Mapping and assessing typhoon-induced forest disturbance in Changbai Mountain National Nature Reserve using time series Landsat imagery

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Monitoring forest disturbances is important for understanding changes in ecosystems. The 1986 Typhoon Vera was a serious disturbance that severely impacted the forest ecosystems of Changbai Mountain National Nature Reserve. Although the typhoon disturbance occurred more than two decades ago, the effects of the typhoon still remain within the study area. Few studies have focused on mapping and assessing disturbances across broad spatial and temporal scales. For this study, we first generated a map of forest composition prior to the typhoon disturbance, which served as a baseline data for the extraction of disturbed area. Then, the Disturbance Index (DI) method was tested for mapping the extent and magnitude of disturbance in the study area by applying a Tasseled Cap transformation to the Landsat imagery. The Landsat-based DI method estimated that an area of 13,764.78 ha of forest was disturbed by the typhoon. Based on visual assessments, these results correspond closely with the reference map derived from ground surveys. These results also revealed the influence of local topographic features on the distribution of windthrow areas. Windthrow areas were more pronounced in areas with elevations ranging from 1,000 to 2,000 m, slopes of less than 10 degrees, and southwestern to northwestern aspects. In addition, the relatively long (25 years) post-typhoon recovery period assessed by this study provided a more comprehensive analysis of the dynamics of forest recovery processes over time. Windthrow areas did not recover immediately after the typhoon, likely due to forest management practices enacted at the time. So far, forest recovery has proceeded more rapidly at elevations below 1,400 m, particularly on western slopes within the study area. Finally, a time series of DI values within the study period suggests a secondary disturbance may have occurred between 2000 and 2001.

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Journal of Mountain Science