Uncertainty Modelling of Groundwater-Dependent Vegetation

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.

Full text

Citation: Robinson, T.P.; Trotter, L.;
Wardell-Johnson, G.W. Uncertainty
Modelling of Groundwater-Dependent
Vegetation. Land 2024,13, 2208.
https://doi.org/10.3390/land13122208
Academic Editor: Charles Bourque
Received: 20 November 2024
Revised: 10 December 2024
Accepted: 14 December 2024
Published: 17 December 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Uncertainty Modelling of Groundwater-Dependent Vegetation
Todd P. Robinson 1 ,*, Lewis Trotter 1and Grant W. Wardell-Johnson 2
1School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;
lewis.trotter@postgrad.curtin.edu.au
2School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;
g.wardell-johnson@curtin.edu.au
*Correspondence: t.robinson@curtin.edu.au
Abstract: Groundwater-dependent vegetation (GDV) is threatened globally by groundwater abstrac-
tion. 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, inva-
sive 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
1. Introduction
Groundwater-dependent ecosystems (GDEs) rely on groundwater to sustain their eco-
logical function [
1
]. These ecosystems host a rich diversity of flora and fauna and support
a range of ecosystem services such as water storage and purification [
2
,
3
]. According to
Doody et al. [
4
], there are three main types: subterranean (aquifers and caves), aquatic
(ecosystems dependent on surface expression of groundwater, such as springs) and terres-
trial (ecosystems dependent on the subsurface presence of groundwater). Groundwater-
dependent vegetation (GDV) can occur in both terrestrial and aquatic GDEs and comprises
vegetation complexes and communities that have some level of reliance on groundwater to
maintain their ability to grow and function [5,6].
Approximately one-third of the world’s vegetation is dependent on groundwater
for at least some of its water requirements [
7
]. However, as around 40% of all global
groundwater abstraction occurs in the arid regions of the world, most of the research on
GDV is focused there [
8
]. With high daily transpiration requirements in these regions, the
inability to source water from a declining water table makes them highly vulnerable to arid
conditions, potentially resulting in widespread mortality [
9
–
11
]. Hence, GDV protection
is an important consideration in water resource management [
11
,
12
], but this requires
knowing its distribution and extent throughout the broader landscape [2,13].
Land 2024,13, 2208. https://doi.org/10.3390/land13122208 https://www.mdpi.com/journal/land

Land 2024,13, 2208 2 of 20
Aerial observations have described GDV as “green islands” in contrast to the surround-
ing landscape [
14
]. This characteristic has been the basis for mapping GDV using earth
observation data; GDV will possess green, moist foliage year-round if it is able to access the
water table, whereas non-GDV will not [
15
] Barron et al. [
16
] exploited this characteristic
in their Groundwater-dependent Ecosystem Mapping (GEM) method. The GEM method
looks for minimal differences in leaf greenness and leaf moisture between wet and dry
acquisitions of bi-annual Landsat imagery. They accomplish this using a combination of
the normalised difference vegetation index (NDVI; Ref. [
17
]) and the normalised difference
wetness index (NDWI; Ref. [18]), respectively.
A variation of the GEM method has been the greater use of the available temporal
resolution (e.g., monthly or bi-monthly acquisitions rather than bi-annual) to explore
yearly variability more finely. Several studies have summarised these acquisitions into
per-pixel mean and standard deviation metrics to identify pixels with low variance, above-
average greenness and, hence, typical healthy GDV characteristics (e.g., Refs. [
19
,
20
]). This
advancement beyond bi-annual observations more fully utilises the seasonal phenological
response of GDV and thus should smooth out any seasonal anomalies like unseasonal
precipitation that may cause flushing of the understorey.
Phenometrics extend this principle further and have the potential to extract sub-
stantially more of the information available from the time series than just the mean and
variance [
21
]. Phenometrics aim to quantify key phenological stages of plants over mul-
tiple seasons. These include statistics that relate to peak greenness, rate of greening, rate
of senescence, overall productivity and the start and end of growth periods. They also
have proven application in the discrimination of vegetation communities. For example,
van Leeuwen et al. [
22
] showed that vegetation communities in wetter locations in their
study area have higher productivity as well as an earlier start to the growing season than
coexisting communities along a drying altitudinal gradient.
Other methods augmenting spectral data have utilised auxiliary variables at different
scales. Over large areas, the Gravity Recovery and Climate Experiment (GRACE) satellite,
which maps Earth’s gravity field using a K-band microwave ranging system [
23
], has
improved our understanding of groundwater storage, although it is limited by its spatial
resolution of c. 300 km [
13
]. At regional scales, analysis-ready layers of evapotranspi-
ration [
24
], fractional cover mapping [
11
] and land surface temperature [
25
] at a coarse
resolution (e.g., 500 m) have been applied. At sub-regional scales, terrain derivatives based
on digital elevation models (DEMs) have been used as surrogates of landscape wetness and
groundwater potential (e.g., Refs. [
3
,
26
]) and to identify suitable (and unsuitable) species
habitat (e.g., Refs. [27–30]).
Contemporary models often combine spectral data with auxiliary variables to express
the likelihood of a pixel being GDV [
30
]. This combination has been demonstrated to
improve upon spectral data alone [
31
]. There has been a dichotomy of approaches for
producing these models: (1) spatial multicriteria evaluation (SMCE) and (2) machine-
learning [
30
]. The major differences between them are that SMCE is based on human
judgement [
32
] whereas machine learning (e.g., random forests, neural networks) is not,
and, therefore, it is less prone to procedural mistakes and perceptual bias. However, neither
approach can be expected to outperform the other [
30
] as demonstrated by Abrams et al. [
33
]
for groundwater mapping. Recent examples of SMCE have used the weighted linear
combination method (e.g., Refs. [
11
,
34
]). A wider range of machine learning approaches
have been used and include maximum likelihood classification (e.g., Ref. [
3
]), probabilistic
frequency ratio (PFR; e.g., Ref. [
33
]), weights of evidence (e.g., Ref. [
35
]) and, in some cases,
an ensemble of several (e.g., Ref. [36]).
The Dempster–Shafer theory of evidence is another branch of modelling that recog-
nises that incomplete (fuzzy) knowledge exists in the lines of evidence (variables) being
used. It has been used for groundwater potential modelling (e.g., Refs. [
37
,
38
]) but appears
to be underutilised for GDV modelling per se. Like SMCE and machine learning, it can
combine multiple pieces of evidence to arrive at a model representing the likelihood (belief)

