Owing to limited land resources and unique ecosystems, islands face more serious ecological risks under the interference of climate change and human activities. In this study, selecting Zhoushan Island as the study area, a landscape ecological risk index model was constructed based on LULC (land use/land cover) data and the landscape ecological risks for Zhoushan Island from 2000 to 2020 were analyzed. The results showed that: (1) From 2000 to 2020, the proportion of forest land and grassland remained above 70%, built-up land expanded from 52.67 km² to 123.52 km², and the beach area and ocean on the north side of the island decreased by 23.24 km² and 24.87 km², respectively; this was mainly converted into built-up land. (2) The number of landscape patches in Zhoushan Island decreased as the landscape ecological risk index decreased. The landscape ecological risk showed distinct spatial autocorrelation, with lowest-risk and medium-risk areas collectively accounting for 80% and higher-risk and highest-risk areas showing a decline. (3) The landscape ecological risks exhibited distinct spatiotemporal differences. Before 2010, the higher-risk and highest-risk areas were mainly distributed in the mudflat and ocean areas on the northern coast. After 2010, the higher-risk and highest-risk areas are mainly distributed in the central region, which comprises woodland, grassland, and built-up land.

Accurately analyzing the water system’s status in a region is crucial for mitigating water resource constraints on the development of island areas. This study centers on Zhoushan, an archipelago city, and employs the water footprint theory (WFT) and the logarithmic mean Divisia index (LMDI) model to scrutinize the evolution of water footprints from 2010 to 2020. It also dissects the impacts of economic, technological, and population factors on these changes. The findings are as follows: From 2010 to 2020, Zhoushan’s total water footprint has remained relatively stable, but its structure is imbalanced, indicating an unhealthy development. The agricultural water footprint dominates, though its proportion has steadily declined from 64% to 28%, while the imported water footprint has significantly increased from 2% to 29%. The skewed distribution of internal and external water consumption contributes to this imbalance, with internal water use accounting for a staggering 86.43%. The ecological security of water resources appears bleak, with average water scarcity and pressure indices reaching 0.42 and 0.41, respectively, indicating an overloaded state of water resources. Despite a 144.50% growth in water footprint efficiency from 2010 to 2020, the average water resource sustainability index merely stands at 0.531, and its sustainability remains precarious. The LMDI model results unveil that technological factors are the primary negative driving factors, contributing to 47.06% of the changes in Zhoushan’s water resource utilization. Conversely, economic and population factors play positive roles, contributing 42.29% and 10.65%, respectively. Therefore, in the course of development, there should be a focus on promoting water-saving tec-nologies, and continuously enhancing the efficiency of water resource utilization. Simultaneously, attention should be given to the development of the tertiary sector and the water resource pressure resulting from the mobile population, aiming to achieve sustainable water resource utilization and further ensure the ecological security of Zhoushan City.

As a newly designated international wetland city, Hangzhou (China) is currently exploring pathways for high-quality, sustainable development as a habitable city. It is necessary to reveal the baseline status of ecological quality scientifically and rationally whilst tracing its historical changes to support future detailed urban development planning. This study employs the GEE platform, utilizing remote sensing images of Hangzhou from 2013 to 2025. The RSEI index is constructed using four indicators directly perceptible to humans: dryness, heat, wetness, and greenness. The RSEI, coefficient of variation, and Sen-trend analysis were applied to evaluate patterns in ecological quality changes within Hangzhou. Results indicate that during the study period, Hangzhou exhibited minimal variation in RSEI values and Sen indices, reflecting overall ecological stability. Areas classified as “good” ecological grade increased, while other grades decreased. Ecological improvement primarily occurred in early-developed central districts like Xihu and Gongshu, demonstrating Hangzhou’s commitment to refined urban ecological management. This study validates the feasibility of RSEI for environmental assessment in Hangzhou, effectively guiding the city’s pursuit of refined development during late-stage urbanization to enhance the residents’ well-being. Furthermore, it provides a case study for ecological and environmental monitoring in megacities with similar characteristics to Hangzhou, offering significant demonstration value and implications.

The dynamic change in the shorelines reflects an important sign to the socio-economic development of coastal areas. The Huizhou–Shanwei region of China has experienced rapid socio-economic development over the past 33 years. The study of the dynamic change in the shorelines in this region can provide basic data support for the marine environmental protection and regional development planning in this region. Based on Landsat RS (remote sensing) images from 1990 to 2023, this study obtained the length and structure data of the shorelines in eight periods by manual visual interpretation. DSAS (Digital Shoreline Analysis System) and other methods were also used to calculate indices such as EPR (End Point Rate) and fractal dimension of the shorelines The results show that, during 33 years, the length of the shorelines increased 15.83 km, with an average growth rate of 0.48 km/y; the value of the intensity of change in the shorelines was 0.08%; the average EPR was 3.66 (m/y), and the artificiality index of the shorelines increased from 0.2895 to 0.4295; the greatest intensity of change was in the estuarine shorelines, with an intensity of change of −2.69%. The overall change in the fractal dimension of the shorelines was small, both between 1.0395 and 1.0673; the shorelines became slightly more curved. As far as the influencing factors are concerned, the influence of the natural environment is a long process, and human activities are more capable of changing the length and shape of the shorelines in a short period of time, with factors such as the degree of economic development having a greater impact on the shorelines.