Spatiotemporal analysis was conducted to reveal long-term distribution patterns of wetland vegetation within the protected area from 1990 to 2022.

• Figure 1A illustrates changes in vegetation spatial patterns over time.
• Figure 1B presents percentage vegetation cover along the sea–land gradient.

Analytical tools such as landscape pattern indices, migration models, and expansion–contraction dynamics were used to quantify ecological changes.

Key Findings

• Spartina alterniflora exhibited high spatial aggregation but showed a declining trend over time.
• Phragmites australis and Suaeda salsa displayed greater fragmentation and increasing spatial coverage.
• Vegetation migration exhibited significant heterogeneity and a clear banded distribution along the land–sea gradient.

GBF Alignment: Aligns with GBF Target 2.
Contribution: Measurable outcomes enhance restoration planning, filling gaps in uniform management approaches.

A comprehensive geospatial database was developed, integrating vegetation cover data derived from remote sensing with key environmental, climatic, and anthropogenic variables. Included metrics encompassed soil salinity, sea surface temperature, seawater salinity, and locations of aquaculture ponds, providing a robust analytical foundation.

GBF Alignment: Supports GBF Target 21.
Contribution: Integrates diverse data layers for holistic analysis, adding value to fragmented conservation datasets.

Vegetation index time series were smoothed using Gaussian fitting to reduce noise and extract key phenological features. A random forest deep learning algorithm was applied to classify wetland vegetation into three dominant types: Spartina alterniflora, Phragmites australis, and Suaeda salsa. Classification accuracy from 1990 to 2022 was validated through field surveys.

GBF Alignment: Contributes to GBF Target 6.
Contribution: Reduces invasive species impact by accurately identifying Spartina alterniflora for targeted control, addressing a key biodiversity threat.

Using the Google Earth Engine (GEE) platform, Landsat series remote sensing data from 1990 to 2022 were systematically acquired, encompassing TM5, ETM+ (Landsat 7), OLI (Landsat 8), and OLI (Landsat 9) sensors. To ensure data quality for subsequent analyses, key spectral bands—near-infrared (NIR), red, and green—were selected and fused.

GBF Alignment: Supports GBF Target 21.
Contribution: Enhances decision-making with real-time, validated datasets, adding value to existing conservation efforts through technological innovation.

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 a case study in the Yangtze River Delta Pilot Site and included in the support of major research projects on oceanography by the National Natural Science Foundation (NSFC).

By systematically integrating remote sensing data, deep learning, and ecological analysis, the project has significantly advanced wetland conservation methodologies, offering scalable solutions for biodiversity preservation,  biological invasion control,  and ecosystem management globally.

Exploring and analysing the key drivers of vegetation spatial and temporal distribution is of practical significance for monitoring the expansion of coastal wetland vegetation, species diversity conservation and sustainable management of coastal wetlands.

• 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.

Using spatial-temporal data, the long-term distribution characteristics of wetland vegetation were analyzed. 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.

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.

A Gaussian function was applied to vegetation index time series to extract candidate features, while a deep learning algorithm identified three major vegetation types (Spartina alterniflora, Phragmites australis, and Suaeda salsa). Field validation confirmed the model's accuracy, enabling precise vegetation classification from 1990 to 2022.

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.