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Imaging Spectroscopy Applications for Mapping and Monitoring Environmental Change in Louisiana’s Coastal Wetlands

  • Author(s): Jensen, Daniel John
  • Advisor(s): Cavanaugh, Kyle C
  • et al.
Abstract

Louisiana’s coastal wetlands are continually threatened by anthropogenic disturbances and relative sea level rise (RSLR), factors which have caused widespread degradation and loss. This region has historically been a productive ecosystem with high carbon sequestration capacity that offers important ecological benefits to neighboring communities. The region’s “blue carbon” stocks and ecosystem services are now severely degrading with the coastal wetlands’ ongoing submergence resulting from RSLR. Accretion is the trapping of sediments and deposition of organic matter resulting in the buildup of the soil surface, and wetlands will submerge if the factors governing accretion do not sufficiently account for RSLR. Remote sensing offers data that can be applied to wetland ecology to assess ecological processes relating to RSLR and accretion at a regional scale. Imaging spectroscopy, specifically, enables sophisticated modeling techniques that may improve remote sensing applications in wetland regions. This project applies imaging spectroscopy to Louisiana’s coastal wetlands, developing applications for Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data that include image processing algorithms, environmental parameter retrievals, and assessment of vegetation growth patterns and changes. The study’s first chapter addresses image processing methods for correcting bidirectional reflectance distribution function (BRDF) effects that inhibit image mosaicking. We developed a new empirical algorithm—the adaptive reflectance geometric correction—and applied it to imagery collected around the Atchafalaya and Wax Lake deltas to produce optimized surface reflectance mosaics. The second chapter sees the estimation and mapping of hydrologic suspended solids in from those AVIRIS-NG mosaics with an independently validated and transferable algorithm. The third chapter integrates the AVIRIS-NG data with simultaneously collected data from the Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) to estimate aboveground biomass (AGB) in Louisiana’s Wax Lake Delta (WLD). The fourth chapter uses the AVIRIS-NG data to map the WLD’s vegetation species distribution, then compares the results to published data from five years prior to assess extent changes for key vegetation types. It further employs the AGB dataset to examine key species’ growth patterns across elevational gradients and zones. The methods and results developed herein will enable future efforts to model accretion and predict wetland loss at regional scales.

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