结合Landsat时序遥感干扰检测的福建将乐县森林林龄估算与评价

高精度的森林林龄可以改善森林生物量、蓄积量、碳储存量等的估算精度。为提高频繁发生干扰区域森林年龄估算精度，以森林干扰强度较大的福建省将乐县为例，通过构建将乐县1987～2019年Landsat时序数据集，利用LandTrendr算法获得森林干扰开始时间节点特征，与林龄建模，实现干扰区林龄估算；接着利用GF-1号影像的波段、植被指数、纹理以及地形因子特征，通过递归特征消除的随机森林算法，与林龄建模，实现非干扰区林龄估算；最后将两部分的林龄合并，得到研究区2019年森林年龄。结果表明：(1)将乐县森林干扰总面积为346.37 km2，其中，针叶林、阔叶林干扰面积占比75.06%；(2)利用LandTrendr算法的干扰开始时间节点估算的林龄误差（RMSE=1.91 a）较小，模型精度（R2=0.94）较高；(3)通过递归特征消除的随机森林算法估算的针叶林、阔叶林林龄的R2和RMSE分别为0.64、0.48和4.71 a、12.71 a。研究表明：结合长时间序列的干扰算法可以有效提高干扰区森林年龄估算精度，为亚热带山区的区域尺度上进行森林林龄估计提供参考。

Estimation and Evaluation of Forest Age in Jiangle County,Fujian Province based on Lansat Time Series Remote Sensing Disturbance Detection

High-precision forest age can improve the estimation accuracy of forest biomass， stock and carbon storag， In order to improve the accuracy of forest age estimation in areas with frequent disturbances， taking Jiangle County， Fujian Province， where the forest disturbance intensity is high， as an example. the Landsat time series data of Jiangle County from 1987 to 2019 was constructed， and， the LandTrendr algorithm was used to obtain the node characteristics of the beginning of forest disturbance， through modeling with forest age information， realizing the mapping of the existing forest age in the disturbance area. Then， using the band， vegetation index， texture and topographic factor characteristics of the GF-1 image. through the recursive feature elimination of random forest algorithm， and the forest age modeling to achieve non- Estimation of the forest age in the disturbed area； In the end， the forest age of the two parts are combined to obtain the forest age in the study area in 2019. The results show that：①The total area of forest disturbance in Jiangle County from 1987 to 2019 is 346.37 km2， of which the - coniferous forest and broad-leaved forest account for 75.06% of the disturbance area；②The forest age error （RMSE=1.91 years） estimated by the LandTrendr algorithm’s disturbance start time node is small， and the model accuracy （R²=0.94） is high；③The R² and RMSE of the age of coniferous forest and broad-leaved forest estimated by the random forest algorithm with the recursive feature elimination random forest algorithm are 0.64，0.48 and 4.71，12.71 years， respectively. Research shows that the disturbance algorithm combined with long time series can effectively improve the accuracy of forest age estimation in disturbance area， and provide a reference for forest age estimation at the regional scale in the subtropical mountainous area.