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. 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. 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. 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. Tree 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.

中文翻译：

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使用国家森林清单，机载激光扫描和Sentinel-2数据绘制森林年龄图

林分年龄是森林管理和保护的重要信息，例如对于生长模型，管理活动和采伐的时间安排或有关保护区的决定。但是，有关林分年龄的区域性信息通常不存在。在这项研究中，我们开发了用于大范围预测挪威森林年龄的回归模型。为了进行模型开发，我们使用了4800多个挪威国家森林清单（NFI）地块，分布在挪威的一个18.2 Mha研究区域中，纬度为北纬58°至65°。预测变量基于机载激光扫描（ALS），Sentinel-2和现有的公共地图数据。我们对由63个已知年龄的云杉林组成的独立数据集进行了模型验证。最好的建模策略是将独立的线性回归模型拟合到每个观察到的站点索引（SI）级别，并在模型的应用中使用SI预测图。最重要的预测变量是ALS高度的上百分数，SI特定模型的均方根误差（RMSE）在3至31年（6％至26％）之间，平均21年（25％） 。平均偏差（MD）介于− 1和3年之间。随着SI的增加，模型得到了改进，而RMSE值大于100年的低SI展位最大。使用实际应用所需的映射SI，RMSE和MD的地块级别分别为19至56年（29％至53％）和5至37年（5％至31％）。对于验证台，RMSE和MD分别为12年（22％）和2年（3％）。在描述年龄的模型中，通过机载激光扫描估算的树高和预测的位置指数是最重要的变量。总体而言，我们获得了良好的结果，尤其是对于高SI的展台。可以考虑将这些模型用于实际应用，尽管如果可以使用更好的SI映射，我们看到了很大的改进潜力。