Impact of different tree species composition and seasonality on forest stand height predictions using airborne laser scanning and National forest inventory data

Abstract

Airborne laser scanning (ALS) data has been widely used for the assessment of various forest inventory parameters, such as forest stand height, biomass, etc. However, the spatial distribution of the ALS point cloud can be affected by various factors related to the survey methodology and forest stand characteristics. This study uses national coverage high-resolution ALS data with minimum point density of 4 points per square meter in combination with National forest inventory (NFI) field data to construct forest stand height models for forest stands dominated by 6 most common tree species in Latvia in mixed forest stand conditions- Pinus sylvestris L., Betula pendula Roth, Picea abies (L.) Karst, Populus tremula L., Alnus incana (L.) Moench and Alnus glutinosa (L.) Gaertn. We also take into account the ALS technology used and variations in the growing season. The ALS point cloud data was cut along the borders of the NFI plots and a statistical analysis of the spatial distribution of points within the borders of the NFI plots was performed. The results show that the RMSE value of the linear model using all NFI plot data is 1.91m, while the data sets divided by different tree species and seasonality reach the RMSE value in the range of 1.4m to 3.8m for Scots pine and Birch respectively.Key words: Forest inventory, airborne laser scanning, phenology, large scale forest mapping