Land 2024,13, 2208 3 of 20
of GDV for each pixel. However, what distinguishes it from other techniques is its ability
to also produce other useful outputs, such as the plausibility model, which represents
locations where GDV could reside, even if it does not currently. These models make up the
lower (belief) and upper (plausibility) bounds that a hypothesis is true and the difference
between them is the degree of uncertainty, which triggers resurvey events.
The Pilbara region of Western Australia is a center of arid zone biodiversity and is
globally recognised as a major producer of high-quality iron ore [
39
]. As iron ore mines
in the area continue to mature, redirecting pit water (dewatering) to provide access to the
ore beneath is becoming more common [
40
]. This can result in a localised lowering of the
water table. If the water table is reduced beyond root limits, mortality of GDV can occur.
Knowing the location of GDV will greatly assist its protection via repeat monitoring and
intervention. However, none of the methods mentioned above, which are all based on
the “green islands” theory, are designed to map GDV if it is already unhealthy, dead or
previously existing. Therefore, we argue that GDV that can currently be detected from
satellite requires consistent monitoring, particularly of its response to drawdown, and
that all habitats that can plausibly host GDV, and thus can be used to infer a GDE, require
protection, particularly against the threat of diminished groundwater, intense bushfires,
invasive species, land clearing and other disturbances.
Our aim is to utilise a time series of remotely sensed imagery and digital elevation
model derivatives to create a suite of outputs that can inform land managers where GDV
currently exists and requires monitoring for change, where it could exist and requires
protection, where it is unlikely to exist and can be revisited less regularly and where we
need more information to improve our level of uncertainty. To achieve our aim, we seek to
(a) produce a range of phenometrics and determine those suitable for GDV discrimination;
(b) develop an ecological niche model showing suitable GDV habitat; (c) combine variables
using Dempster–Shafer theory of evidence modelling to produce the suite of model outputs
to define locations that require repeat monitoring, protection against disturbance and
follow-up surveys. We expect that healthy GDV will stay greener for longer than non-GDV
and thus phenometrics related to year-round greenness (e.g., productivity metrics) can be
utilised to differentiate GDV. We also expect that the areas close to waterbodies and other
depressions will provide the most suitable habitat. We apply our approach to a practical
case study in the southeast of the arid Pilbara region of Western Australia, where damming
has reduced the flow of water north of the dam wall, affecting vegetation communities,
including GDV, since the early 1980s.
2. Materials and Methods
2.1. Study Area
The Pilbara bioregion [
41
] is approximately 180,000 km
2
and is entirely contained
within the arid zone [
42
] of Western Australia (Figure 1). It is one of only 15 national biodi-
versity hotspots [
39
]. Mean annual precipitation varies spatially from around
300–350 mm
in the northeast to 250 mm in the south and west and is temporally unreliable with ex-
tended droughts not uncommon [
43
]. Mean annual potential evapotranspiration exceeds
3100 mm [
42
]. Most precipitation occurs in summer and early autumn (January to March).
The convective nature of summer rain means that large amounts can be received in a single
fall and is often highly localised. Winter rainfall (June–August) is usually much lower than
summer and autumn and only occurs, primarily, because of elongated southern latitude
fronts. Spring rain throughout the Pilbara is extremely low and usually restricted to rain in
November preceding the summer wet season [44].

Land 2024,13, 2208 4 of 20
Land 2024, 13, 2208 4 of 20
Figure 1. Study area located on the southeastern boundary of the Pilbara bioregion in the arid zone
of Australia. Elevation was sourced from the SRTM mission. Arid zone is based on Zomer et al. [42].
The Pilbara bioregion is based on the IBRA (2012) version 7 dataset [41]. Land system abbreviations
are defined in Table A2.
2.2. GDV Species
The flora in the Pilbara bioregion is diverse, with 1137 vascular species (1094 were
native) recorded during surveys undertaken in the late 1990s [44]. Phreatophytes are used
to infer the presence of a groundwater-dependent ecosystem [1]. These species draw water
from the phreatic zone and can usually be found along streams where there is a steady
flow of surface or groundwater or consistent pooling where the water table is near the
surface. They contrast in appearance from coexisting vadophytes (Figure 2A), which have
no dependence on groundwater. Eucalyptus victrix L.A.S. Johnson and K.D. Hill (Coolibah;
Figure 2B), E. camaldulensis subsp. refulgens Brooker and M.W. McDonald (River Red Gum;
Figure 2C) and Melaleuca argentea W. Fig (Silver Cadjeput; Figure 2D) are the three most
common “GDV species” in the Pilbara bioregion.
Figure 1. Study area located on the southeastern boundary of the Pilbara bioregion in the arid zone
of Australia. Elevation was sourced from the SRTM mission. Arid zone is based on Zomer et al. [
42
].
The Pilbara bioregion is based on the IBRA (2012) version 7 dataset [
41
]. Land system abbreviations
are defined in Table A2.
2.2. GDV Species
The flora in the Pilbara bioregion is diverse, with 1137 vascular species (1094 were
native) recorded during surveys undertaken in the late 1990s [
44
]. Phreatophytes are used
to infer the presence of a groundwater-dependent ecosystem [
1
]. These species draw water
from the phreatic zone and can usually be found along streams where there is a steady
flow of surface or groundwater or consistent pooling where the water table is near the
surface. They contrast in appearance from coexisting vadophytes (Figure 2A), which have
no dependence on groundwater. Eucalyptus victrix L.A.S. Johnson and K.D. Hill (Coolibah;
Figure 2B), E. camaldulensis subsp. refulgens Brooker and M.W. McDonald (River Red Gum;
Figure 2C) and Melaleuca argentea W. Fitzg (Silver Cadjeput; Figure 2D) are the three most
common “GDV species” in the Pilbara bioregion.
2.3. Study Site and Field Data
The study site, located in the southeast corner of the Pilbara bioregion, covers an area
of c. 20,000 ha in the vicinity of Ophthalmia Dam (Figure 1). The dam was constructed in
1981 by Mt. Newman Mining Company Pty Ltd. approximately 19 km east of the town
of Newman (Figure 1) for town and mining water supply. Post-construction, there has
been negligible downstream contribution and a restriction of flood events that is partially
responsible for vegetation condition decline north of the dam wall [45,46].

