Interactions among ecosystem services (ESs) involve tradeoffs and synergies. Quantitatively studying the trade-off and synergistic relationships between land use/land cover change (LULC) and ESs enables the precise identification of the quality status and driving factors of ESs within the region, which is crucial for rational resource allocation and environmental protection. In this study, the spatial and temporal change characteristics of the three ESs of carbon storage (CS), soil retention (SR) and habitat quality (HQ) are explored by using the InVEST model and GIS technology in the region around Taihu Lake, and the tradeoffs and synergies among the three are determined based on the difference comparison. The results indicate that: (1) The study area has a downward trajectory in CS and HQ from 1990 to 2020, while SR experiences some fluctuations. The spatial distribution of the three ESs exhibits high levels in the southwest and low levels in the northeast. (2) The most sensitive regions where tradeoffs and synergies are most pronounced occur primarily in the newly construction land regions and the southwestern mountainous and hilly areas. In newly construction land regions, there are often tradeoffs relationships observed between CS and SR, as well as between HQ and SR. Conversely, a predominantly negative synergy is mainly observed between CS and HQ. In the southwestern hilly terrain, due to changes in landscape patterns, HQ and SR exhibit higher levels of negative synergistic relationships. (3) LULC is a significant driver of spatial and temporal changes in ESs, as well as changes in tradeoffs and synergies in the study area, necessitating integrated research from economic, social and climate change perspectives.

Symmetries and symmetry-breaking play significant roles in data security. While remote sensing images, being extremely sensitive geospatial data, require protection against tampering or destruction, as well as assurance of the reliability of the data source during application. In view of the increasing complexity of data security of remote sensing images, a single watermark algorithm is no longer adequate to meet the demand of sophisticated applications. Therefore, this study proposes a dual watermarking algorithm that considers both integrity authentication and copyright protection of remote sensing images. The algorithm utilizes Discrete Wavelet Transform (DWT) to decompose remote sensing images, then constructs integrity watermark information by applying Chebyshev mapping to the mean of horizontal and vertical components. This semi-fragile watermark information is embedded into the high-frequency region of DWT using Quantization Index Modulation (QIM). On the other hand, the robust watermarking uses entropy to determine the embedding position within the DWT domain. It combines the stability of Singular Value Decomposition (SVD) and embeds the watermark according to the relationship between the singular values of horizontal, vertical, and high-frequency components. The experiment showed that the proposed watermarking successfully maintains a high level of invisibility even if embedded with dual watermarks. The semi-fragile watermark can accurately identify tampered regions in remote sensing images under conventional image processing. Moreover, the robust watermark exhibits excellent resistance to various attacks such as noise, filtering, compression, panning, rotating, and scaling.