# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 1 Introduction  
Knowledge of the coastline location forms the basis for coastal assessments and characterization of the land-ocean transition zone, and provides information on continental and coastal area [1]. The Antarctic coast is characterized by numerous ice shelves, which are permanent floating extensions of the glacial ice sheet onto the ocean. These ice shelves play an important role for ice sheet dynamics and mass balance. The Antarctic coastline region has a relatively high surface elevation, it is subjectedto \(\sim\)25% of the net ice sheet precipitation and covers 11% of Antarctica's area. Defining the Antarctic coastline becomes crucial for assessment of the overall glacial mass budget and, particularly, ice sheet mass balance [2]. In this paper, the coastline of the Antarctica is defined as the boundary between the ocean and the ice sheet, which includes ice shelves. The coastal zones of Antarctica are affected by ongoing natural processes, such as iceberg calving (i.e., change in shape) and thinning (i.e., loss of ice shelf volume). Hence, coastline monitoring is of great importance to assess changes in the coastline configuration and ice sheet mass balance. Recently, satellite remote sensing observations have offered an efficient means for mapping the continent of Antarctica [3], and have significantly improved the precision of coastline extraction. They have obviated the logistical obstacles of surface travel to obtain in situ data and the use of subjective manual delineation methods to map the coastline. Typically, due to the contrast of albedo between the ocean and the snow-covered ice sheets, optical images are considered the most efficient data set to precisely delineate the coastline. They are also used for this purpose due to ease of accessibility and their extensive temporal record [4]. Different methods for delineating coastlines based on optical images have been proposed in the past few decades. An approach for automatic coastline detection based on snakes (parametric active contours) has been applied to Landsat images of Antarctica [5]. A new approach combining histogram thresholding and band ratio has been applied to extract the Urmia Lake coastlines from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) images [6]. Based on morphological tools, a new approach comprising two steps has shown promising results [7]. A spectral linear mixing approach (SLMA) has been utilized to assess coastline changes in southeastern Brazil based on TM and ETM+ images [8]. Although optical imagery is capable of generating high-resolution continuous coastlines with pixel-level precision, it is not suitable for polar regions since they are severely affected by cloud cover, seasonally reduced solar illumination, and other meteorological conditions [9]. Therefore, in order to improve the accuracy and continuity of the delineated coastline when Landsat images are affected by clouds, radar remote sensing data from synthetic aperture radar (SAR) have been used in this study. With its ability to penetrate through clouds and its independence of daylight, SAR data have been used in a wide range of applications in the polar regions. A new automatic algorithm, based on pulse-coupled neural networks (PCNN), has been used to delineate coastlines by processing COSMO-SkyMed SAR products generated from different polarizations, geometric configurations, and measurement modes [10]. A novel algorithm (the modified K-means method and an adaptive coarse-fine object-based region-merging, MLAORM) for coastline extraction has been proposed for wide-swath Sentinel-1A images, which combines the modified K-means method and an adaptive coarse-fine object-based region-merging scheme [11]. More than 93% of the detected coastlines in Indonesia is located within 2-pixels distance in comparison with the manually traced coastlines [11].