Land 2024,13, 2208 5 of 20
Land 2024, 13, 2208 5 of 20
Figure 2. (A) Groundwater-dependent vegetation species appear as “green islands” in the
background in contrast to the saltbush (Atriplex sp.) in the foreground, which has no groundwater
dependence. (B) Example of a healthy Eucalyptus victrix with a health rating of 5 (see text). (C)
Example of a E. calmuldulensis with a health rating of 4 due to some leaf loss on lower limbs. (D)
Example of a healthy Melaleuca argentea. (E) Acacia species, which are common vadophytes in the
uplands. (F) Dead mature M. argentea tree. (G) Dead E. calmuldulensis.
2.3. Study Site and Field Data
The study site, located in the southeast corner of the Pilbara bioregion, covers an area
of c. 20,000 ha in the vicinity of Ophthalmia Dam (Figure 1). The dam was constructed in
1981 by Mt. Newman Mining Company Pty Ltd. approximately 19 km east of the town of
Newman (Figure 1) for town and mining water supply. Post-construction, there has been
negligible downstream contribution and a restriction of flood events that is partially
responsible for vegetation condition decline north of the dam wall [45,46].
Field observations focused on GDV species around the dam and up to 16 km north
of it. These samples were acquired opportunistically using GPS-enabled tablets running
ArcGIS Collector [47] in late August 2019. GDV species were recorded along with a health
Figure 2. (A) Groundwater-dependent vegetation species appear as “green islands” in the background
in contrast to the saltbush (Atriplex sp.) in the foreground, which has no groundwater dependence.
(B) Example of a healthy Eucalyptus victrix with a health rating of 5 (see text). (C) Example of a
E. calmuldulensis
with a health rating of 4 due to some leaf loss on lower limbs. (D) Example of a
healthy Melaleuca argentea. (E)Acacia species, which are common vadophytes in the uplands.
(F) Dead
mature M. argentea tree. (G) Dead E. calmuldulensis.
Field observations focused on GDV species around the dam and up to 16 km north
of it. These samples were acquired opportunistically using GPS-enabled tablets running
ArcGIS Collector [
47
] in late August 2019. GDV species were recorded along with a health
score from 1–5, where 1 signified a dead plant, 3 signified branches without leaves and
5 was a perfectly healthy specimen. For the purposes of this study, we grouped GDV
species together and removed any with health ratings less than 4, leaving a sample size

Land 2024,13, 2208 6 of 20
of
119 observations
(Figure 1). This was randomly split into two 50% partitions used for
training (N = 60) and model validation (N = 59). We did not record non-GDV plant species.
M. argentea is considered an obligate phreatophyte, requiring permanent surface and
near-surface water for survival and has the most dependence on groundwater of the three
observed [
48
]. Mortality is rapid if groundwater becomes unavailable (Figure 2F). While it is
common near creeks throughout the Pilbara, it was not observed in our survey in the vicinity
of the dam. E. camuldulensis and E. victrix were observed. Both are facultative phreatophytes
because they can grow in areas where groundwater is not available and satisfy water
requirements from unsaturated zones but will use groundwater if it is available [
49
,
50
]. If
established over shallow groundwater, they are likely to develop groundwater dependence
and have higher vigour and better stand development due to increased water uptake [
51
].
However, such dependence makes them vulnerable if groundwater becomes unavailable,
depending on the rate of groundwater drawdown. There appears to be a correlation
between size and groundwater dependence, with the health of large E. camuldulensis trees
(>10 m tall) found to decline in response to groundwater levels lowering, while smaller
examples are less impacted [52].
2.4. Datasets
We acquired one cloud-free Sentinel 2A/B image from approximately the first week
of each month of 2019 from the Sentinel Australasia Regional Access (SARA) catalogue.
This was achievable for all months except April, which was interpolated by averaging the
March and May acquisitions (Table A1). We used the hydrologically enforced 30 m reso-
lution digital elevation model (DEM) acquired by the Shuttle Radar Topography Mission
(SRTM—https://pid.geoscience.gov.au/dataset/ga/72759 (accessed on 22 November 2024))
for all terrain derivatives. Land systems, which tie together geographical, geological and
ecological data, were sourced from van Vreeswyk et al. [
44
]. The land systems displayed in
Figure 1are defined in Table A2.
2.5. Phenometric Modelling
2.5.1. Moisture-Adjusted Vegetation Index
All Sentinel images were transformed into a time series of moisture-adjusted vegeta-
tion index (MAVI) layers as described by Zhu et al. [53]:
MAVI =NIR −RED
NIR +RED +SWIR (1)
where NIR is near-infrared reflectance (c. midpoint = 0.865
µ
m), RED is reflectance in
the red wavelengths (c. midpoint = 0.665
µ
m) and SWIR is reflectance in the short-
wave infrared (c. midpoint = 1.61
µ
m). These bands correspond to bands 8, 4 and 11
of Sentinel-2, respectively.
2.5.2. Description of Phenometrics
The MAVI time series was smoothed by fitting a Savitzky–Golay function using TIME-
SAT version 3.3 [
54
]. Ten phenometrics were extracted based on the smoothed function as
defined in Table 1. Figure 3provides an illustration to accompany the definitions.
2.5.3. Choosing Between Phenometrics
A correlation coefficient was calculated between all pairs of phenometrics to identify
variable redundancy. All phenometrics with a correlation
≥
0.90 were removed. The
remaining phenometrics were then run through MaxENT software v. 3.3.3 [
55
] singularly
using the training dataset (N = 60), and an area under the curve (AUC) was computed from
the corresponding receiver-operating characteristic (ROC) curve [
56
] based on the testing
dataset. A ROC curve is a graph of the false-positive rate (x-axis) against the true-positive
rate (y-axis). ROC curves that are close to the top left-hand corner illustrate a good-fitting
model and will have a high corresponding AUC [
57
]. The AUC is a summary performance

