Spatial investigation and pattern exploration of salinity erosion damage of the Ming Great Wall in Gansu, China
Source abstract
Abstract
The Great Wall is currently facing a significant amount of erosion damage that are rapidly causing its decay and destruction. Among these, erosion damage caused by saltwater intrusion, are the most devastating, as they undermine the foundation of the wall, leading to rapid collapse and disappearance. This study utilizes low-altitude oblique photography technology with unmanned aerial vehicles (UAVs) to construct a digital method route for rapid survey and precise quantification of the damage. We conducted a comprehensive survey of erosion damage along the Ming Great Wall in Gansu, China. A three-dimensional point cloud model was utilized to quantitatively assess the morphological characteristics of the erosion damage. Additionally, the distribution patterns of saline erosion were examined through a combination of field sampling and physicochemical experimental analysis. Our findings indicate that the spatial distribution of erosion damage exhibits an aggregative characteristic and is closely related to natural geographical elements. There are regional differences in the morphological characteristics of erosion, with high concentrations of erosion height and depth data in different sections. The degree of saltwater intrusion and the distribution of soluble salt content vary in different parts of the wall, showing a regular pattern of higher levels on the south side, decreasing from bottom to top. The paper conducts a basic survey of the spatial distribution and patterns of erosion damage, which decrease from a macroscopic distribution to key sections, and produces a risk map of erosion damage based on the survey results, providing reference and scientific support for the protection and restoration of the Great Wall.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Building materials and conservation · Conservation Techniques and Studies · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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