Mapping terrestrial groundwater‐dependent ecosystems in arid Australia using Landsat‐8 time‐series data and singular value decomposition. Issue 4 (13th March 2022)

• Record Type: Journal Article
• Authors: Brim Box, Jayne; Leiper, Ian; Nano, Catherine; Stokeld, Danielle; Jobson, Peter; Tomlinson, Adrian; Cobban, Dale; Bond, Tim; Randall, Debbie; Box, Paul
• Editors: Disney, Mat; Cord, Anna
• Is Part Of: Remote sensing in ecology and conservation. Volume 8:Issue 4(2022)
• Page Start: 464
• Page End: 476
• Publication Date: 2022-03-13
• Subjects: Groundwater‐dependent ecosystem -- Landsat -- mapping -- time‐series -- singular value decomposition; Remote sensing -- Periodicals; Ecology -- Research -- Periodicals; Ecology -- Methodology -- Periodicals; Ecology -- Remote sensing -- Periodicals; Nature conservation -- Methodology -- Periodicals; 577.0723
• DOI: 10.1002/rse2.254
• Languages: English
• ISSNs: 2056-3485

Abstract

Abstract: 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. Abstract : The spatial extent of terrestrial vegetation types reliant on groundwater in arid regions is poorly known, which is problematic given that the global demand for groundwater continues to grow and in arid regions, groundwater extraction already exceeds recharge. This study presents a novel approach to map terrestrial groundwater‐dependent ecosystems (GDEs) by applying singular value decomposition to time‐series of vegetation indices derived from Landsat‐8 data, which isolates the temporal and spatial sources of variation associated with groundwater use. The final GDE classification map correctly classified 72% of ground‐truthed sites as GDEs (true positives) and 16% incorrectly (false positives), and this approach is broadly applicable to arid regions worldwide.