for use in IRBM, which consider five aspects of river basins, specifically water, ecosystems, socioeconomic development, ability and data. To enable decision-making that promotes progress toward the goals, five scenarios, 17 sub-scenarios and 29 key parameters are provided that form a diverse set of scenarios corresponding to specific decision schemes. Moreover, these scenarios, sub-scenarios and parameters consider future uncertainties and both engineering and non-engineering measures that can be taken to achieve the co-development of human and natural factors in a basin.

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It is of importance but great difficulty to objectively and quantitatively evaluate the sustainable development level, especially in the weight determination process and uncertainty evaluation. The traditional weight determination methods hardly reflect the coupling effect (interaction) among the indices. More importantly, conventional evaluation methods seldom consider the uncertainties of the indices in the index system. Thus, it is indispensable to apply a more comprehensive approach to solve these defects. This paper presents a new method to evaluate the sustainable development level. The approach integrates the advantages of the Extended Fourier Amplitude Sensitivity Test (EFAST) and Set Pair Analysis (SPA) (called EFAST-SPA). The EFAST algorithm is used to determine the indices' weight, and the SPA is employed to handle the uncertain relations in the evaluation system and to calculate the sustainable development level. A quantitative evaluation on the agricultural sustainable development in the middle reaches of Heihe river has been conducted using the EFAST-SPA method. The results have been compared with the traditional entropy method and it was concluded that EFAST-SPA and entropy are highly in line with the actual development status. In most cases, the EFAST-SPA method can describe the development levels more accurately, which reflects a higher reliability and application value of this proposed approach. Moreover, the presented method deepens the understanding of sustainable development evaluation from the view of uncertainty analysis inside the evaluation system.

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.

Seling Co Lake, located on the Qinghai-Tibet Plateau, has been expanding rapidly since the 1980s and, in 2008, surpassed Namtso Lake to become the largest lake in Tibet. Additionally, this rapid expansion has significantly impacted the ecological environment, and human activities surround the lake. Thus, it is of great importance to reveal the expansion pattern of Seling Co Lake for a long time-series. Previous studies always contained errors when exploring this subject due to the limitations associated with the quality of remote sensing images. To overcome the existing deficiency, a method based on the SRTM1 DEM and a water frequency Landsat-series dataset is developed to reconstruct the complete inundation area of Seling Co Lake from 1987 to 2021 while taking full advantage of the relationship between the water frequency and terrain. The results show that the water frequency reconstruction model proposed in this study has a significant optimization effect on the restoration of the permanent and seasonal water areas of Seling Co Lake. In particular, the proposed method can effectively improve the underestimated water-frequency pixel values of the seasonal waters located on the southern and northern shores of Seling Co Lake. The water-inundation area of Seling Co Lake showed an overall increasing trend with a rate of 26.02 km2∙year−1 (p < 0.01), and this expansion trend was mainly concentrated in the southern and northern parts of the lake. This study cannot only provide an efficient and feasible remote sensing means of reconstructing the water-inundation area for lakes in complex terrain according to topographic conditions but also greatly refines our understanding of the annual variations in the water-inundation area of Lake Seling Co.

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Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.