Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Abstract

This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

“…Preprocessing is widely employed to eliminate or reduce unwanted variations in raw spectral data, such as noise bands, light contamination, and scattering effects [18]. In this study, three preprocessing methods were utilized: multiplicative scatter correction (MSC) to address the scattering effects in the spectra, standard normal variate (SNV) for drift issues, and first difference (1D) for baseline drift [19]. In addition to each of these methods, a combination (MSC + SNV + 1D) method was employed, and their respective impact on enhancing the model performance was ascertained.…”

Growing Stock Volume Estimation for Daiyun Mountain Reserve Based on Multiple Linear Regression and Machine Learning

“…They showed that the canopy density exerted the most significant effect on the GSV. Meng et al [13] investigated the relationships between the GSV and each of the infrared and near-infrared bands and discovered strong correlations. Liao et al [14] argued that textural features could significantly improve the estimation accuracy of the GSV, particularly for high-resolution images of complex forest structures.…”

Mechanical Property Prediction of Larix gmelinii Wood Based on Vis-Near-Infrared Spectroscopy

“…Traditional wood mechanics testing is destructive and not suitable for rapid testing in online production due to lengthy testing times, harsh conditions, poor stability and reproducibility. As a green, fast and non-destructive testing method, visible-near-infrared spectroscopy (Vis-NIRS) has been widely used in agriculture, petrochemicals, life science and other fields [10,11]. Due to its advantages of a simple operational process, accurate analysis, strong model generalization ability, and real-time online monitoring, Vis-NIRS can meet the needs of rapid and accurate determination of large numbers of samples in an area [12].…”