Assessing the efficacy of NDVI and PRI near surface spectral reflectance sensors in coastal dune environments

Keywords: coastal geomorphology; aeolian; vegetation; instrument validation, NDVI
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 03:40 PM (Eastern Time (US & Canada)) - 2/28/2022 05:00 PM (Eastern Time (US & Canada))
Room: Virtual 39

Abstract

Coastal dunes are the analog result of aeolian transport and depositional processes. Roughness elements are a vital system component that yields momentum extraction from the wind, often initiating sediment deposition. Dune vegetation is a dynamic roughness element that changes dimension in response to wind speed, seasonal growth, and stress from punctuated disturbances (i.e. overwash burial, trampling, and root desiccation). Quantitative indices including normalized difference vegetation index (NDVI) and photochemical reflective index (PRI) have been developed to quantify changes in vegetation structure and health, respectively. While these remote sensing approaches have been widely utilized across scientific disciplines, few studies have extended their application to the coastal environment.

The objective of this study was to assess the near surface spectral reflectance sensors (SRS) for use within the coastal dune environment. METER NDVI and PRI SRS sensors were validated to test instrument sensitivity to variables commonly encountered in coastal field experiments: grain size, vegetation coverage, and moisture. Data logger channel variability and instrument deployment height was also tested. Results show that instrument variability occurs in < 3% of the operational index range, building confidence for future sensor deployments in coastal dune environments.