An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from Landsat 8 imagery: A new rock outcrop map and area estimation for the entire Antarctic continent

As the accuracy and sensitivity of remote-sensing satellites improve, there is an increasing demand for more accurate and updated base datasets for surveying and monitoring. However, differentiating rock outcrop from snow and ice is a particular problem in Antarctica, where extensive cloud cover and widespread shaded regions lead to classification errors. The existing rock outcrop dataset has significant georeferencing issues as well as overestimation and generalisation of rock exposure areas. The most commonly used method for automated rock and snow differentiation, the normalised difference snow index (NDSI), has difficulty differentiating rock and snow in Antarctica due to misclassification of shaded pixels and is not able to differentiate illuminated rock from clouds. This study presents a new method for identifying rock exposures using Landsat 8 data. This is the first automated methodology for snow and rock differentiation that excludes areas of snow (both illuminated and shaded), clouds and liquid water whilst identifying both sunlit and shaded rock, achieving higher and more consistent accuracies than alternative data and methods such as the NDSI. The new methodology has been applied to the whole Antarctic continent (north of 82°40′ S) using Landsat 8 data to produce a new rock outcrop dataset for Antarctica. The new data (merged with existing data where Landsat 8 tiles are unavailable; most extensively south of 82°40′ S) reveal that exposed rock forms 0.18 % (21 745 km2) of the total land area of Antarctica: half of previous estimates.

Details

Publication status:
Published
Author(s):
Authors: Burton-Johnson, Alex ORCID, Black, Martin ORCID, Fretwell, Peter ORCID, Kaluza-Gilbert, Joseph

On this site: Alex Burton-Johnson, Martin Black, Peter Fretwell
Date:
1 August, 2016
Journal/Source:
The Cryosphere / 10
Page(s):
1665-1677
Digital Object Identifier (DOI):
https://doi.org/10.5194/tc-10-1665-2016