Given the increasing frequency and severity of floods caused by climate change, there is a pressing requirement for creative ways to improve public comprehension and control of hydrological phenomena. Contemporary technology provides unparalleled possibilities to transform this domain. This project investigates the possibilities for merging gaming engines and digital twins to enhance flood education, data visualization, and interactive monitoring. This study proposes the utilization of immersive digital twins to enhance the comprehension of hydrological and hydraulic systems. The suggested method utilizes game engines to generate dynamic and interactive models that connect raw data to practical insights, enabling a more profound understanding of flood dynamics. This study underscores the wide-ranging usefulness of digital twins in various watersheds by focusing on the development of advanced monitoring systems, the benefits of improved data visualization, and educational outreach. The incorporation of real-time data via IoT technology considerably improves the significance and precision of these virtual models. This novel approach seeks to refashion flood management approaches by cultivating well-informed stakeholders and advocating for effective environmental education, ultimately leading to more resilient and prepared communities. An immersive digital twin of the real world can assist decision-makers technically, psychologically, and mentally by making complex phenomena easier to understand and visualize, thanks to real-time data and simulations that keep the information up-to-date, consequently leading to a more precise and intuitive decision-making process.

The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km²/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km²/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.

The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods. In order to deal with the problem of small samples in hyperspectral image classification, a novel small sample classification method based on rotation-invariant uniform local binary pattern (RULBP) features and a graph-based masked autoencoder is proposed in this paper. Firstly, the RULBP features of hyperspectral images are extracted, and then the k-nearest neighbor method is utilized to construct the graph. Furthermore, self-supervised learning is conducted on the constructed graph so that the model can learn to extract features more suitable for small sample classification. Since the self-supervised training mainly adopts the masked autoencoder method, only unlabeled samples are needed to complete the training. After training, only a small number of samples are used to fine-tune the graph convolutional network, so as to complete the classification of all nodes in the graph. A large number of classification experiments on three commonly used hyperspectral image datasets show that the proposed method could achieve higher classification accuracy with fewer labeled samples.

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

Considering the shortcomings of the currently used time functions for dynamically predicting surface mining subsidence and calculating its parameters, a novel time function is proposed on the basis of an in-depth analysis on the movement characteristics of mining surface points in a fully mined area and the measured mining subsidence data in the field during the course of the mining process. The proposed function can be used to effectively characterize the surface subsidence, the subsidence velocity, and the acceleration of the mining area. All the parameters involved in the function have their physical meaning, and their influence on the function was also analyzed in this study. A parameter calculation method is proposed for the new time function based on the normalization method and least square principle. Taking the measured dynamic subsidence data of 22,618 working faces in a coal mine as an example, the reliability of the new time function model was verified by comparing the measured data with the predicted results. The results show that the average relative root-mean-square error was 5.2%, and the prediction accuracy was improved compared with the Knothe time function, double-parameter Knothe time function, and piecewise optimized Knothe time function.

Spatio-temporal change characteristic in leaf area index (LAI), gross primary productivity (GPP), total leaf area and total carbon sequestration of terrestrial vegetation at different geographic regions, provinces and land cover change types over China during 2000–2018 were clarified, respectively. The net increase in total leaf area and carbon sequestration over China from 2000 to 2018 is 1.7491 × 10⁶ km² and 1957.36 TgC, and Southwestern part contributes the most with a contribution of 25.3% for leaf area and 20.1% for carbon sequestration, respectively. The overall contribution of northern provinces to enhanced carbon sequestration capacity is greater than that of southern provinces, with a maximum for Inner Mongolia, even though southern provinces have a much higher leaf area increase. The annual increase rates of LAI and GPP in returning cropland to forest–grassland and artificial restoration of forest–grassland are higher than that of self-rehabilitation of rapid growth stage for planted young vegetation. However, due to self-rehabilitation of forest–grassland has the largest area percentage of 59%, it contributes the most to the increases of leaf area and carbon sequestration among different land cover change types, except for northeastern China, where improved farmland productivity is a dominant factor. It should be noted that the leaf area and carbon sequestration exhibit upward trends in urbanized areas over China. whereas slight increasing and even negative trends are also observed in southwestern, southern, and eastern China (e.g., Shanghai), where artificial surface construction occupied more cropland during urbanization processing. Compared with climatic factors, LAI is the dominant factor influencing GPP increases over China since 2000, and gradually weakened coupling relationship between LAI and GPP is observed from north to south. For climatic factors, GPP is dominantly affected by precipitation in northern parts and by solar radiation in southern parts.