Traditional methods for assessing forest health and productivity often rely on either ground-based sensors, which are labor-intensive and limited in scope, or standard satellite and drone imagery, which only captures the flat, top-down view of the canopy. This two-dimensional perspective misses the crucial vertical structure where light, leaves, and physiology interact. While light detection and ranging (LiDAR) can accurately map a forest's three-dimensional (3D) structure, it lacks the biochemical data needed to assess photosynthesis. Based on these challenges, there is a critical need for an integrated approach that can combine structural and spectral information to conduct in-depth research on light interception in tall conifer species.
Researchers from the Research Institute of Subtropical Forestry, Chinese Academy of Forestry, and Hebei Agriculture University published (DOI: 10.1016/j.plaph.2026.100175) their findings on February 3, 2026, in Plant Phenomics. The study introduces a novel workflow that processes high-density point clouds and five-band spectral data to estimate the fraction of absorbed photosynthetically active radiation (fPAR) at a voxel level, providing an unprecedented, species-specific tool for precision forestry.
The team surveyed a slash pine seed orchard in China over four years, using a drone-mounted LiDAR system and a multispectral sensor to gather data in tandem. They compared the predictive power of 14 vegetation indices (VIs) with four machine learning models: Random Forest (RF), XGBoost, Partial Least Squares Regression (PLSR), and Support Vector Machine (SVM). The RF model proved superior, explaining 84% of the variation in ground-measured fPAR. The analysis revealed that the red-edge based indices, particularly the Red Edge Chlorophyll Index (RECI), were the most influential predictors of light absorption. The resulting 3D maps highlighted a striking vertical gradient, with the upper crown absorbing an average of 26% more light than the lower strata—a pattern linked to dense needle clumping and steep leaf angles. The research also captured a clear seasonal rhythm, with fPAR peaking in winter and dipping in late spring, likely due to water stress and changing light quality, and identified persistent clusters of high- and low-fPAR trees across the plantation.
"The real game-changer is the 3D perspective," the authors said. "For the first time, we can see exactly how light is captured, not just at the top, but throughout the entire crown. This allows us to pinpoint which trees or families are more efficient at using sunlight and to understand how the canopy’s internal structure affects that efficiency. It’s a major step toward bridging the gap between genetics and the environment in forestry."
This scalable, high-throughput workflow provides a practical tool for tree breeders and forest managers aiming to enhance productivity and carbon sequestration. By identifying families with superior light-use efficiency, breeding programs can be accelerated. For silviculture, the 3D maps offer a precise guide for targeted thinning to optimize light distribution to lower, shaded canopy layers, potentially boosting overall stand growth and resilience. While developed for slash pine, the methodology can be recalibrated for other tall conifer plantations, promising a more sustainable, data-driven future for global forestry.
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References
DOI
10.1016/j.plaphe.2026.100175
Original Source URL
https://doi.org/10.1016/j.plaphe.2026.100175
Funding information
This work was funded by the Fundamental Research Funds of CAF, No. CAFYBB2022QA001and the Science and Technology innovation 2030-Agricultural biological breeding major project (2023ZD040580105).
About Plant Phenomics
Plant Phenomics publishes breakthrough research that integrates genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics and computer science to advance plant phenotyping—from the cellular level to entire populations. The journal tackles key scientific challenges in phenomics by showcasing cutting‑edge technologies for data acquisition, management, interpretation and modelling. Its scope spans practical applications across all plant sciences, aiming to translate novel phenotyping tools into real‑world solutions for breeding, crop management and fundamental plant biology. By fostering interdisciplinary innovation, Plant Phenomics serves as a hub for researchers developing high‑throughput, accurate and scalable methods to decipher complex plant traits, ultimately accelerating progress in agriculture and plant science.