Identification of forest disturbance and estimation of forest age in subtropical mountainous areas based on Landsat time series data

Forest age is significantly correlated with the net primary productivity, biomass, carbon flux, and the community structure of forest ecosystems. A Landsat time series was constructed using archived Landsat data and topographical maps to achieve large-scale spatial data on forest age. An algorithm used to identify forest disturbance based on a Mann-Kendall trend test, Mann-Kendall abrupt change test, and a difference rate index (DRI) was proposed. A forest age estimation scheme was established based on the classification of forest disturbance-recovery scenarios to obtain the spatial distribution data of forest age in the study region. The results show that: (1) through de-clouding and spectral fitting, imagery acquired by the Landsat-5 Thematic Mapper and Landsat-8 Operational Land Imager sensors could be used to construct a Landsat time series over the period of 1987–2018 in subtropical areas with complex topography; (2) a DRI was extracted from the time series as a disturbance indicator, which was subjected to a Mann-Kendall trend test, leading to the identification of five forest disturbance-recovery scenarios: recovery (or no recovery) after complete disturbance, recovery after partial disturbance, sustained recovery after positive disturbance, and non-disturbance; (3) based on identification of disturbance-recovery scenarios, a forest age estimation scheme was further developed by using the mean fractional vegetation cover before disturbance, fractional vegetation cover at the end of disturbance, and the vegetation recovery rate after disturbance in conjunction with Landsat Multispectral Scanner data from 1974 and topographical maps from the 1960 s, which achieved overall accuracy metrics of R²=0.72 and RMSE=7.8 years for forest age estimates. Specifically, the accuracy of forest age estimates was high in middle-aged and near-mature forests but low in young and mature forests, regardless of the forest vegetation type. The proposed algorithm for identifying areas of forest disturbance and forest age estimation can allow for forest change monitoring and forest age estimation at a regional scale of subtropical mountainous areas, providing a reference for the remote sensing estimation of forest ecological parameters in those areas.