Land 2024,13, 2208 7 of 20
measurement that is calculated using the trapezoidal rule of the area under the ROC [
58
].
In our implementation, the AUC measures how much separability there is between GDV
and background samples (other land covers). An AUC of 1 indicates perfect separation,
whereas 0.5 denotes an unusable model because true positives and false positives are not
distinguishable [
59
]. We calculated the AUC for all variables together and proceeded to
reduce the full model by removing the poorest single model (lowest AUC) at each iteration.
This allowed for the most parsimonious set of phenometrics to be retained. We plot the
response curves of retained variables to describe the relationship between each phenometric
and GDV.
Table 1. Summary of phenometrics derived from the time series of Sentinel-2.
Name Abbrev. Definition Units
Length of Season LOS The number of months between the SOS and EOS Months
Base Value BV The average of the smallest left and right values MAVI
Max Value MV The maximum MAVI value in the time series MAVI
Amplitude AMP The difference between the MV and the base value BV MAVI
Start of Season SOS Where the left part of the function reaches 50% of the AMP MAVI
End of Season EOS Where the right part of the function reaches 50% of the AMP MAVI
Rate of Increase ROI The rate of vegetation “green-up” at the beginning of the season MAVI/months
Rate of Decrease ROD The rate of vegetation “green-down” at the end of the season MAVI/months
Large Integral LI Area under the curve between SOS and EOS MAVI ×months
Small Integral SI Area under the curve, above the BV, between SOS and EOS MAVI ×months
Land 2024, 13, 2208 7 of 20
Table 1. Summary of phenometrics derived from the time series of Sentinel-2.
Name Abbrev. Definition Units
Length of Season LOS The number of months between the SOS and EOS Months
Base Value BV The average of the smallest left and right values MAVI
Max Value MV The maximum MAVI value in the time series MAVI
Amplitude AMP The difference between the MV and the base value BV MAVI
Start of Season SOS Where the left part of the function reaches 50% of the AMP MAVI
End of Season EOS Where the right part of the function reaches 50% of the AMP MAVI
Rate of Increase ROI The rate of vegetation “green-up” at the beginning of the season MAVI/months
Rate of Decrease ROD The rate of vegetation “green-down” at the end of the season MAVI/months
Large Integral LI Area under the curve between SOS and EOS MAVI × months
Small Integral SI Area under the curve, above the BV, between SOS and EOS MAVI × months
Figure 3. Illustration of the phenometrics extracted from the Sentinel time series. See Table 1 for
definitions.
2.5.3. Choosing Between Phenometrics
A correlation coefficient was calculated between all pairs of phenometrics to identify
variable redundancy. All phenometrics with a correlation ≥ 0.90 were removed. The
remaining phenometrics were then run through MaxENT software v. 3.3.3 [55] singularly
using the training dataset (N = 60), and an area under the curve (AUC) was computed
from the corresponding receiver-operating characteristic (ROC) curve [56] based on the
testing dataset. A ROC curve is a graph of the false-positive rate (x-axis) against the true-
positive rate (y-axis). ROC curves that are close to the top left-hand corner illustrate a
good-fiing model and will have a high corresponding AUC [57]. The AUC is a summary
performance measurement that is calculated using the trapezoidal rule of the area under
the ROC [58]. In our implementation, the AUC measures how much separability there is
between GDV and background samples (other land covers). An AUC of 1 indicates perfect
separation, whereas 0.5 denotes an unusable model because true positives and false
positives are not distinguishable [59]. We calculated the AUC for all variables together and
proceeded to reduce the full model by removing the poorest single model (lowest AUC)
at each iteration. This allowed for the most parsimonious set of phenometrics to be
Figure 3. Illustration of the phenometrics extracted from the Sentinel time series. See Table 1
for definitions.
2.6. Ecological Niche Modelling
The SRTM DEM was resampled to 10 m resolution to match the Sentinel imagery
using cubic convolution in ArcGIS PRO v. 3.1 [
60
]. As topography is a first-order control
on the spatial variation of hydrological conditions, and groundwater flow often follows
surface topography [
61
], we derived three raster surfaces from the DEM, which have been
shown to be related to water runoff and pooling [
27
]: convexity, topographic position index
(TPI) and the saga wetness index (SWI). All metrics were computed in SAGA software

Land 2024,13, 2208 8 of 20
v. 7.8.2 [
62
]. Convexity (CON) controls the direction of flow and transport of materials
and deposition of soil. Hills have high convexity and thus high runoff, whereas incised
streams are concave in shape and thus receive and move water [
63
]. The TPI compares the
elevation of each cell in a DEM to the mean elevation of a specified neighbourhood around
that cell and was calculated using a 100
×
100 m neighbourhood window. Positive TPI
values represent locations that are higher than the average of their neighbourhood window
(e.g., ridges) and whose negative values are lower (e.g., valleys), with flat areas close to
0 [
64
]. The SWI was used to quantify the topographic control of hydrological processes [
65
].
Higher values receive more water. Finally, we also included the layer of land systems in
our model.
Variables were assessed for multicollinearity, where any with a correlation
≥
0.90 were
removed and the remaining set was run through MaxENT. Backward selection based on the
AUC was performed to retain the most parsimonious set of variables representing suitable
GDV habitats.
2.7. Uncertainty Modelling
Uncertainty modelling identifies that there may be incomplete knowledge in the
body of evidence (e.g., it may be anecdotal, indirect or come from different sources at
different scales and with some inherent inaccuracy). Uncertainty modelling frequently
uses the evidential belief function theory developed by Dempster [
66
,
67
] and added to by
Shafer [
68
], coined the Dempster–Shafer theory by Barnett [
69
]. This theory allows for the
mapping of the spatial distribution of uncertainty, where a pixel might contain GDV but
the combination of evidence is insufficient to be certain [70].
Unlike traditional Bayesian probability theory, under the Dempster–Shafer belief
theory, there is no requirement for probability not committed to a particular hypothesis to
be committed to its negation [
70
,
71
]. This is arranged via a frame of discernment (aka as
a universe of discourse) where all possible hypotheses and combinations of hypotheses
are disclosed. In our approach, we have two singleton hypotheses; a pixel is either GDV
or it is not GDV. Hence, our frame of discernment,
Θ
= {GDV, NOT GDV}, will accept
evidence for all possible combinations, {GDV}, {NOT GDV} and {GDV, NOT GDV}. The
latter, the non-singleton set, represents our ignorance or an inability to commit to either
singleton hypothesis.
Layers are assigned to the hypotheses, and each pixel is given a basic probability
assignment (BPA). A BPA represents the mass of support (m) of one of the hypotheses,
and not its proper subsets. For example, if the BPA representing the support that a piece
of evidence provides of an individual pixel supporting the hypothesis {GDV} is 0.6, then
m({GDV}) = 0.6 and the remainder is committed to the non-singleton hypothesis represent-
ing ignorance, m({GDV, NOT GDV}) = 0.4. It could be GDV but we do not know, and more
evidence is required. In our implementation, the phenometrics model was used to support
the hypothesis of {GDV} and the ENM was inverted and used to support the hypothesis
{NOT GDV}. We reasoned that suitable habitat does not ensure the presence of GDV species,
but unsuitable habitat is good evidence in support of a location not hosting GDV. As the
two models were scaled from 0 to 1, each pixel already had an appropriate BPA assigned.
The mathematics of Dempster–Shafer Belief Modelling (DSBM) allows for four map-
pable degrees of belief: belief, plausibility, disbelief, and belief interval [
72
]. Belief is the
total support for a hypothesis drawn from the BPAs for all subsets of that hypothesis [73]:
BEL(X) = ∑m(Y) when Y⊆X (2)
where BEL(X) is the total support for a hypothesis, drawn from the BPAs for all subsets of
that hypothesis. Thus, BEL({GDV}) is the sum of the BPAs, m(Y), for the hypothesis {GDV}
and represents the probability that a pixel is GDV.

