Satellite-based salt marsh elevation, vegetation height, and species composition mapping using the superspectral WorldView-3 imagery

摘要

Very high resolution (VHR) space-borne data are needed to finely and continuously map salt marshes. The WorldView-3 (WV-3) sensor leverages one panchromatic, eight optical, and eight shortwave infrared (SWIR) bands at 0.31, 1.24, and 7.5m pixel size, respectively. Although eight optical bands have been previously pansharpened, no attempt to use the 16-band superspectral data set at VHR (0.31m) has been yet reviewed. Here, we propose to reliably pan-sharpen the 16 WV-3 predictors so as to model (artificial neural network, ANN) salt marsh elevation and vegetation height and classify species composition at VHR using calibration/validation handheld vegetation height, airborne lidar elevation, and drone blue-green-red (BGR) responses. Three models have been created over a megatidal bay (Beaussais Bay, Brittany, France) provided with mud flats, salt marshes, and polders. VHR-screened WV-3 bands very satisfactorily predicted salt marsh elevation and vegetation height responses (r=0.86, R-2=0.71, root mean square error (RMSE)=0.33m and r=0.88, R-2=0.77, RMSE=5.72 cm, respectively). The WV-3 superspectral data set outperformed the eight-band multispectral and four-band traditional data sets to classify 15 salt marsh habitats (OA=95.47, 82.33, and 69.27%, respectively). Adding WV-3-based salt marsh elevation and vegetation height augmented the 15-class classification of the superspectral and traditional data sets (OA=97.60 and 77.47%, respectively), but not for the multispectral one (OA=81.93%).