Although the sustainability of cultural heritage plays important roles in the inheritance of humans’ civilization and history, assessments of such fields is still limited. In this paper, the spatiotemporal analysis method and coupling coordination model were applied to assess the sustainability of cultural heritage in Shandong Province. Results show that (1) the National Key Cultural Relics Protection Units (“cultural units“ hereinafter) of Shandong Province have obvious convergence of spatiotemporal and city distribution; (2) although the tourism response degree of each city vary greatly, their tourism income exhibits positive correlation (i.e., correlation coefficient reaches 0.6639) with cultural units; (3) the coordination between cultural units, capital inputs, and tourism income in different cities is different, mainly shows three levels of well coordination (Jinan, Jining, Qingdao, Yantai, Weifang, and Zibo), general coordination (Zaozhuang, Weihai, Tai’an, Liaocheng, Heze, Linyi, and Rizhao), and poor coordination (Dezhou, Binzhou, and Dongying). These findings imply that the SDGs 11.4.1 is an effective indicator to promote the sustainable development of cultural heritage. However, there are still shortcomings at the city level, and the distribution of heritage and tourism response in the area should be considered in the future.

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes.

Flood disasters pose numerous hazards which seriously endanger human life and property. Therefore, flood monitoring is essential. In August 2018, an unusual precipitation occurred in Weifang City, Shandong Province, China, causing severe flooding. By making full use of polarization information from Sentinel-1A SAR data before and during the disaster in Weifang City, we were able to employ the Doublet, Otsu and Region Growing methods towards VV and VH images to extract the water body information. To improve the accuracy of water body identification we took the intersection processing of VV and VH images before and during disaster respectively. Then we monitored the submerged area by taking overlay analysis of the results of before and during flood. The comparison analysis between these three methods and the InSAR result show that the Doublet method has the largest difference with the InSAR result and has the lowest recall and precision rate. The Otsu and Region Growing methods are equivalent, but the Otsu had a higher recall rate while the Region Growing method had a higher precision. The area of flood inundation in Weifang City based on the Otsu method is about 1892.7km², while the area of flood inundation according to the Region Growing method is about 1576.6km².

Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.

Urban land subsidence has emerged as a critical issue in China, driven by rapid urbanization and extensive infrastructure development, posing serious threats to public safety and economic sustainability. However, most existing subsidence prediction models rely primarily on one-dimensional time-series deformation data, neglecting the combined influence of multiple environmental factors. To address this limitation, we propose an optimized transformer-based prediction model capable of capturing long-range temporal dependencies and complex nonlinear relationships among multi-source variables. The Jinan Start-up Area, a region facing high subsidence risk due to accelerated urban expansion, was selected as the study area. High-resolution time-series deformation data were obtained using a small baseline subset interferometric synthetic aperture radar technique, and auxiliary datasets including land cover classification, road network density, and precipitation were integrated to construct a comprehensive multi-feature dataset. Experimental results indicate that subsidence exhibits distinct spatial heterogeneity across land cover types, with dense road networks amplifying local instability and precipitation-driven recharge alleviating deformation. The proposed model achieves superior predictive accuracy, outperforming recurrent neural network, gated recurrent unit, and long short-term memory by 20.6%, 1.4%, and 7.9%, respectively. These results demonstrate the advantages of the model for addressing urban land subsidence forecasting.

Dynamic trade-offs and synergies among ecosystem services (ESs) are highly sensitive to land-use change, spatial scale, and future uncertainty. However, most ES-based zoning studies rely on static assessments that overlook temporal dynamics and scenario robustness. To address this limitation, we propose a novel intensity–trend–stability framework that integrates historical interaction strength, projected future trajectories, and cross-scenario consistency to assess and spatially zone ES interactions. The framework was applied to the Songnen Plain, China, using multi-scale analysis and four contrasting land-use scenarios for 2030. An XGBoost–SHAP model was further employed to identify key drivers and nonlinear effects underlying ES interaction dynamics. Results show that (1) land-use transitions exhibit strong scenario dependency under different development pathways. (2) Water yield consistently exhibits trade-offs with other ESs, whereas soil retention, carbon sequestration, and habitat quality maintain stable synergies, with interaction intensity generally weakening at coarser scales. (3) The proposed framework effectively identifies stable conflict zones, synergistic hotspots, and transitional areas, with HHH zones of water-related interactions accounting for 30.72–37.43% of the study area, while LLH zones of other ES pairs each occupy more than 39%. (4) Climatic and topographic factors primarily regulate water-related interactions, whereas vegetation conditions and landscape configuration dominate synergistic ES relationships, with pronounced nonlinear threshold effects. The proposed framework improves the detection of dynamic ES interaction patterns and supports scenario-based ecological zoning and sustainable land-use management.