Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm² by 2022, and by 2023, only 0.55 hm² remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health.

The northward expansion of Spartina alterniflora (S. alterniflora) poses a profound ecological threat to coastal ecosystems and biodiversity along China’s coastline. This invasive species exhibits strong adaptability to colder climates, facilitating its potential spread into northern regions and underscoring the urgent need for a nuanced understanding of its spatial distribution and invasion risks to inform evidence-based ecosystem management strategies. This study employed multi-temporal Sentinel-1/2 imagery (2016–2022) to map and predict the spread of S. alterniflora in Bohai Bay. An object-based random forest classification achieved an overall accuracy above 92% (κ = 0.978). Over the six-year period, the S. alterniflora distribution decreased from 46.60 km² in 2016 to 12.56 km² in 2022, reflecting an annual reduction of approximately 5.67 km². This decline primarily resulted from targeted eradication efforts, including physical removal, chemical treatments, and biological competition strategies. Despite this local reduction, MaxEnt modeling suggests that climate trends and habitat suitability continue to support potential northward expansion, particularly in high-risk areas such as the Binhai New District, the Shandong Yellow River Delta, and the Laizhou Bay tributary estuary. Key environmental drivers of S. alterniflora distribution include the maximum temperature of the warmest month, mean temperature of the wettest quarter, isothermality, sea surface temperature, mean temperature of the warmest quarter, and soil type. High-risk invasion zones, covering about 95.65 km². These findings illuminate the spatial dynamics of S. alterniflora and offer scientific guidance for evidence-based restoration and management strategies, ensuring the protection of coastal ecosystems and fostering sustainable development.

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved superior extraction accuracy (Kappa > 0.9, overall accuracy 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4,055.06 hm² to 6,105.50 hm², primarily in tidal flats and water bodies. A clearing project reduced its extent to 5,063.62 hm² by 2022, and by 2023, only 0.55 hm² remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species' resilience. Continuous monitoring and maintenance are essential to prevent resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health.