The overwhelming spread of Spartina alterniflora (smooth cordgrass) over recent decades has put many native plant communities and coastal environments at risk. Therefore, longterm monitoring of S. alterniflora dynamics is necessary to better understand and manage the invasion of the species. However, it is difficult to map Spartina saltmarshes in China on an annual or multiyear epoch basis. To address this issue, we developed a classification approach integrating Google Earth Engine (GEE) and object-based hierarchical random forest (RF) classification, and we applied this approach to quantify the expansion and dieback of S. alterniflora at Dafeng Milu National Nature Reserve, Jiangsu, China during 1993-2020. Results showed that the area of S. alterniflora expanded from 24.48 ha in 1993 to 1,564.96 ha in 2010. However, after ecological hydrological engineering and an increase in Elaphures davidianus (Pè re David's deer) numbers in 2011, the S. alterniflora area decreased significantly to 944.28 ha in 2020. During 2011-2020, the S. alterniflora area decreased substantially at a rate of 67 ha per year and by 86% in one area studied in Dafeng Milu National Nature Reserve. In 2020, the 944.28 ha of S. alterniflora in the reserve was mainly distributed in mudflats by the sea. Overall, these results show that it is feasible to identify S. alterniflora using the GEE platform and object-based hierarchical RF classification; moreover, this approach could improve understanding and management of this invasion species.
Saltmarsh carbon storage contributes significantly to combating global climate change and achieving regional carbon neutrality. Yet saltmarsh carbon stocks have shown a trend of decline in recent years. Therefore, long-term monitoring and analyzing of saltmarshes for their carbon storage is imperative to better protect and manage this pool of carbon. This study investigated the spatiotemporal dynamics in saltmarsh carbon storage during 1987–2020, by using the Google Earth Engine (GEE) platform and applying the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and analyzed the driving factors of carbon storage in saltmarshes. The key results are as follows. Firstly, carbon density values in saltmarshes ranged more than 14-fold, from 7.24 to 104.99 Mg·hm-2, and the total carbon storage showed a decreasing trend. Secondly, reduced carbon storage was concentrated in inshore saltmarshes adjacent to reclamation sites, especially in Shandong, whereas augmented carbon storage characterized the offshore saltmarshes dominated by Spartina alterniflora, especially in Shanghai and Jiangsu. Overall, the carbon stocks of saltmarshes have fallen by 10.44 Tg; the decrease in carbon storage caused by Suaeda salsa, Phragmites australis, and mudflats exceeded the increase in carbon storage caused by Spartina alterniflora and Scirpus mariqueter. Further, we found that reclamation was the most dominant driver of carbon storage reductions, except for sea level rise and hurricane disturbances that can also negatively impact carbon storage, while greater carbon storage was closely related to the invasion of Spartina alterniflora. This study’s findings facilitate the development of a carbon storage management strategy for saltmarsh ecosystems to address global climate change and contribute to attaining carbon neutrality.
The invasion and expansion of Spartina alterniflora in coastal salt marsh wetlands have greatly affected the material cycle of the ecosystem. A total of 372 topsoil samples were collected from 124 sites representing two land-cover types by implementing an unprecedented high sampling density study in the Dafeng Milu National Nature Reserve. Classical statistics and geostatistics were used to quantify soil organic carbon (SOC) and total nitrogen (TN) spatial distribution. Redundancy analysis (RDA) was used to detect correlations between environmental factors, SOC, and TN. The results showed that SOC and TN have moderate variability. The spatial distributions of SOC and TN were similar, and the highest values were observed in the southwest of the study area. In different land cover types, the SOC and TN in the vegetation coverage areas with Spartina alterniflora as the dominant species were significantly higher than those in bare land. RDA showed that TN and aboveground biomass significantly affected the spatial distribution of SOC, while SOC and AGB dominated the spatial distribution of TN.
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