Development of regional height growth model for Scots pine using repeated airborne laser scanning data

The rapid development of remote sensing technologies is creating unprecedented opportunities for monitoring and inventorying forest ecosystems. One advantage of remote sensing data is that it can be used to monitor and measure tree growth in near real-time, providing extremely useful data for growth modelling. This study used Aerial Laser Scanning (ALS) data from 14,920 Scots pine stands for the Katowice Regional Directorate of State Forests in southwestern Poland. We tested the possibility of calibrating a regional height growth model for Scots pine for a study area covering 754 thousands of hectares of forests. The model was validated with models developed for Scots pine using the traditional approach based on field data. Our results show that the model calibrated using remote sensing data does not differ significantly from the model calibrated using traditional field measurements from stem analysis. What is more, using a model developed from ALS data gives even better accuracy in modelling height growth than a traditional model calibrated with ground data. Our results are promising for the application of repeated ALS data to the development of regional height growth models, allowing long-term prediction of tree growth under current climatic conditions