Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data

摘要

The aim of this study was to explore the ability of estimating change in total aboveground biomass (AGB) in young forests using multi-temporal airborne laser scanner data. A field data-set covering 11 growth seasons of 39 circular plots of size 200 m(2) from young forest in south-eastern Norway was used in the analyses. Different approaches for prediction of the AGB change were tested. One approach was based on modeling AGB for each point in time and predicting change as the difference between separate AGB predictions. We also tested two approaches based on modeling and predicting change directly, and two approaches where growth/reduction rates were modeled and used in prediction. The approach where change was predicted as a difference between biomass predictions seemed to yield the best results (root mean square error [RMSE] 14.8%). The other approaches yielded results that were similar in terms of RMSE, except for the approach where AGB change was predicted using a growth rate. The results indicate that prediction of change as a difference between AGB predictions works satisfactory for a wide range of forest conditions, but that the direct approaches can perform better in some cases.