Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods

Classifications of coniferous forest stands regarding tree species and age classes were performed using hyperspectral remote sensing data (HyMap) of a forest in western Germany. Spectral angle mapper (SAM) and maximum likelihood (ML) classifications were used to classify the images. Classification was performed using (i) spectral information alone, (ii) spectral information and stem density, (iii) spectral and textural information, (iv) all data together, and results were compared. Geostatistical and grey level co-occurrence matrix based texture channels were derived from the HyMap data. Variograms, cross variograms, pseudo-cross variograms, madograms, and pseudo-cross madograms were tested as geostatistical texture measures. Pseudo-cross madograms, a newly introduced geostatistical texture measure, performed best. The classification accuracy (kappa) using hyperspectral data alone was 0.66. Application of pseudo-cross madograms increased it to 0.74, a result comparable to that obtained with stem density information derived from high spatial resolution imagery.