Land 2024,13, 2208 9 of 20
Plausibility is the degree to which a hypothesis cannot be disbelieved; conditions
(e.g., habitat) appear to be right but hard evidence is lacking. It is calculated thusly [73]:
PL(X)=∑1−BELX(3)
where X = not X. Hence, PL({GDV}) is equal to 1 −BEL{NOT GDV} or 1 −DIS(X).
Disbelief is the degree of support for all hypotheses that do not intersect with that
hypothesis and is, therefore, clearly associated with plausibility [73]:
DIS(X)=∑BELX(4)
where X = not X. Hence, DIS({GDV}) is equal to BEL{NOT GDV}.
The range between plausibility (the upper bound of belief) and belief (the lower bound
of belief) is the belief interval and represents the doubt or uncertainty in the hypothesis [
73
]:
BELINT(X) = PL(X) −BEL(X) (5)
Image Thresholding
ROC analysis can also be used for determining the threshold values for dichotomising
continuous layers into binary layers. Several threshold determination methods exist, and
the choice between them is based on map use (e.g., Ref. [
74
]). We chose to balance the
importance of the false positive and true-positive rates, which corresponds to the value
closest to the top northwest corner of the ROC plot [
75
,
76
]. This was performed using
Youden’s J Statistic and identifying the corresponding cutoff [77]:
J = MAX(True-Positive Rate + False-Positive Rate −1) (6)
We used this approach to threshold the belief map to delineate likely GDV populations
that require follow-up monitoring of anomalous spectral responses that may indicate stress
from groundwater drawdown. We also used this technique to delineate a plausible habitat
that requires protection.
3. Results
3.1. Phenometric Modelling
Correlation analysis revealed considerable multicollinearity between the ten pheno-
metrics. Hence, only four were retained for further analysis as they had pairwise correla-
tions below 0.9 (Figure 4A). These were length of season (LOS), rate of increase (ROI), rate
of decrease (ROD) and the large integral (LI). The AUC statistics for each phenometric are
also presented in Figure 4A and illustrate the discrimination potential of several. The full
model combining all four uncorrelated phenometrics produced an AUC of 0.99, which did
not decline during backwards stepwise elimination. The only variable retained was the LI.
The response curve for the LI shows that the probability of GDV increases sigmoidally up
to a value of 600 (MAVI is stored as an 8-byte integer), with all higher values very likely
GDV (Figure 5).
3.2. Ecological Niche Modelling
None of the potential variables for ecological niche modelling (ENM) were found to
be highly correlated (r > 0.9), and so all were initially retained (Figure 4B). As the land
systems were categorical, they were not tested against the other variables. AUC statistics
computed for each variable on their own revealed that the land systems were the most
accurate layer (AUC = 0.85). The full model produced an AUC = 0.91. Stepwise removal
showed no reduction in AUC when CON and the SWI were removed but did show a slight
reduction (AUC = 0.89) when the TPI was, and so it was retained. The final ENM had an
AUC = 0.92 (an improvement on the full model) and identified the key determinant of GDV

Land 2024,13, 2208 10 of 20
habitat to be the river land system, which delineates seasonally active flood plains and
major river channels (Figure 5B) and where the TPI is negative (Figure 5C).
Land 2024, 13, 2208 10 of 20
Figure 4. Correlation matrix with individual area under the curve (AUC) statistics for (A)
phenometric variables defined in Table 1 and (B) DEM derivatives (CON = convexity, TPI =
topographic position index, SWI = SAGA wetness index) used in ecological niche modelling.
Figure 4. Correlation matrix with individual area under the curve (AUC) statistics for (A) phenometric
variables defined in Table 1and (B) DEM derivatives (CON = convexity, TPI = topographic position
index, SWI = SAGA wetness index) used in ecological niche modelling.

