posted on 2015-02-11, 10:58authored byS. Grebby, D. Cunningham, Kevin Tansey, J. Naden
Vegetation cover can affect the lithological mapping capability of space- and airborne instruments because it obscures the spectral signatures of the underlying geological substrate. Despite being widely accepted as a hindrance, few studies have explicitly demonstrated the impact vegetation can have on remote lithological mapping. Accordingly, this study comprehensively elucidates the impact of vegetation on the lithological mapping capability of airborne multispectral data in the Troodos region, Cyprus. Synthetic spectral mixtures were first used to quantify the potential impact vegetation cover might have on spectral recognition and remote mapping of different rock types. The modeled effects of green grass were apparent in the spectra of low albedo lithologies for 30%-40% fractional cover, compared to just 20% for dry grass cover. Lichen was found to obscure the spectra for 30%-50% cover, depending on the spectral contrast between bare rock and lichen cover. The subsequent impact of vegetation on the remote mapping capability is elucidated by considering the outcomes of three airborne multispectral lithological classifications alongside the spectral mixing analysis and field observations. Vegetation abundance was found to be the primary control on the inability to classify large proportions of pixels in the imagery. Matched Filtering outperformed direct spectral matching algorithms owing to its ability to partially unmix pixel spectra with vegetation abundance above the modeled limits. This study highlights that despite the limited spectral sampling and resolution of the sensor and dense, ubiquitous vegetation cover, useful lithological information can be extracted using an appropriate algorithm. Furthermore, the findings of this case study provide a useful insight to the potential capabilities and challenges faced when utilizing comparable sensors (e.g., Landsat 8, Sentinel-2, WorldView-3) to map similar types of terrain.
Funding
This work was supported by a Natural Environment Research Council (NERC) CASE Studentship
(NE/F00673X/1) with the British Geological Survey (BGS), awarded to Stephen Grebby. We
gratefully acknowledge the NERC Airborne Research and Survey Facility (MC04/30) for ATM data
acquisition, and the NERC Field Spectroscopy Facility (loan No: 589.1209) and Alasdair MacArthur Remote Sens. 2014, 6 10882
for loan of and advice on using the ASD FieldSpecĀ® Pro. We also express our gratitude to the
Geological Survey Department of Cyprus (GSD) for providing the digital geology data, to Stelios
Nicolaides (GSD) and Simon Jowitt (Monash University) for invaluable logistical and scientific help
in the field, and to Luke Bateson (BGS) for AZGCORR software support. Stephen Grebby is grateful to the Geological Remote Sensing Group for a Student Fieldwork and Travel Award. The five
anonymous reviewers are thanked for their comments and suggestions which helped to improve the manuscript. Stephen Grebby and Jonathan Naden publish with permission of the executive director, British Geological Survey (NERC).
History
Citation
Remote Sensing, 2014, 6 (11), pp. 10860-10887
Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography/GIS and Remote Sensing