4.7 Article

Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing

期刊

REMOTE SENSING OF ENVIRONMENT
卷 258, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112385

关键词

Saltwater intrusion; Sea level rise; Coastal floodplain swamps; Remote sensing; EVI; MODIS; Climate change; Google Earth Engine; Ghost Forest

资金

  1. UF Graduate Student Fellowship
  2. Glick Graduate Scholarship
  3. Melnick Scholarship Award

向作者/读者索取更多资源

Remote sensing data can be used to detect differences in coastal floodplain swamps (CFS) vegetation associated with long-term, low-level saltwater intrusion (SWI), with hypotheses analyzing summary statistics showing significant differences between impacted and unimpacted sites. However, further methodological advancements are needed to reliably detect the temporal transition process of CFS undergoing SWI.
Coastal floodplain swamps (CFS) are an important part of the coastal wetland mosaic, however they are threatened due to accelerated rates of sea level rise and saltwater intrusion (SWI). While remote sensing-based detection of wholesale coastal ecosystem shifts (i.e., from forest to marsh) are relatively straightforward, assessments of chronic, low-level SWI into CFS using remote sensing have yet to be developed and can provide a critical early-warning signal of ecosystem deterioration. In this study, we developed nine ecologically-based hypotheses to test whether remote sensing data could be used to reliably detect the presence of CFS experiencing SWI. Hypotheses were motivated by field- and literature-based understanding of the phenological and vegetative dynamics of CFS experiencing SWI relative to unimpacted, control systems. Hypotheses were organized into two primary groups: those that analyzed differences in summary measures (e.g., median and distribution) between SWI-impacted and unimpacted control sites and those that examined timeseries trends (e.g., sign and magnitude of slope). The enhanced vegetation index (EVI) was used as a proxy for production/biomass and was generated using MODIS surface reflectance data spanning 2000 to 2018. Experimental sites (n = 8) were selected from an existing network of long-term monitoring sites and included 4 pairs of impacted/non-impacted CFS across the northern Gulf of Mexico from Texas to Florida. The four best-supported hypotheses (81% across all sties) all used summary statistics, indicating that there were significant differences in the EVI of CFS experiencing chronic, low-level SWI compared to controls. These hypotheses were tested using data across a large and diverse region, supporting their implementation by researchers and managers seeking to identify CFS undergoing the first phases of SWI. In contrast, hypotheses that assessed CFS change over time were poorly supported, likely due to the slow and variable pace of ecological change, relatively short remote sensing data record, and/or specific site histories. Overall, these results show that remote sensing data can be used to identify differences in CFS vegetation associated with long-term, low-level SWI, but further methodological advancements are needed to reliably detect the temporal transition process.

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