Forest age map covering Germany for 2026

General description: In Lange et. al (2026) we used a digital elevation (BKG 2016) and a digital surface model (BKG 2020) to generate a canopy height model. The latter was used in conjunction with a remotely sensed tree species (groups) map (Blickensdörfer et al., 2024) to obtain area-wide, species-specific tree heights. An allometric model was built by relating tree species, height and age information from Germany's national forest inventory (Thünen Institute, 2018, 2024). This model was applied to the area-wide, species-specific tree heights to estimate forest age on a national scale. Further, the record dates of the height information and a forest cover loss map (Hansen et al., 2013) were used to correct the age estimates. This results in a national-scale forest age map with additional uncertainty information. More information can be found in the related publication and in the UFZ Forest condition monitor web-application. Data description: Data is provided in Cloud Optimized GeoTiff format (projection EPSG:32632). Forest age and standard deviation maps are available in a spatial resolution of 10 m for the year 2026. The forest age map shows the age of forests in years. The standard deviation map shows the standard deviation of the forest age estimation in years. Values of the standard deviation map are scaled by 10 to reduce the file size. Final standard deviation values are obtained by dividing the raw values by 10. Forest areas which encountered forest loss or severe disturbances (see Hansen et al., 2013) are indicated with a value of 0. File descriptions: Data is provided in Cloud Optimized GeoTiff format (projection EPSG:32632). Two files are provided: Forest-age_v02-05-14_Germany_age_masked_cog.tif contains forest age values across Germany for 2026 Forest-age_v02-05-14_Germany_age_sd_masked_cog.tif contains forest age standard deviation values across Germany for 2026 Please note: Forest pixels were selected according to the tree species map from Blickensdörfer et al. (2024).总体说明：在Lange等（2026）的研究中，研究团队采用德国联邦测绘与大地测量局（BKG）2016年发布的数字高程（Digital Elevation）数据与BKG 2020年发布的数字表面模型（Digital Surface Model）生成冠层高度模型（Canopy Height Model）。将该冠层高度模型与遥感树种（组）分布图（Blickensdörfer等，2024）结合使用，可获取全域范围内的树种特异性树高。研究团队基于德国国家森林清查数据（图能研究所，2018、2024）中的树种、树高与年龄信息构建异速生长模型，并将该模型应用于全域树种特异性树高数据，以在国家尺度上估算森林年龄。此外，结合树高数据的记录年份与森林覆盖损失图（Hansen等，2013）对年龄估算结果进行校正，最终生成带有附加不确定性信息的国家尺度森林年龄分布图。更多细节可参阅相关研究论文及UFZ森林状况监测网络应用程序。 数据说明：本数据集采用云优化GeoTIFF（Cloud Optimized GeoTIFF, COG）格式存储，投影坐标系为EPSG:32632。提供2026年的森林年龄图与标准差图，空间分辨率均为10米。其中，森林年龄图以年为单位展示森林的林龄；标准差图以年为单位展示森林年龄估算结果的标准差。为压缩文件体积，标准差图的原始数值已被放大10倍，最终标准差数值需将原始值除以10得到。发生森林损失或严重扰动的森林区域（详见Hansen等，2013）将以数值0标识。 文件说明：数据采用云优化GeoTIFF格式存储，投影坐标系为EPSG:32632。本次共提供两个文件： Forest-age_v02-05-14_Germany_age_masked_cog.tif：包含2026年德国全域的森林年龄数值 Forest-age_v02-05-14_Germany_age_sd_masked_cog.tif：包含2026年德国全域的森林年龄标准差数值 注意事项：森林像元的选取基于Blickensdörfer等（2024）发布的树种分布图。