Forest disturbance monitoring based on the time-series trajectory of remote sensing index

Abstract

Forest ecosystems, which are major parts of the terrestrial biosphere, play an important role in terrestrial carbon cycling and storage. However, the accuracy of forest carbon-flux estimation is greatly influenced by the lack of forest disturbance data. Thus, we conduct a study in Wuning County in Southern China by adopting a time-series trajectory analysis technique to detect forest disturbances in 14 Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images from 1986 to 2011. This technique not only identifies forest disturbance, but also provides vegetation recovery information. By analyzing the time-space disturbance characteristics of forest disturbance, we found that Wuning County has suffered from a significantly dramatic disturbance in the 1990s, most of which has occurred in low-elevation areas because of human activities. Compared with field observations, the Kappa coefficient of our disturbance products reaches 0.80 with an overall accuracy of 89.7%, thus indicating the significant potential of the technique for forest disturbance monitoring.