LiDAR-based TWI and terrain attributes in improving parametric predictor for tree growth in southeast Finland

The effect of soil moisture content on vegetation and therefore on growth is well known. Information about the growth of forest stands is key in forest planning and management, and is the concern of various stakeholders. One way to assess moisture content and its impacts on forest growth is to apply the Topographic Wetness Index (TWI) and the derived terrain attributes from the Digital Terrain Model (DTM). The TWI is an important terrain attribute, used in various ecological studies. In the current study, a total of 9987 tally trees within 197 sample plots in southeastern Finland and LiDAR (Light Detection and Ranging) based TWI were selected to examine: 1) the effect of cell resolutions and focal statistics of neighborhood cells of DTM, on tree diameter increment, and 2) possibilities to improve the prediction accuracy of an existing single-tree growth model using the terrain attributes and TWI with the combined effects of three characteristics (i.e., cell resolutions, neighborhood cells and terrain attributes). The results suggest that the TWI with terrain attributes improved the growth estimation significantly, and within different site types the Root Mean Square Errors (RMSE) were lowered substantially. The best results were obtained for birch trees. The higher resolution of the DTM and the lower focal neighborhood cells were found to be the best alternative in computing the TWI.