Investigating within-forest variability in the onset of tree height growth in a boreal Scots pine forest

Tree growth is a key indicator of forest health and development, especially in the context of a changing climate. Interactions between abiotic and biotic factors influencing tree growth are highly complex, with their full magnitude still unknown. Even trees of the same species and within the same forest can present high variability in their growth, as they are affected by various macro- and micro-scale factors. In order to detect and quantify tree growth at tree level, close-range monitoring with high spatial and temporal resolutions is required. For this purpose, LiDAR (Light Detection and Ranging) data is widely recognized for its ability to produce high-resolution point clouds, which enable studying intricate changes in trees. 

The goal of our study is to explore the potential of daily LiDAR time-series for detecting the onset of tree height growth and quantifying the total growth in tree height, to help understand the biotic and abiotic factors contributing to height growth variability in Scots pine (Pinus sylvestris) trees. Here, we studied 97 Scots pine trees during the growing season of 2021 with dense spatiotemporal point cloud time series collected with LiDAR Phenology Station (LiPhe) in Hyytiälä forest research station, Finland. We developed a semi-automatic framework to extract individual tree height time-series, which includes point cloud registration, point cloud segmentation, and tree height estimation. Based on extracted height time-series derived from LiPhe, we detected the onset of tree height growth using a change point detection algorithm.  

We found up to 28 days of variability in the onset of height growth within the studied Scots pine trees. To investigate the factors influencing the variability in the onset of height growth, we used tree size, neighborhood characteristics, and topography as explanatory variables in a linear mixed-effects model. These variables were also estimated from LiDAR data. The best performing model for modelling the onset of growth combined Plant Area Index (PAI), Vertical Complexity Index (VCI), and Topographic Wetness Index (TWI), as fixed-effect terms.  

Our results suggest that higher density and complexity of neighboring trees leads to earlier onset of tree height growth, which can suggest competition for light and microclimate variability. Meanwhile, lower TWI led to earlier onset of tree height growth, indicating that trees located on a slightly higher slope with less water availability grew earlier in height. Lower areas may have a cooler microclimate, since they often retain more soil moisture and are less exposed to wind, which can lead to later growth onset.  

We conclude that daily LiDAR time-series enables measurements that are challenging to achieve using other techniques, such as detecting the onset of height growth. Our study suggests that the onset of height growth may be mainly influenced by light competition and microclimate, demonstrating the potential of tree-level LiDAR-derived metrics in studying how microclimatic changes affect forest adaptability in the context of a changing climate.  We will further continue to study the influence of the timing of growth on the amount of growth during the growing season.