Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

He, Keqi; Zhang, Yu; Li, Wenhong; Sun, Ge; McNulty, Steve

doi:10.3390/f13030411

Title: Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

Journal Article · Thu Mar 03 00:00:00 EST 2022 · Forests

DOI:https://doi.org/10.3390/f13030411· OSTI ID:1862819

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^[3]; McNulty, Steve ^[3]

Duke Univ., Durham, NC (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
USDA Forest Service, Research Triangle Park, NC (United States). USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1862819

Report Number(s):: LA-UR-22-21989

Journal Information:: Forests, Vol. 13, Issue 3; ISSN 1999-4907

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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