Search

Fractal dimension is a fundamental index used to describe the complexity of coastlines. However, the traditional method of calculating divider dimensions using hand-drawn circles is no longer suitable for studying coastlines at large spatial scales due to its limited accuracy and lack of convenience in the current information age. Therefore, this paper developed an automated software based on the principle of the divider method, using the open-source PyQGIS. This software accurately and conveniently calculated the divider dimensions of Chinese mainland coastline, various sea area coastlines, and different province coastlines. Furthermore, the divider dimensions calculated by the software were compared and analyzed with the box-counting dimensions obtained through the box-counting method. It is found as follows. ➀ Compared with the traditional hand-drawn divider method, the automated divider method can ensure computational speed and accuracy while extending the calculation scale, and the obtained divider dimension is generally higher than the box-counting dimension obtained by the box-counting method. ➁ The divider dimension of Chinese mainland coastline in 2020 is 1.194 8, while the divider dimensions of the north and south of Hangzhou Bay are 1.170 4 and 1.217 5, respectively, indicating a higher complexity in the coastline on the south of Hangzhou Bay. ➂ The fractal dimension of Chinese mainland coastline exhibits substantial spatial variations, showing a pattern of higher values in the north and south and lower values in the middle. At the sea area level, the divider dimension of the coastline is the largest (1.209 1) in the South China Sea area and the smallest (1.156 3) in the South Yellow Sea area. At the provincial level, the divider dimension of the coastline is the largest (1.290 4) in Tianjin and the smallest (1.087 5) in Shanghai. This study demonstrates that the automated divider method offers a convenient and robust calculation tool for analyzing the fractal dimension of coastlines. It enhances computational efficiency and accuracy while providing technical support and serving as a reference for decision-making in the investigation and comprehensive management of coastlines. [ABSTRACT FROM AUTHOR]

[Display omitted] • Remote sensing and environmental management are combined in an assessment. • Three environmental indicators are selected for coastal monitoring and assessment. • A DPSR framework explores coast response to rapid anthropogenic disturbance. Coastal systems are continuously reshaped by rapid anthropogenic disturbance globally. However, the awareness of how coastal systems adapt to rapid anthropogenic disturbance remains inadequate. Thus, this study assessed the spatial response of coastal systems to rapid anthropogenic disturbances, and a typical coastal system (Hangzhou Bay) was selected for a case study. Three environmental indicators (i.e., coastline change rate, accretion/erosion grade, and the displacement of the center of gravity) were selected to measure the morphological changes in the coastal system between 1990 and 2020. The results showed that the coastal waters of Hangzhou Bay decreased by 432 km2, and the coastal morphology became structured driven by increasing artificial coastlines. Moreover, the accretion–erosion equilibrium of underwater elevation transitioned northwest, which derived the shift of geometric center of gravity toward the northwestern part of the coast. These fluctuating signals provided us with a macroscopic perception of coastal system changes, which is expected to provide local decision-makers with sites where coastal systems are adaption loss to assist coastal protection. [ABSTRACT FROM AUTHOR]