G. González Trilla, P. Pratolongo, M.E. Beget, P. Kandus, J. Marcovecchio, C. Di Bella
Journal of Coastal Research 29 (1), 231-238, (1 January 2013) https://doi.org/10.2112/JCOASTRES-D-11-00214.1
KEYWORDS: Coastal marshes, spectral indices, remote sensing, biomass, LAI, canopy cover, biophysical parameters, Spartina alterniflora
González Trilla, G.; Pratolongo, P.; Beget, M.E.; Kandus, P.; Marcovecchio, J., and Di Bella, C., 2013. Relating biophysical parameters of coastal marshes to hyperspectral reflectance data in the Bahia Blanca Estuary, Argentina.
Salt marshes occupying the tidal fringe of estuaries and protected coasts provide valuable ecosystem services, and remote sensing is a powerful tool for their large-scale monitoring. However, in order to apply remote sensing techniques to evaluate the ecological state of salt marshes, a deeper understanding is needed about the interactions between field biophysical parameters and the sensor's reflectance. The main objective of this work is to analyze and quantify the influence of different biophysical parameters characterizing stands of Spartina alterniflora marshes in the Bahia Blanca Estuary, Argentina, on their spectral response. Spectral reflectance at high resolution was measured in S. alterniflora canopies under natural conditions, manipulating standing biomass by means of successive harvestings. Reflectance data were acquired using a FieldSpec® spectroradiometer, which measures in the visible, near-infrared, and shortwave-infrared spectral bands. Based on these reflectance data, spectral indices such as the normalized difference vegetation index (NDVI) were calculated for each biomass condition. Biomass, leaf area index (LAI), percent canopy cover (PCC), water content, and soil properties were also evaluated. LAI, PCC, and biomass were positively correlated between each other. As a general trend, as biomass decreased, absorption in red wavelengths decreased and reflectance in near-infrared increased. Several indices explained the variability in LAI, biomass, and PCC. For example, NDVIRouse had a positive regression with PCC (R2 = 0.80, N = 75) and LAI (R2 = 0.67, N = 75). Results indicate that LAI, biomass, and PCC of Spartina alterniflora could be accurately determined from spectral data.