Mapping terrestrial groundwater-dependent ecosystems in arid Australia using Landsat-8 time-series data and singular value decomposition

Jayne Brim Box, Ian Leiper, Catherine Nano, Danielle Stokeld, Peter Jobson, Adrian Tomlinson, Dale Cobban, Tim Bond, Debbie Randall, Paul Box

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    The spatial extent of terrestrial vegetation types reliant on groundwater in arid Australia is poorly known, largely because they are located in remote areas that are expensive to survey. In previous attempts, the use of traditional remote sensing approaches failed to discriminate vegetation using groundwater from surrounding vegetation. Difficulties in discerning vegetation groundwater use by remote sensing may be exacerbated by the unpredictable rainfall patterns and lack of annual wet and dry seasons common in arid Australia. This study presents a novel approach to mapping terrestrial groundwater-dependent ecosystems (GDEs) by applying singular value decomposition (SVD) to time-series of vegetation indices derived from Landsat-8 data, to isolate the temporal and spatial sources of variation associated with groundwater use. In-situ data from 442 sites were used to supervise and validate logistic regression models and neural networks, to determine whether sites could be correctly classified as GDEs using components obtained from the SVD. These results were used to produce a probability map of GDE occurrence across a 557 000 ha study area. Overall accuracy of the final classification map was 79%, with 72% of sites correctly identified as GDEs (true positives) and 16% incorrectly classified as GDEs (false positives). The approach is broadly applicable in arid regions globally, and is easily validated if general background knowledge of regional vegetation exists. Globally, and going forward, increased water extraction is expected to severely limit water available for GDEs. Successfully mapping GDEs in arid environments is a critical step towards their sustainable management, and the human and natural systems reliant upon them.

    Original languageEnglish
    Pages (from-to)1-13
    Number of pages13
    JournalRemote Sensing in Ecology and Conservation
    Issue number4
    Early online date13 Mar 2022
    Publication statusPublished - 2022


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