Land 2024,13, 2208 11 of 20
Land 2024, 13, 2208 11 of 20
Figure 5. Response curves for each of the retained variables of (A) the large integral, where MAVI
is expressed as an 8-byte integer. The probability of GDV increases with higher values of the large
integral. (B) The topographic position index showing the probability of GDV increases when it is
negative. (C) Land systems showing the majority of GDV samples are found within the “River” land
system (RGERIV) with a minor association with the “Newman” land system (RGENEW). See Table
A2 for other definitions.
3.2. Ecological Niche Modelling
None of the potential variables for ecological niche modelling (ENM) were found to
be highly correlated (r > 0.9), and so all were initially retained (Figure 4B). As the land
systems were categorical, they were not tested against the other variables. AUC statistics
computed for each variable on their own revealed that the land systems were the most
accurate layer (AUC = 0.85). The full model produced an AUC = 0.91. Stepwise removal
showed no reduction in AUC when CON and the SWI were removed but did show a slight
reduction (AUC = 0.89) when the TPI was, and so it was retained. The final ENM had an
AUC = 0.92 (an improvement on the full model) and identified the key determinant of
GDV habitat to be the river land system, which delineates seasonally active flood plains
and major river channels (Figure 5B) and where the TPI is negative (Figure 5C).
Figure 5. Response curves for each of the retained variables of (A) the large integral, where MAVI
is expressed as an 8-byte integer. The probability of GDV increases with higher values of the large
integral. (B) The topographic position index showing the probability of GDV increases when it is
negative. (C) Land systems showing the majority of GDV samples are found within the “River”
land system (RGERIV) with a minor association with the “Newman” land system (RGENEW). See
Table A2 for other definitions.
3.3. Uncertainty Modelling
The model based on the phenometrics was used in support of the hypothesis {GDV}
and is shown in Figure 6A. The high-likelihood areas are those that are greener for longer
periods of the year than other vegetation and, therefore, have the characteristics expected
to be GDV (Figure 6A). The inverted ecological niche model is used in support of the
hypothesis of {NOT GDV} and shows the upland areas as habitats that are highly likely to
be suitable for non-GDV (Figure 6B).

Land 2024,13, 2208 12 of 20
Land 2024, 13, 2208 12 of 20
3.3. Uncertainty Modelling
The model based on the phenometrics was used in support of the hypothesis {GDV}
and is shown in Figure 6A. The high-likelihood areas are those that are greener for longer
periods of the year than other vegetation and, therefore, have the characteristics expected
to be GDV (Figure 6A). The inverted ecological niche model is used in support of the
hypothesis of {NOT GDV} and shows the upland areas as habitats that are highly likely
to be suitable for non-GDV (Figure 6B).
Figure 6. Groundwater-dependent vegetation model results. (A) The BELIEF map, which represents
locations in support of GDV presence. Thresholding GDV is delineated in blue. (B) The DISBELIEF
map, which represents locations in support of GDV absence. (C) The PLAUSIBILITY map, which
illustrates potential GDV habitat that needs protection. Suggested habitat for protection is
delineated in purple. (D) The BELIEF INTERVAL map, where high belief intervals present an
opportunity for further sampling to reduce uncertainty.
Figure 6. Groundwater-dependent vegetation model results. (A) The BELIEF map, which represents
locations in support of GDV presence. Thresholding GDV is delineated in blue. (B) The DISBELIEF
map, which represents locations in support of GDV absence. (C) The PLAUSIBILITY map, which
illustrates potential GDV habitat that needs protection. Suggested habitat for protection is delineated
in purple. (D) The BELIEF INTERVAL map, where high belief intervals present an opportunity for
further sampling to reduce uncertainty.
The plausibility map is a complement of the map of disbelief and shows locations
where the habitat is suitable for GDV (Figure 6C) but does not infer GDV to be present
(unlike the belief map). It shows the upper boundary of our commitment to the hypothesis
{GDV} and hence has a wider range of suitable locations relative to the map of belief. Highly

Land 2024,13, 2208 13 of 20
plausible areas closely follow the stream network. The belief interval represents the degree
of uncertainty in a pixel, satisfying either the belief or the disbelief hypotheses (Figure 6D).
The areas with the highest belief interval have habitats that appear to be suitable for GDV,
but there is currently no concrete evidence of it being there. These areas are predominantly
on the banks of watercourses in proximity to existing and predicted GDV and represent
locations where follow-up field visits would have the most value in reducing uncertainty.
Areas shaded in white to light brown satisfy either the belief or disbelief hypotheses and
no further information is required.
Image Thresholding
ROC curves corresponding to the belief and plausibility models are shown in Figure 7.
The optimal threshold for the belief model was found to be a model value > 0.75. This
threshold allows for a 2% false-positive rate and a 98% true-positive rate (see blue lines,
Figure 7A). This means that any thresholded pixel chosen at random will be GDV 98% of
the time. Rarely (2% of the time), it will be a different vegetation type with a similar spectral
response to GDV. The patches of GDV are shown in blue in Figure 6A and indicate plants
that need to be monitored for changes in vigour. The optimal threshold for the plausibility
model was 0.58 and returned a false-positive rate of 19% and a true-positive rate of 96%
(see purple lines, Figure 7B). The preferred GDV habitat is shown delineated in purple in
Figure 6C and indicates habitats that need to be protected from disturbances.
Land 2024, 13, 2208 13 of 20
The plausibility map is a complement of the map of disbelief and shows locations
where the habitat is suitable for GDV (Figure 6C) but does not infer GDV to be present
(unlike the belief map). It shows the upper boundary of our commitment to the hypothesis
{GDV} and hence has a wider range of suitable locations relative to the map of belief.
Highly plausible areas closely follow the stream network. The belief interval represents
the degree of uncertainty in a pixel, satisfying either the belief or the disbelief hypotheses
(Figure 6D). The areas with the highest belief interval have habitats that appear to be
suitable for GDV, but there is currently no concrete evidence of it being there. These areas
are predominantly on the banks of watercourses in proximity to existing and predicted
GDV and represent locations where follow-up field visits would have the most value in
reducing uncertainty. Areas shaded in white to light brown satisfy either the belief or
disbelief hypotheses and no further information is required.
Image Thresholding
ROC curves corresponding to the belief and plausibility models are shown in Figure
7. The optimal threshold for the belief model was found to be a model value >0.75. This
threshold allows for a 2% false-positive rate and a 98% true-positive rate (see blue lines,
Figure 7A). This means that any thresholded pixel chosen at random will be GDV 98% of
the time. Rarely (2% of the time), it will be a different vegetation type with a similar
spectral response to GDV. The patches of GDV are shown in blue in Figure 6A and indicate
plants that need to be monitored for changes in vigour. The optimal threshold for the
plausibility model was 0.58 and returned a false-positive rate of 19% and a true-positive
rate of 96% (see purple lines, Figure 7B). The preferred GDV habitat is shown delineated
in purple in Figure 6C and indicates habitats that need to be protected from disturbances.
Figure 7. Receiver operating characteristic (ROC) curves illustrating the true and false-positive rates
overall threshold values. Chosen thresholds are shown in blue and purple and correspond to the
models of (A) belief and (B) plausibility, respectively, and relate to the delineations in the same
colour in Figure 6.
Figure 7. Receiver operating characteristic (ROC) curves illustrating the true and false-positive rates
overall threshold values. Chosen thresholds are shown in blue and purple and correspond to the
models of (A) belief and (B) plausibility, respectively, and relate to the delineations in the same colour
in Figure 6.
4. Discussion
The importance of groundwater for maintaining the health of GDV and associated
ecosystems has become more widely recognised in recent years. Nonetheless, GDV remains
under threat globally due to unsustainable levels of abstraction, causing groundwater
depletion [
78
]. The ongoing monitoring of existing GDV communities and the protection
of highly suitable habitats is needed urgently but this will be underpinned by accurately
knowing their current locations and spatially defining their environmental niche, respec-

