Abstract
Sea-level rise and climate change stresses pose increasing threats to coastal wetlands that are vital to wildlife habitats, carbon sequestration, water supply, and other ecosystem services with global significance. However, existing studies are limited in individual sites, and large-scale mapping of coastal wetland degradation patterns over a long period is rare. Our study developed a new framework to detect spatial and temporal patterns of coastal wetland degradation by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data. Then, this framework was tested to track the degradation of coastal wetlands at the Alligator River National Wildlife Refuge (ARNWR) in North Carolina, United States, during the period from 1995 to 2019. We identified six types of coastal wetland degradation in the study area. Most of the detected degradation was located within 2 km from the shoreline and occurred in the past five years. Further, we used a state-of-the-art coastal hydrologic model, PIHM-Wetland, to investigate key hydrologic processes/variables that control the coastal wetland degradation. The temporal and spatial distributions of simulated coastal flooding and saltwater intrusion confirmed the location and timing of wetland degradation detected by remote sensing. The combined method also quantified the possible critical thresholds of water tables for wetland degradation. The remote sensing–hydrologic model integrated scheme proposed in this study provides a new tool for detecting and understanding coastal wetland degradation mechanisms. Our study approach can also be extended to other coastal wetland regions to understand how climate change and sea-level rise impact wetland transformations.
Highlights
Coastal wetlands provide critical ecological functions and services for coastal communities, such as clean water and blue carbon storage, soil erosion mitigation, biodiversity conservation, and wildlife habitat provision
This study demonstrates that a remote sensing-hydrologic model integrated scheme can effectively identify coastal wetland degradation at multiple scales and improve understanding of the relationship between coastal hydrology and vegetation dynamics
We proposed a new framework by analyzing seasonal Normalized Difference Vegetation Index (NDVI) time series to identify when and where coastal wetland degradation occurred
Summary
Coastal wetlands provide critical ecological functions and services for coastal communities, such as clean water and blue carbon storage, soil erosion mitigation, biodiversity conservation, and wildlife habitat provision. Despite their importance, coastal wetlands represent the most endangered ecosystems under a warming climate [1]. Hopfensperger et al [13] showed that drought-induced saltwater intrusions sped coastal wetland deterioration rates in the Mississippi River Deltaic Plain. Due to their low-lying nature, coastal wetlands are exceptionally vulnerable to SLR besides more frequent floods and droughts [14,15]. Landward saltwater migration driven by SLR can significantly impact the survival and productivity of freshwater-dependent coastal plants [7]
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