Monitoring Long-Term Change Trends in Boreal Permafrost Area Using Time Series Landsat

As climate change accelerates in recent decades, boreal permafrost areas have been undergone various changes. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, it is difficult to acquire optical data for monitoring the long-term land cover changes in the boreal permafrost areas due to the Polar Night and weather conditions. In this study, a new classification method was proposed to classify spatiotemporally sparse and irregularly acquired Landsat data in central Yakutia. The proposed method showed spatially consistent results in the overlap regions of neighboring Landsat data. The classification results of time series multiple Landsat images were used for understanding the land cover changes during the study period of about 20 years. The result of analyzing the long-term land cover changes revealed the spatial and temporal patterns for each Landsat path/row.