Assessment of nitrogen status and vegetation composition in tidal freshwater marshes using partial least squares regression models of hyperspectral canopy reflectance

Abstract

Hyperspectral canopy reflectance was used to predict sub-surface water nutrients, vegetation composition, and canopy nutrients, which could lead to more useful means for assessing the status of wetlands. Thirty field quadrats at two tidal freshwater marsh sites on the Nanticoke River (Maryland) were treated with five nitrogen levels. During the 2004-05 growing seasons, hyperspectral canopy reflectance was measured using a spectroradiometer with 1nm resolution across the visible and near - infrared spectrum (350-1075 nm), water samples were collected using lysimeters, species cover was quantified, and biomass was collected and analyzed for canopy nutrients. ANOVA was used to determine whether nitrogen affected reflectance, species composition, canopy N and P, and partial least squares regression was used to develop reflectance models predictive of these ecosystem properties. Results indicated that hyperspectral radiometry could be used as a remote sensing tool for quantifying sub-surface water nitrogen, vegetation composition, and canopy nutrients in tidal freshwater marshes.