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Mangrove restoration projects are often evaluated based on the increase in mangrove forest (MF) area, but the reliability of this indicator as a measure of successful restoration is questionable. Considering both numerical and quality dimensions, this study assessed mangrove restoration efforts in the Leizhou Peninsula and Beibu Gulf (LP-BG) in China. The hypothesis was that due to the limited social capital investment in mangrove restoration, there exist hierarchical differences in the urgency of mangrove restoration. Time-series Landsat imagery from the Google Earth Engine platform was used to analyze the MF distribution from 2000 to 2020. A resilience indicator, incorporating resistance and adaptive capacity, was constructed to assess MF quality within identified mangrove boundaries. The results revealed an increase in MF area from 6655.87 ha in 2000 to 14,607.93 ha in 2020. However, the majority (79.6%) of MF patches exhibited low resilience (values < 3), with only a minority (51 patches) demonstrating high resilience. Interestingly, MFs within the mangrove reserve displayed higher resilience, but these areas did not exhibit a significant spatial expansion of MFs. These findings highlight the limitation of relying solely on the net growth in the MF area as an indicator of successful restoration. Instead, an ecologically optimal solution is recommended, focusing on expanding conservation boundaries to include remnant MFs outside protected areas, rather than creating new planting areas. This study provides an assessment framework to evaluate the efficiency of mangrove restoration efforts and offers insights for local decision makers to guide future restoration endeavors. [ABSTRACT FROM AUTHOR]

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]