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Mapping groundwater‐dependent ecosystems using remote sensing measures of vegetation and moisture dynamics
Author(s) -
Barron Olga V.,
Emelyanova Irina,
Van Niel Thomas G.,
Pollock Daniel,
Hodgson Geoff
Publication year - 2012
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.9609
Subject(s) - environmental science , groundwater , vegetation (pathology) , hydrology (agriculture) , land cover , riparian zone , ecosystem , normalized difference vegetation index , topographic wetness index , physical geography , remote sensing , soil science , land use , ecology , geology , leaf area index , geography , digital elevation model , medicine , geotechnical engineering , pathology , biology , habitat
Protection of groundwater‐dependent ecosystems (GDEs) is an important criterion in sustainable groundwater management, particularly when human water consumption is in competition with environmental water demands; however, the delineation of GDEs is commonly a challenging task. The Groundwater‐dependent Ecosystem Mapping (GEM) method proposed here is based on interpretation of the land surface response to the drying process derived from combined changes in two multispectral indices, the Normalised Difference Vegetation Index and the Normalised Difference Wetness Index, both derived from Landsat imagery. The GEM method predicts three land cover classes used for delineation of potential GDEs: vegetation with permanent access to groundwater; vegetation with diminishing access to groundwater; and water bodies that can persist through a prolonged dry period. The method was applied to a study site in the Ellen Brook region of Western Australia, where a number of GDEs associated with localised groundwater, diffuse discharge zones, and riparian vegetation were known. The estimated accuracy of the method indicated a good agreement between the predicted and known GDEs; Producer's accuracy was calculated as up to 91% for some areas. The method is most applicable for mapping GDEs in regions with a distinct drying period. Copyright © 2012 John Wiley & Sons, Ltd.