A simple radiometric correction model to improve automatic mapping of vegetation from multispectral satellite data (Remote Sensing of Environment, 1994)
A simplified model for radiometric corrections has been used to improve nonsupervised classification of vegetation cover in a hilly area near Barcelona, Spain. A digital elevation model and standard parameters for exoatmospheric solar irradiance, atmospheric optical depth, and sensor calibration are the only inputs required. Radiometric classes obtained by cluster classification of Landsat TM images from nonradiometrically corrected images include several classes related to terrain illumination, but not to vegetation or thematic cover differences. The use of radiometric correction allows identifying all radiometric classes obtained as vegetation or thematic classes with 83.3% global accuracy. Classes obtained include Pinus halepensis, Quercus ilex, and Quercus cerrioides forests, shrublands, grasslands, urban areas with vegetation, urban areas without vegetation, and denuded areas. Radiometric correction helps in estimating surfaces and spectral features of these classes. The results are discussed considering botanical composition, date (phenology), and vegetation dynamics.