Tree inventory software8/28/2023 ![]() ![]() Overall, we obtained good results, especially for stands with high SI. Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Mean deviance (MD) ranged between − 1 and 3 years. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. ![]() The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. However, area-wide information about forest stand age often does not exist. The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. ![]()
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