Improving local forest growth prediction by terrain-derived attributes, airborne γ-ray, and leaf area index

Forest growth information is important in forest management and planning, and it is an issue of concern for various stakeholders. Therefore, accurate information about potential growth in forested landscape is needed. Forest growth has been traditionally assessed by way of modeling, however models fitted for a larger area tend to be biased when applied to local settings, and there is thus a crucial need for localization or other ways to improve the growth prediction of such models. There are various ways to achieve this improvement, one of which is by introducing new data elements. Consequently, this presented research used the known effect of topography on soil moisture content to achieve growth prediction improvements for local forest growth. A total of 9987 tally trees and 1118 sample trees distributed in 197 plots were used to examine the suitability of using terrain attributes derived from digital terrain model (DTM), airborne γ-ray and leaf area index (LAI) data, in improving pre-existing diameter at breast height (dbh) model for a five-year period (id5) in southeastern Finland. Statistical examinations of the mixed effect modeling (linear and non-linear) and multilayer perceptron modeling were used in the analysis. The results varied between sample trees and species. The best root mean square error (RMSE) improvements of the national model were obtained for broadleaved trees, followed by pine and spruce. All of the γ-ray (γk, γu, and γth) windows were shown to be significant in eliminating the local bias. The effect of the DTM source showed that a higher resolution with a lower focal neighborhood is the best combination for quantifying terrain attributes, notably the Topographic Wetness Index (TWI). The growth prediction improvement tended to be more accurate in less fertile site types of Vaccinum site type and Calluna site type. LAI demonstrated improvement when combined with terrain attributes, especially intensity-based LAI. The presented study concludes that the newly introduced elements are suitable for improving local forest growth prediction and reduced the RMSE of pre-existing model for dbh increment. However, further research is needed to confirm these findings, e.g. using larger geographical extents and attribute variations.