Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning

Abstract

This study considered airborne laser scanning (ALS) based aboveground biomass (AGB) prediction in mountain forests. The study area consisted of a long transect from southern Norway to northern parts of the country with wide ranges of elevation along a long latitudinal gradient (58°N–69°N). This transect was covered by ALS data and field data from 238 plots. AGB was modeled using different types of predictor variables, namely ALS metrics, variables related to growing conditions (elevation, latitude, and climatic variables), and tree species information. Modelling of AGB in the long transect covering diverse mountainous forest conditions was challenging: the RMSE values were rather large (37%–70%). The effects of growing conditions on model predictions were minor. However, species information was essential to improve accuracy. The analysis revealed that when doing inventories of spruce-dominated areas, all plots should be pooled together when the models are developed, whereas if pine or deciduous species dominate the area in question, separate dominant species-wise models should be constructed.