Abstract
BackgroundThe 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. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. 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. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age.ResultsThe 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. 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. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. 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. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively.ConclusionsTree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.
Highlights
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
We evaluated the models based on coefficient of determination (R2), root mean squared error (RMSE), and mean deviance (MD) according to sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
We found that forest age can be predicted with relatively high accuracy, especially for forests younger than 100 years and that tree height estimated from airborne laser scanning and predicted site index were the most important variables in predicting age
Summary
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. In Norway the age of stands is often unknown, there are no public maps with reliable estimates of forest age, and even in areas with forest management inventories age is often one of the most uncertain parameters. Growing processes over time result in specific tree dimensions and forest structure and are determined by a combination of historic management and abiotic factors. Abiotic factors are environmental conditions including topography, soil type, and macro- and microclimatic variables These characteristics determine the growth and production potential of a site for a given tree species and result in trees of greatly varying dimensions given the same age. Other methods for estimating site index make use of climate (Nothdurft et al 2012; Sharma et al 2012) or remotely sensed data (Socha et al 2017; Kandare et al 2017)
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