This study reports an inventory of marsh dieback events from spatial and temporal perspectives in the North Inlet-Winyah Bay(NIWB)estuary,South Carolina(SC).Past studies in the Gulf/Atlantic coast states have reported...This study reports an inventory of marsh dieback events from spatial and temporal perspectives in the North Inlet-Winyah Bay(NIWB)estuary,South Carolina(SC).Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned,leaving stubble of dead stems or mudflat with damaged ecosystem services.Reported marsh dieback in SC,however,have been limited.This study identified all marsh dieback events in the estuary since 1998.With 20 annually collected Landsat images,the Normalized Difference Vegetation Index(NDVI)series was extracted.A Stacked Denoising Autoencoder neural network was developed to identify the NDVI anomalies on the trajectories.All marsh dieback patches were extracted,and their inter-annual changes were examined.Results showed a continuous,spatially variable multi-year dieback event in 1998–2005,which aligned with the reported dieback in the early 2000s from other states.The identified patches mostly returned to normal within one year while the phenomenon reoccurred in other areas of the estuary during the prolonged dieback period.This study presents the first attempt to explore long-term dieback dynamics in an estuary using satellite time series.It provides valuable information in documenting marsh healthiness and environmental resilience on SC coasts.展开更多
基金supported by NASA EPSCoR Research Grant Program[Award#:NNX15AK53A]in South Carolina,USA.
文摘This study reports an inventory of marsh dieback events from spatial and temporal perspectives in the North Inlet-Winyah Bay(NIWB)estuary,South Carolina(SC).Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned,leaving stubble of dead stems or mudflat with damaged ecosystem services.Reported marsh dieback in SC,however,have been limited.This study identified all marsh dieback events in the estuary since 1998.With 20 annually collected Landsat images,the Normalized Difference Vegetation Index(NDVI)series was extracted.A Stacked Denoising Autoencoder neural network was developed to identify the NDVI anomalies on the trajectories.All marsh dieback patches were extracted,and their inter-annual changes were examined.Results showed a continuous,spatially variable multi-year dieback event in 1998–2005,which aligned with the reported dieback in the early 2000s from other states.The identified patches mostly returned to normal within one year while the phenomenon reoccurred in other areas of the estuary during the prolonged dieback period.This study presents the first attempt to explore long-term dieback dynamics in an estuary using satellite time series.It provides valuable information in documenting marsh healthiness and environmental resilience on SC coasts.
