Revealing the spatiotemporal evolution and dominant drivers of natural vulnerability in China's coastal islands by remote sensing

In the context of global warming and rising sea levels, investigating the natural vulnerability patterns of island groups is essential for establishing comprehensive ecological security barriers. Using the Google Earth Engine (GEE) platform, we identified 869 islands (>0.2 km 2 ) along China's coast MNDWI and Otsu threshold segmentation. Based on multi-dimensional spatial features, K-means clustering classified these islands into four types: Large Irregular Nearshore Islands (LINI), Medium Regular Offshore Islands (MROI), Small Irregular Nearshore Islands (SINI), and Medium Regular Nearshore Islands (MRNI). An Island Natural Vulnerability Index (INVI), incorporating ecological, topographical, and hydrodynamic characteristics, was developed to assess the spatiotemporal evolution of different island types from 2015 to 2024. The results indicate that China's coastal islands showed an overall increasing trend in vulnerability. Moderately and highly vulnerable islands increased from 168 to 312 and from 8 to 36, respectively. LINIs and SINIs degraded fastest, while MROIs remained relatively stable. Different factors influenced island natural vulnerability over different timescales. Topography and vegetation coverage served as primary long-term controls, while marine dynamic factors, especially mean wave height (MWH), exerted short-term amplification. Unlike previous single-island studies, this study integrates differences in island area, shape, and location to effectively assess the natural vulnerability of different island types, thereby enhancing the comparability and regional applicability of the assessment results. Based on the findings, we propose targeted strategies to mitigate the increasing vulnerability of islands, including prioritizing interventions for rapidly deteriorating islands and implementing preventive conservation for highly sensitive SINIs.