Inclusive and Participatory Research on environmental culture and CEPA's efforts
Biodiversity Conservation Activities with the Participation of Islanders
Capacity Building through an Environmental Culture Approach
Academic Communication
Project results were disseminated via an academic paper in Ocean-Land-Atmosphere Research (a Science Partten Journal) and shared in AAASScience WeChat Public (Official Media of American Association for the Advancement of Science in China). The findings were also included as acase studyin the Yangtze River Delta Pilot Site and included in the support of major research projects on oceanography by the National Natural Science Foundation (NSFC).
Communicating challenges and solutions in academic and public platforms expanded the reach and impact of the project.
Results were systematically presented to stakeholders, increasing awareness and adoption potential.
Open dissemination enhances collaboration and knowledge-sharing across disciplines.
Publishing actionable insights in both academic and public domains accelerates the solution's adoption by conservationists worldwide.
Drivers like marine environmental factors (e.g., salinity, wave height) and anthropogenic influences (e.g., aquaculture) were analyzed using a Generalized Additive Model (GAM) to explore their relationships with vegetation evolution.
Spartina alterniflora was influenced by marine factors (e.g., salinity and wave height).
Phragmites australis and Suaeda salsa were driven by rainfall, anthropogenic activities, and interspecific competition.
Understanding these factors supports better management of invasive species and promotes biodiversity conservation.
GAM effectively modeled non-linear relationships between vegetation data and drivers, ensuring robust analysis.
Integrating environmental and anthropogenic datasets enriched the accuracy of driver identification.
Continuous data supplementation and refinement of analytical models are necessary for long-term reliability.
Mechanistic exploration of drivers is essential for predictive and adaptive conservation strategies.
Spatial and Temporal Characteristics Analysis of Wetland Vegetation
Vegetation classification results based on the reconstruction of phenological feature time series.
Nanjing Normal University
Using spatial-temporal data, the long-term distribution characteristics of wetland vegetation were analyzed. The results of the study show the evolution of vegetation in the protected area both spatially and temporally. Figure 1A shows the spatiotemporal patterns of vegetation distribution from 1990 to 2022. Figure 1b shows the observed percent vegetation cover along the sea–land gradient from 1990 to 2022. Tools such as the landscape pattern index, migration modeling, and expansion/decline dynamics identified distinct trends:
Spartina alterniflora patches showed high aggregation and a decreasing trend.
Phragmites australis and Suaeda salsa patches displayed lower aggregation, higher fragmentation, and increasing trends.
Vegetation migration patterns revealed significant spatial and temporal heterogeneity, with vegetation distributed in bands along the terrestrial gradient.
Models such as center-of-mass migration and dynamism indices quantified vegetation movement and density changes.
Landscape pattern indices captured fragmentation and aggregation metrics.
Temporal and spatial heterogeneity of vegetation dynamics require multi-faceted analytical approaches.
A comprehensive database was created, integrating remote sensing-derived vegetation cover with key environmental, climate, and human activity data. This includes metrics such as soil salinity, sea surface temperature, seawater salinity, and aquaculture pond locations, forming a robust foundation for further analyses.
Field verification ensured the accuracy of remote sensing interpretations (see Figures 1 and 2).
Database construction enabled integration of spatial data with environmental drivers, forming the foundation for robust ecological analyses.
Reliable field data are critical for validating remote sensing outputs.
A well-structured database improves analytical efficiency and supports multi-variable correlation studies.
Using a Gaussian function, vegetation index time series were fitted to reduce noise and extract key features. A random forest deep learning algorithm classified wetland vegetation into three types (Spartina alterniflora, Phragmites australis, Suaeda salsa). Field validation confirmed classification accuracy from 1990–2022.
Noise in raw vegetation index curves was minimized through Gaussian fitting, improving classification accuracy.
Random forest algorithms amplified interspecies spectral variance, enabling reliable feature extraction and identification.
Spectral features (e.g., vegetation moisture and structure) were crucial for increasing interspecies variance and classification precision.
Curve fitting and noise reduction significantly improved the accuracy of temporal analyses, highlighting the importance of preprocessing raw data.
Using the Google Earth Engine (GEE) platform, Landsat TM/OLI series remote sensing data from 1990 to 2022 were collected, covering TM5, ETM+7, OLI8, and OLI9. Key spectral bands (near-infrared, red, and green light) were fused to ensure high-quality data for subsequent analysis.
Remote sensing images with ≤10% cloud cover were selected for radiometric and atmospheric corrections through batch processing.
Thematic information extraction leveraged band combinations that emphasized vegetation characteristics (e.g., NIR bands for vegetation reflectance peaks).
Limitations in resolution (spatial, temporal, and spectral) can introduce errors, necessitating robust corrections (radiometric/geometric).
Data fusion between satellites was crucial for achieving consistent long-term datasets, but this step required additional processing to align temporal and spatial resolutions.
