The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples

Purpose The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (delta C-13 and delta N-15) in forest leaf litterfall samples. Materials and methods Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400-1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, delta C-13 and delta N-15 in the litterfall samples. The most important wavelengths were selected using beta coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set. Result and discussion The results showed that the data of TC and delta C-13 could not be fitted to the PLSR model, possibly due to small variations observed in the TC and delta C-13 data. The model, however, was fitted well to TN and delta N-15. The cross-validation R-2 (cv) of the models for TN and delta N-15 were 0.74 and 0.67 with the RMSEcv of 0.53% and 1.07aEuro degrees, respectively. The external validation R-2 (ex) of the prediction was 0.64 and 0.67, and the RMSEex was 0.53% and 1.19 aEuro degrees, for TN and delta N-15, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and delta N-15, showing that the models were reliable for the prediction of TN and delta N-15 in new forest leaf litterfall samples. Conclusion The PLSR model was not successful in predicting TC and delta C-13 in forest leaf litterfall samples using hyperspectral data. The predictions of TN and delta N-15 values in the external litterfall samples were reliable, and PLSR can be used for future prediction.