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The age of forest stands is critical information for many aspects of forestmanagement and conservation but area-wide information about forest stand ageoften does not exist. In this study, we developed regression models forlarge-scale area-wide prediction of age in Norwegian forests. For modeldevelopment we used more than 4800 plots of the Norwegian National ForestInventory (NFI) distributed over Norway between 58{\deg} and 65{\deg} northernlatitude in a 181,773 km2 study area. Predictor variables were based onairborne laser scanning (ALS), Sentinel-2, and existing public map data. Weperformed model validation on an independent data set consisting of 63 sprucestands with known age. The best modelling strategy was to fit independentlinear regression models to each observed site index (SI) level and using a SIprediction map in the application of the models. The most important predictorvariable was an upper percentile of the ALS heights, androot-mean-squared-errors (RMSE) ranged between 3 and 31 years (6% to 26%) forSI-specific models, and 21 years (25%) on average. Mean deviance (MD) rangedbetween -1 and 3 years. The models improved with increasing SI and the RMSEwere largest for low SI stands older than 100 years. Using a mapped SI, whichis required for practical applications, RMSE and MD on plot-level ranged from19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. Forthe validation stands, the RMSE and MD were 12 (22%) and 2 years (3%). Treeheight estimated from airborne laser scanning and predicted site index were themost important variables in the models describing age. Overall, we obtainedgood results, especially for stands with high SI, that could be considered forpractical applications but see considerable potential for improvements, ifbetter SI maps were available.

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data