Recently, CESS Ph.D candidate Li Wenyu published a paper as the first author called"Continous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imager"on the journal of Remote Sensing of Environment. And Professor Gong Peng is the corresponding author.

Continuous monitoring of coastline dynamics is of crucial importance to the understanding of relative contributions of various potential driving factors behind the long-term coastline change. While a large number of efforts have been made to extract coastline and detect coastline change with remotely sensed data, the temporal frequency and spatial resolution of coastline datasets obtained are generally not fine enough to reflect the detailed process of coastline retreat and/or advance, particularly in coastlines with subtle variability. To overcome these limitations, we developed a method to continuously monitor the dynamics of a muddy coastline with subtle variability in western Florida at annual and subpixel scales using time-series Landsat data (1984–2013). First, robust indicators were used to indicate the annual “average” location of the dynamic coastline. Due to the complexity of muddy-coast morphology, the annual average location is represented not by the coast “line”, but by the fractional inundated “area” of coastline pixels (pixels where the coastline is located), namely annually inundated area. Second, the annually inundated area of coastline pixels was estimated with a model proposed in this study, and the uncertainty was estimated with the Monte Carlo method. The retrievals were validated at 10 sites with aerial imagery, and the overall RMSE (root mean square error) is 11.48%. Third, the long-term trend for the time series of annually inundated area was derived with a statistical model. The results indicate that the muddy coast in western Florida continues to shrink with an average rate of 0.42 ± 0.05 km²/year during the three decades. This study demonstrates the feasibility of time-series Landsat data in continuous monitoring of coastline dynamics.

According to Thomson Reuters Citation report in 2014, the impact factor ofRemote Sensing of Environmentis 6.393