Land 2024,13, 2208 14 of 20
tively. Our uncertainty modelling framework demonstrates that this can be achieved by
leveraging a combination of phenometic and ecological niche modelling.
4.1. Phenometric Modelling
The use of a time series of remotely sensed imagery, rather than annual or biannual
acquisitions (e.g., Ref. [
15
]), has previously been recommended for mapping GDV [
79
]
and has led to an uptake of various time series-based approaches (e.g., Refs. [
3
,
25
,
80
]).
Nonetheless, phenometric modelling for GDV mapping remains rare, although research is
emerging on its use for monitoring the impact of different mining-induced disturbances
on the surrounding vegetation [
81
,
82
]. In our implementation, we identified the large
integral to be the most useful phenometric for characterising GDV, and it was able to
differentiate GDV from non-GDV almost perfectly (AUC = 0.99). This was achieved as it
relates to the evergreen appearance of GDV that contrasts with seasonally green vegetation,
like grasses, or less vigorous vadophytes, which are not dependent on groundwater. It
also demonstrates that the 10 m resolution of Sentinel is a suitable resolution to minimise
spectral mixing and thus confusion of these land covers, which was not achieved using the
30
×
30 m resolution Landsat imagery in the study by Barron et al. [
16
]. As this evergreen
characteristic is generally ubiquitous amongst all GDV, we expect that the large integral will
be portable to most systems. One notable exception is blue oak trees (Quercus douglasii) in
California [
83
], which are GDV but also deciduous. Even so, other phenometrics are likely
to provide discrimination and the procedure for choosing between them will not change.
In addition to the large integral, we identified other candidate phenometrics with
discrimination potential based on individual AUC values (Figure 4A), including the start
of season (SOS) and end of season (EOS). Conceptually, SOS and EOS are akin to selecting
imagery at the start of the dry season and at the end of the wet season. This is the theory
underpinning the GEM model [
16
] and may partially explain why they were able to obtain
high accuracy (e.g., 91%) in some areas with only two images. However, this needs further
testing and expansion.
4.2. Ecological Niche Modelling
The TPI, CON and SWI were all found to be strong variables with AUC values
between 0.78 and 0.80, which is consistent with the findings of recent similar studies
(e.g., Refs. [3,34]).
However, we were able to omit CON and SWI without loss of model
accuracy. We also found that most GDV was confined to one land system (“River”), which
delineates major river channels and serves as a surrogate for proximity to water as used by
Duran-Llacer et al. [
34
]. Our response curves identified that GDV preferred negative TPI
values, which indicates suitable habitats where the elevation is lower than its immediate
surrounds (e.g., incised streams).
4.3. Uncertainty Modelling and Image Thresholding
ROC thresholding delineated 111 ha as GDV requiring repeat monitoring (Figure 6A).
This equates to narrowing the search space for existing GDV to less than 1% of the study
area and to be suitable for drone missions for more detailed exploration. However, unlike
Bayesian modelling, this does not allow one to assume the other 99% is unsuitable. For
example, plausibility modelling identified 2653 ha, or around 13%, of suitable habitat for
GDV, even if it is not currently growing there. The complement of this is the disbelief
model, which suggests that 87% of the study area is not suitable for GDV and can be
monitored infrequently.
The plausible areas were identified throughout the riparian zone and require protection
to deliver ecosystem resilience against the threat of intense bushfires, invasive species,
land clearing and other disturbances to counterbalance anthropogenic impacts, including
mining and pastoralism. Due to their rich biodiversity, riparian zones are also known to
be effective corridors for a variety of fauna, including feral cats, which threaten important

