A new semi-automatic seamless cloud-free landsat mosaicking approach tracks forest change over large extents

The extensive and freely available archive of Landsat satellite data is used throughout the world to assess forest changes over large areas and long time periods (30-40 years). But analyzing Landsat data in time-series is not free of challenges (e.g. data processing and storage capabilities, dealing with cloud cover and other data gaps, and accounting for changes in illumination conditions due to atmospheric effects, sun angle and vegetation phenology). In this research, we present a method used to create annual seamless cloud-free mosaics for the entire state of Victoria, Australia (19 Landsat tiles), for a 30 year period. These mosaics were created by first constructing yearly Best Available Pixel (BAP) composites from over 3000 individual scenes. Then, forested areas were analyzed in time-series to determine breakpoints (e.g. a disturbance event such as fire). Following this, the breakpoints were used to fit a piece-wise linear regression model through each pixel's temporal trajectory. In this way, data gaps and other radiometric anomalies were removed. These gap-free composites can be used by a variety of stakeholders for land management, statutory reporting and decision making activities. This ensures state-wide consistency, and offers significant savings in processing and storage requirements.