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 (δ¹³C and δ¹⁵N) 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, δ¹³C and δ¹⁵N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set.

Results and discussion

The results showed that the data of TC and δ¹³C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ¹³C data. The model, however, was fitted well to TN and δ¹⁵N. The cross-validation R² _cv of the models for TN and δ¹⁵N were 0.74 and 0.67 with the RMSE_cv of 0.53% and 1.07‰, respectively. The external validation R² _ex of the prediction was 0.64 and 0.67, and the RMSE_ex was 0.53% and 1.19 ‰, for TN and δ¹⁵N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ¹⁵N, showing that the models were reliable for the prediction of TN and δ¹⁵N in new forest leaf litterfall samples.

Conclusions

The PLSR model was not successful in predicting TC and δ¹³C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ¹⁵N values in the external litterfall samples were reliable, and PLSR can be used for future prediction.