UAV hyperspectral combined with LiDAR to estimate chlorophyll content at the stand and individual tree scales

Chlorophyll is an important indicator of vegetation health status, accurate estimation of which is important for evaluating forest carbon sink. In this study, we estimated the chlorophyll content of coniferous forests, broad-leaved forests and mixed forest stands at stand and individual tree level by unmanned air vehicle (UAV) hyperspectral data combined with light detection and ranging (LiDAR) point clouds, which improved the non-destructive estimation accuracy of forest chlorophyll. We further comprehensively analyzed the spatial distribution of chlorophyll content at different scales. A total of 36 spectral characteristic variables related to chlorophyll content were screened by correlation analysis based on the fusion of UAV hyperspectral data and LiDAR point clouds combining with the empirical data from ground plots. We constructed multiple models for chlorophyll estimation by using statistical model, including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest and hybrid data-driven PROSPECT mechanism model. The optimal model was selected to estimate the chlorophyll content. The horizontal and vertical distribution of chlorophyll content at the stand level and individual tree level were analyzed. The results showed that the random forest model was superior to the models constructed by multiple stepwise regression, BP neural network and BP neural network optimized by firefly algorithm for chlorophyll estimation with R² and RMSE of 0.59-0.64 and 3.79-5.83 μg·cm^-2, respectively. The accuracy of the mechanism model was the highest, with R² and RMSE of 0.97 and 3.40 μg·cm^-2. The chlorophyll contents differed across stand types, with that in broad-leaved forest (25.25-31.60 μg·cm^-2) being higher than mixed forest (13.52-23.93 μg·cm^-2) and coniferous forest (6.40-13.71 μg·cm^-2). There were significant differences in chlorophyll contents the in vertical direction among different stands. For individual tree of different species, the chlorophyll content inside the canopy was lower than that outside the canopy in the horizontal direction. In the vertical direction, there was no difference in chlorophyll content among different layers of Pinus sylvestris var. mongolica canopy. However, significant differences were observed among the upper, middle, and lower layers of Juglans mandshurica canopy. Using the fusion of hyperspectral image and LiDAR point cloud data, the mechanism model driven by hybrid data could effectively improve the accuracy and stability of chlorophyll content estimation at different scales.