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We analysed stability and predictive capabilities of known nitrogen absorption features between plant material prepared for NIRS (dried) and RS (fresh) studies. Grass spectra were taken of the plant canopy, and again after the grass sample was dried and ground. Models were derived using stepwise multiple linear regression (sMLR). Regression values (adj.r2) produced using the dried material were greater than those produced using canopy material. For dried material only wavebands from the SWIR region were selected. Wavebands selected by sMLR on canopy material were located in both the VNIR and SWIR regions. Using wavebands selected for dried material models produced low adj.r2 values when applied to canopy plant material; differences in adj.r2 values are smaller when wavebands selected in canopy material models are applied to dried material. Widening of nitrogen features produced higher adj.r2 values for both dried and canopy material. This work shows that obtaining models with high predictive capabilities for nitrogen concentration is possible, but waveband selection should not be limited to features identified by NIRS studies. To accommodate for variability in absorption features, and instrument errors, absorption features should be widened. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]