Diffuse Reflectance Spectroscopy to Advance Tree-Level NSC Analysis

Projecting future forest dynamics helps naturalists determine the overall health of a particular forest. Because trees are a major source of oxygen to the atmosphere, the survival and preservation of trees is essential to human health as well as the health of the overall ecosystem.

One way to monitor future forest dynamics is by measuring the non-structural carbohydrate (NSC) concentrations in trees (1). A NSC in trees offers a buffer between carbohydrate supply and demand, and helps trees fight off bouts of drought (2).

Looking up in a green forest. Trees with green leaves, blue sky and sun light. Bottom view background. | Image Credit: © Stephen Davies - stock.adobe.com

A recent study conducted by researchers from Germany, Hungary, and Colombia explored non-structural carbohydrate (NSC) estimation in trees. In their study, the scientist used a novel approach that combines near-infrared (NIR) and mid-infrared (MIR) spectroscopy with advanced data fusion techniques to enhance accuracy in NSC quantification (3).

NSCs, comprising soluble sugars and starches, are vital indicators of tree health, carbon cycling, and resilience under environmental stress (1–3). However, their effective measurement across various tissue types and environmental conditions has posed significant challenges. Traditional chemical analysis, though precise, is labor-intensive and limited in scalability (3). The researchers in the study sought to test spectroscopy-based methods to estimate NSC content to see if they can overcome barriers traditional chemical analysis brings.

The researchers analyzed 180 samples of leaves, roots, stems, and branches from 73 tree species across various biogeographic regions (3). Using diffuse reflectance spectroscopy, both NIR and MIR spectral data were evaluated for their ability to predict NSC content. Key to the study was the application of partial least squares regression (PLSR) combined with spectral variable selection techniques such as competitive adaptive reweighted sampling (CARS) (3). The benefit of using these methods is that the team was able to systematically assess and compare the prediction accuracy of different approaches (3).

NIR proved to be more effective than MIR, which was another question explored in the study. Per their findings, the NIR data outperformed MIR in NSC estimation, with a root mean square error (RMSE) of 2.58% of dry matter and an r² value of 0.64, compared to MIR’s RMSE of 2.90% and r² of 0.52 (3). However, when NIR and MIR data were fused using high-level data fusion methods, accuracy improved markedly. The RMSE dropped to 2.19%, and the r² increased to 0.72, demonstrating the complementary strengths of the two spectral domains (3).

The NIR and MIR modeling results allowed the researchers to develop an effective prediction model. Further refinement through spectral variable selection with the CARS algorithm pushed accuracy to unprecedented levels, with an RMSE of 1.97% and an r² of 0.78 (3).

The study also revealed tissue-specific variations in the effectiveness of spectral methods. High-level data fusion was particularly successful for leaf samples, whereas the NIR data combined with CARS proved most effective for woody tissues like branches and stems (3). This underscores the importance of tailoring analytical approaches to specific tissue types.

By providing a scalable and efficient method for NSC estimation, diffuse reflectance spectroscopy can enable more frequent and widespread monitoring of tree health and carbon dynamics, particularly under extreme environmental conditions (3). In the conclusion of the article, the authors suggested that future studies look at a larger number of samples per tissue type and selected tree species (3). The researchers believe that these efforts could fine-tune the sensitivity of diffuse reflectance spectroscopy for NSC retrieval and establish it as a reliable tool for large-scale ecological studies (3).

References

1. Ninomiya, H.; Kato, T.; Vegh, L.; Wu, L. Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0). Geosci. Model Dev. 2023, 16 (14), 4155–4170. DOI: 10.5194/gmd-16-4155-2023
2. Schiestl-Aalto, P.; Ryhti, K.; Makela, A.; et al. Analysis of the NASC Storage Dyanmics in Tree Organs Reveals the Allocation to Belowground Symbionts in the Framework of Whole Tree Carbon Balance. Front. For. Glob. Change 2019, 2, 00017. DOI: 10.3389/ffgc.2019.00017
3. Vohland, M.; Jung, A.; Reu, B.; Ramirez, J. A. Quantification of Non-structural Carbohydrates in Different Tissues of Trees with Diffuse Reflectance Spectroscopy: Is There a Benefit from a Fusion of Near- and Mid-infrared Data? Talanta 2024, 269, 125406. DOI: 10.1016/j.talanta.2023.125406