Assessment of Antarctic moss health from multi-sensor UAS imagery with random forest modelling

• Published in: International Journal of Applied Earth Observation and Geoinformation
• Main Authors: Turner, D, Lucieer, A, Malenovsky, Z, King, D, Robinson, SA
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Science Bv 2018
• Subjects: Engineering; Geomatic Engineering; Photogrammetry and Remote Sensing; Antarctic; East Antarctica; The Antarctic; Windmill Islands; Antarc*; Antarctica
• Online Access: https://doi.org/10.1016/j.jag.2018.01.004; http://ecite.utas.edu.au/125181

Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic continent and as such mapping their extent and monitoring their health is important to environmental managers. Across Antarctica, moss beds are experiencing changes in health as their environment changes. As Antarctic moss beds are spatially fragmented with relatively small extent they require very high resolution remotely sensed imagery to monitor their distribution and dynamics. This study demonstrates that multi-sensor imagery collected by an Unmanned Aircraft System (UAS) provides a novel data source for assessment of moss health. In this study, we train a Random Forest Regression Model (RFM) with long-term field quadrats at a study site in the Windmill Islands, East Antarctica and apply it to UAS RGB and 6-band multispectral imagery, derived vegetation indices, 3D topographic data, and thermal imagery to predict moss health. Our results suggest that moss health, expressed as a percentage between 0 and 100% healthy, can be estimated with a root mean squared error (RMSE) between 7 and 12%. The RFM also quantifies the importance of input variables for moss health estimation showing the multispectral sensor data was important for accurate health prediction, such information being essential for planning future field investigations. The RFM was applied to the entire moss bed, providing an extrapolation of the health assessment across a larger spatial area. With further validation the resulting maps could be used for change detection of moss health across multiple sites and seasons.