Land 2024,13, 2208 15 of 20
native bird species that utilise the existing GDV as safe havens for nesting and migration,
in addition to ground-dwelling mammals (e.g., quolls and bilbies) and reptiles [39,84].
Weeds are another major threat to riparian zones, as they compete with native vegeta-
tion [
39
,
85
], including GDV, and modify habitat for native fauna, often amplifying the issue
of feral fauna (cats and pigs). The most significant weeds are ecosystem-transforming [
39
]
and include woody perennials such as highly invasive mesquite (Prosopis spp.) popula-
tions, parkinsonia (Parkinsonia aculeata), calotropis (Caloptropis procera) and date palms (Phoenix
dactylifera). Protection must include the immediate removal of these species from
GDV habitat.
The belief interval (Figure 6D) highlights locations where GDV has not been detected
spectrally but that are highly plausible habitats. This layer is key to reducing uncertainty in
future iterations. This can be achieved through additional surveys or new layers of spatial
information. For example, Brim Box et al. [
80
] used a depth of groundwater layer to rule
out the potential for GDV. In their study, if the groundwater was beyond 10 m, then it was
deemed unsuitable habitat for Melaleuca species. Similarly, E. victrix stands where the depth
of groundwater exceeded 10 m were unlikely to be utilising the groundwater and so were
ruled out. This would require a high-resolution layer of groundwater interpolated from a
network of piezometers. While bore fields do exist throughout the Pilbara region, they are
too scattered to be relied on as a persistent data source for modelling. Generally, for much
of the Pilbara region, the presence of GDV species has been used to infer groundwater
depth, rather than the other way around.
4.4. Recommendations
For simplicity, we have grouped GDV species together. One source of heterogeneity is
thus the proportion of each individual species at different locations. Hence, the next level
of mapping should consider species discrimination or at least obligate versus facultative
phreatophytes. Obligate species have no drought resistance and die rapidly without access
to groundwater [
86
]. This is important because obligate species (e.g., M. Argentea) require
different groundwater resource management and likely even stricter monitoring than the
facultative species. In addition, we recommend the mapping of non-GDV species to rule
them out as false positives.
There is a paucity of freely available digital elevation models for the study area. This
required resampling 30 m resolution SRTM data to 10 m. We suspect some smoothing
may have occurred in the resampling because there are very fine detailed locations in the
belief map, based on 10 m Sentinel imagery, where GDV is likely to occur, which escapes
the plausibility map, which was based on resampled SRTM (c.f. Figure 6A,C). Lidar and
multispectral sensors attached to a drone would make for an impressive dataset for this
purpose, as adopted by Tweed et al. [
19
] and Perez Hoyos et al. [
13
]. We recommend that it
be captured monthly to enable the creation of a similar set of phenometrics.
5. Conclusions
Modelling under uncertainty provides useful geospatial intelligence in the pursuit
of sustainable groundwater management. GDV, at our study site, can be modelled and
mapped based on the characteristic that it will, when healthy, remain greener for longer
than non-GDV. The large integral of a time series of moisture-adjusted vegetation indices
captures this characteristic well and can considerably reduce the search space for monitoring
(e.g., <1% of the study area).
Suitable habitat could be explained by a combination of land systems and topographic
position, where negative topographic positions (e.g., valleys) were favoured. These ar-
eas could plausibly host GDV and require protection against the threat of groundwater
abstraction, intense bushfires, invasive species, land clearing and other disturbances.
The greatest uncertainty was found in locations where GDV appears to be absent
but the habitat is suitable. These are the locations requiring more investigation to refine
the model. This may include follow-up surveys or the introduction of new information
(e.g., depth of groundwater).

Land 2024,13, 2208 16 of 20
Author Contributions: Conceptualisation, T.P.R., L.T. and G.W.W.-J.; methodology, T.P.R., L.T. and
G.W.W.-J.; validation, T.P.R. and L.T.; formal analysis, T.P.R. and L.T.; investigation, T.P.R. and
L.T.; data curation, T.P.R. and L.T.; writing—original draft preparation, T.P.R.; writing—review and
editing, T.P.R., L.T. and G.W.W.-J.; visualisation, T.P.R. and L.T.; project administration, T.P.R.; funding
acquisition, T.P.R. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Spatial Information Systems Research Ltd., Project No
5E01—Measuring and Monitoring Vegetation Health Impact through Earth Observation.
Data Availability Statement: The original contributions presented in this study are included in the
article; further inquiries can be directed to the corresponding author.
Acknowledgments: The authors would like to thank Bart Huntley and Jennifer Carter for their
feedback on earlier versions of this manuscript and all members of the ENVestigator team for helpful
discussion throughout the project. Thank you to Mandy Robinson for assistance with proofreading.
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
Appendix A
Table A1. Sentinel imagery acquired for this study.
Collection Acquisition Date Platform Instrument Product Orbit Number Processing Level
S2 9 January 2019 S2B MSI S2MSIL2A 17 L2A
S2 3 February 2019 S2A MSI S2MSIL2A 17 L2A
S2 5 March 2019 S2A MSI S2MSIL2A 17 L2A
-Interpolated - - - - -
S2 4 May 2019 S2A MSI S2MSIL2A 17 L2A
S2 3 June 2019 S2A MSI S2MSIL2A 17 L2A
S2 3 July 2019 S2A MSI S2MSIL2A 17 L2A
S2 7 August 2019 S2B MSI S2MSIL2A 17 L2A
S2 5 September 2019 S2B MSI S2MSIL2A 17 L2A
S2 5 October 2019 S2B MSI S2MSIL2A 17 L2A
S2 5 November2019 S2B MSI S2MSIL2A 17 L2A
S2 5 December 2019 S2B MSI S2MSIL2A 17 L2A
Table A2. Land systems found within the study area.
Code Name Description
RGEBGD Boolgeeda Stony lower slopes and plains below hill systems supporting hard and soft spinifex grasslands or
mulga shrublands.
RGECAD Cadgie Hardpan plains with thin sand cover and sandy banks supporting mulga shrublands with soft and
hard spinifex.
RGEDIV Divide
Gently undulating sandplains with minor dunes, supporting hard spinifex hummock grasslands with
numerous shrubs.
RGEELI Elimunna
Stony plains on basalt supporting sparse acacia and cassia shrublands and patchy tussock grasslands.
RGEFAN Fan Washplains and Gilgai plains supporting groved mulga tall shrublands and minor
tussock grasslands.
RGEFTC Fortescue Alluvial plains and flood plains supporting patchy grassy eucalypt and acacia woodlands and
shrublands and tussock grasslands.
RGEJAM Jamindie Stony hardpan plains and rises supporting groved mulga shrublands, occasionally with
spinifex understorey.
RGEMCK McKay Hills, ridges, plateaux remnants and breakaways of meta sedimentary and sedimentary rocks
supporting hard spinifex grasslands with acacias and occasional eucalypts.
RGENEW Newman Rugged jaspilite plateaux, ridges and mountains supporting hard spinifex grasslands.
RGERIV River
Narrow, seasonally active flood plains and major river channels supporting moderately close, tall
shrublands or woodlands of acacias and fringing communities of eucalypts, sometimes with tussock
grasses or spinifex.
RGEROC Rocklea
Basalt hills, plateaux, lower slopes and minor stony plains supporting hard spinifex and occasionally
soft spinifex grasslands with scattered shrubs.
RGESPH Spearhole Gently undulating gravelly hardpan plains and dissected slopes supporting groved mulga
shrublands and hard spinifex.
RGESYL Sylvania Gritty surfaced plains and low rises on granite supporting acacia–eremophila–cassia shrublands.
RGEWNM Wannamunna Hardpan plains and internal drainage tracts supporting mulga shrublands and woodlands and
occasionally eucalypt woodlands.
RGEWSP Washplain Hardpan plains supporting groved mulga shrublands.

Land 2024,13, 2208 17 of 20
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