Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches

Abstract

Biochemical traits in forest vegetation are key indicators of leaf physiological processes, specifically photosynthetic and other photochemical light pathways, and are critical to the quantification of the terrestrial carbon cycle. Advances in remote sensing sensors and platforms are allowing multi-dimensional and continuous-spatial information to be acquired in a fast and non-destructive way to quantify forest biochemical traits at multiple spatial scales. Here we demonstrate the use of high spectral resolution, hyperspectral data combined with high density three-dimensional information from Light Detection and Ranging (LiDAR) both acquired from an unmanned aerial system (UAS) platform, to quantify and assess the three-dimensional distribution of biochemical pigments on individual tree canopy surfaces. To do so, a DSM based fusion method was developed to integrate the 3D LiDAR point cloud with hyperspectral reflectance data. Regression-based models were then developed to predict a number of biochemical traits (i.e., chlorophyll (Chl) a, b, total Chl and total carotenoids (Cars) content) from a suite of common spectral indices at three vertical canopy levels, and were evaluated using a leave-one-out cross-validation approach. One-way ANOVA and Duncan's multiple comparison post hoc tests were used to investigate the vertical distribution of biochemical pigments on individual tree canopy surfaces, and in response to age and species. Our results demonstrated that a number of vegetation indices, derived from the hyperspectral data, were strongly correlated with a number of biochemical traits (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%). In general, models fitted using leaf samples from the upper, middle and lower canopies separately (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%) had similar accuracy to the models developed with pooled data (Adj-R2 = 0.87–0.90; rRMSE = 5.21–6.11%). The differences between separate models and global models were not statistically significant (P > 0.05). However, the distribution of biochemical pigments across vertical layers varied significantly. For dawn redwood (Metasequoia glyptostroboides) and poplar (Populus deltoides), the results were consistent in that the lower component of the canopy (least light impacted) had the highest chlorophyll and carotenoids biochemical traits. Moreover, the vertical distribution of biochemical traits on individual tree canopy surfaces changed with age likely due to the growth variation from the photosynthetic activity of the canopy. This study indicates the potential of using fused 3D point cloud information with spectral data to monitor physiological activities of forest canopy for carbon accumulation estimation as well as precision forestry applications such as nutrition diagnosis, water regulation and subsequent productivity enhancement of these planted forest systems.