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.

Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. The method first performs coarse alignment using canopy-level digital surface models and Fast Point Feature Histograms, followed by fine registration with Iterative Closest Point. Experiments conducted in six forest plots achieved an average registration accuracy of 0.24 m within 5.14 s, comparable to manual registration but with substantially reduced processing time and human intervention. In contrast to existing tree-based methods, the proposed approach eliminates the need for individual tree segmentation and ground filtering, streamlining preprocessing and improving scalability for large-scale forest monitoring. The proposed method facilitates a range of forest applications, including structure modeling, ecological parameter retrieval, and long-term change detection across diverse forest types and platforms.