Uncertainty Modelling of Groundwater-Dependent Vegetation

Todd P. Robinson (), Lewis Trotter and Grant W. Wardell-Johnson
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Todd P. Robinson: School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
Lewis Trotter: School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
Grant W. Wardell-Johnson: School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia

Land, 2024, vol. 13, issue 12, 1-21

Abstract: Groundwater-dependent vegetation (GDV) is threatened globally by groundwater abstraction. Water resource managers require maps showing its distribution and habitat preferences to make informed decisions on its protection. This study, conducted in the southeast Pilbara region of Western Australia, presents a novel approach based on metrics summarising seasonal phenology (phenometrics) derived from Sentinel-2 imagery. We also determined the preferential habitat using ecological niche modelling based on land systems and topographic derivatives. The phenometrics and preferential habitat models were combined using a framework that allows for the expression of different levels of uncertainty. The large integral (LI) phenometric was capable of discriminating GDV and reduced the search space to 111 ha (<1%), requiring follow-up monitoring. Suitable habitat could be explained by a combination of land systems and negative topographic positions (e.g., valleys). This designated 13% of the study area as requiring protection against the threat of intense bushfires, invasive species, land clearing and other disturbances. High uncertainty represents locations where GDV appears to be absent but the habitat is suitable and requires further field assessment. Uncertainty was lowest at locations where the habitat is highly unsuitable (87%) and requires infrequent revisitation. Our results provide timely geospatial intelligence illustrating what needs to be monitored, protected and revisited by water resource managers.

Keywords: groundwater-dependent ecosystems; ecosystem services; Dempster–Shafer Belief Theory; phenometrics; refugia; remote sensing; arid zone (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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