Monitoring of wetland inundation dynamics in the Delmarva Peninsula using Landsat time-series imagery from 1985 to 2011

Huiran Jin, Chengquan Huang, Megan W. Lang, In Young Yeo, Stephen V. Stehman

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Wetlands provide important ecosystem services, the provision of which is largely controlled by fluctuations in inundation and soil saturation. Inundation is highly dynamic and can vary substantially through time in response to multiple drivers, including precipitation and evapotranspiration. This research focused on developing a practical and effective framework for regional, long-term monitoring of wetland inundation dynamics using airborne LiDAR intensity data (Lang et al., 2013) and Landsat time-series imagery. Subpixel water fraction (SWF) maps indicating the percent of surface water within each 30-m pixel were generated on an annual basis over the entire Delmarva Peninsula on the East Coast of the United States from 1985 to 2011. Comprehensive accuracy assessments of the SWF maps were conducted using historical high-resolution aerial photography to determine the reference condition. The assessment resulted in an estimated root mean square error (RMSE) of 7.78% for the sample of open water areas (mean SWF was ~ 40% for this region of the map). Moreover, a separate accuracy assessment targeting inundation in wetlands (i.e. presence or absence of water) yielded an overall accuracy of 93%. Accuracies derived indicated that Landsat data can be calibrated to accurately extract long-term water information at the regional scale. Characteristics of inundation were examined with respect to different wetland types defined by water regime and dominant vegetation types, as well as different physical drivers. Results showed that tidal wetlands typically exhibited more intensive inundation than nontidal wetlands, and a higher degree of inundation was associated with emergent wetlands compared to wetland areas dominated by woody vegetation. Analysis of change drivers revealed that tide exerted a statistically significant influence on coastal inundation with r2 values of 32–36% and p < 0.01, whereas inundation changes in inland wetland areas were in part driven by precipitation with r2 values of 25–34% and p < 0.08. Because an up-to-date archive of Landsat imagery is globally available and LiDAR data are becoming increasingly more affordable, the developed framework can be easily implemented to generate a continuous inundation record in many regions of the globe to assist in ongoing and future studies focused on wetland hydrology and wetland management.

Original languageEnglish (US)
Pages (from-to)26-41
Number of pages16
JournalRemote Sensing of Environment
Volume190
DOIs
StatePublished - Mar 1 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Keywords

  • Accuracy assessment
  • Delmarva
  • Interannual change
  • Inundation mapping
  • Landsat time-series
  • Subpixel water fraction (SWF)
  • Wetlands

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