Intangible cultural heritage (ICH) reflects a region’s history and culture, serving as a significant indicator of regional identity and cohesion. The Grand Canal Basin in China is rich in historical traditions, containing a rich array of ICH resources. Analyzing the spatiotemporal distribution characteristics and influencing factors of ICH within the Grand Canal Basin of China can provide a scientific basis for developing cultural industries and promoting sustainable regional economic growth. This study employed GIS-based spatial analysis methods, including kernel density estimation, the mean nearest neighbor index, and standard deviation ellipse, to investigate the spatiotemporal distribution of 504 national-level ICH items (including extensions) in the Grand Canal Basin of China. The results demonstrate the significant spatial clustering of ICH, with concentrations in high-density regions, particularly at the northern and southern ends of the canal. There is significant regional disparity in the distribution of ICH, with an uneven quantity and structure, predominantly featuring traditional skills and traditional drama categories. The average centroid shift of ICH exhibits a north-to-south oscillatory trajectory. However, overall, it demonstrates a southward-moving trend. This study also underscores the impacts of urbanization, population density, economic development, and transportation infrastructure on ICH distribution. Among these factors, urbanization exerts the strongest influence on the spatial distribution of ICH. The impact of the natural environment is relatively minor; however, it remains a significant element that cannot be overlooked during development. This research offers valuable data and insights for local governments and institutions to formulate evidence-based strategies for the protection and sustainable utilization of ICH resources, promoting sustainable cultural and economic development in the Grand Canal Basin.

Accurate estimation of terrestrial ecosystem carbon storage and the scientific formulation of ecological conservation and land use policies are essential for promoting regional low-carbon sustainable development and achieving the goal of “carbon neutrality.” In this study, the FLUS–InVEST model was used to evaluate the carbon stocks of the Jiangsu coastal zone in China from 1995 to 2020 and scientifically forecast the changes in carbon stocks in 2030 under three scenarios: natural exploitation, ecological protection, and economic development. The results are as follows: (1) From 1995 to 2020, carbon storage in the coastal zone initially remained stable before declining, a trend closely linked to the accelerated urbanization and economic growth of Jiangsu Province. (2) By 2030, carbon storage under the three scenarios exhibits a pattern of “S1 decrease–S2 increase–S3 decrease,” with a more significant increase in construction land under the natural development and economic development scenarios compared to the ecological protection scenario. (3) The sensitivity of carbon storage to land use changes varies across scenarios. In the natural development scenario, carbon storage is most affected by forest reduction and construction land expansion. In the ecological protection scenario, it is more responsive to increases in non-construction land. In the economic development scenario, the expansion of construction land leads to the most significant decrease in carbon storage. Therefore, when formulating future territorial spatial planning policies and urban development strategies, it is essential to consider ecological protection and economic development scenarios comprehensively, taking into account carbon sequestration capabilities. This approach will ensure effective conservation and restoration of damaged ecosystems while safeguarding the robust development of urban economies and societies.

Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. By integrating the InVEST model and landscape pattern indices, this study analyzes the spatiotemporal evolution characteristics of habitat quality in Jiangsu Province and uses geographical detector analysis to examine the synergistic effects of influencing factors. The results indicate that, from 2000 to 2020, habitat degradation in Jiangsu Province progressively increased, with the spatial distribution of degradation levels showing a gradual change. Under the ecological protection scenario in 2030, habitat fragmentation was alleviated. Conversely, under the economic development scenario, habitat quality further deteriorated, resulting in the largest area of low-quality regions. Minimal changes occurred under the natural development scenario. (2) The landscape indices in Jiangsu Province experienced significant changes from 2000 to 2020. The continuous expansion of urban land into other land use types led to a trend of fragmentation, with a clear increasing trend in dispersion, sprawl, and Shannon’s diversity index, accompanied by a decrease in cohesion. (3) The dominant interacting factors affecting habitat quality were combinations of socioeconomic factors with other factors, indicating that the economy largely determines the spatial distribution pattern of habitat quality. The findings of this study provide optimization strategies for future spatial planning of land use types in Jiangsu Province and offer references for habitat quality restoration efforts in the region.

In UAV (unmanned aerial vehicle) oblique photogrammetry, the occlusion of ground objects, particularly at their base, often results in low-quality real-scene 3D models. To address this issue, we propose a method to enhance model quality by integrating ground-based camera images. This innovative image acquisition method allows the rephotographing of areas in the 3D model that exhibit poor quality. Three critical parameters for reshooting are the reshooting distance and the front- and side-overlap ratios of reshooting images. The proposed method for improving 3D model quality focuses on point accuracy, dimensional accuracy, texture details, and the triangular mesh structure. Validated by a case study involving a complex building, this method demonstrates that integrating ground camera photos significantly improves the overall quality of the 3D model. The findings show that optimal settings for reshooting include a distance (in meter units) of 1.5–1.6 times the camera’s focal length (in millimeter units), a front overlap ratio of 30%, and a side overlap ratio of 20%. Furthermore, we conclude that an overlap rate of 20–30% in reshooting is a usable value.