Assessing the hydrology of Victoria’s
wetlands using remote sensed imagery: a
pilot study
B. Cant, P. Griffioen and P. Papas
June 2012
Arthur Rylah Institute for Environmental Research
Technical Report Series No. 228
Assessing the hydrology of Victorian wetlands using
remotely sensed imagery: a pilot study
Belinda Cant1, Peter Griffioen2 and Phil Papas1
1
Arthur Rylah Institute for Environmental Research
123 Brown Street, Heidelberg, Victoria 3084
2
Peter Griffioen
Peter Griffioen Consulting
Heidelberg Vic 3084
June 2012
Arthur Rylah Institute for Environmental Research
Department of Sustainability and Environment
Heidelberg, Victoria
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Citation: Cant, B., Griffioen, P. and Papas, P. (2012). Assessing the hydrology of Victorian wetlands using remotely
sensed imagery: a pilot study. Arthur Rylah Institute for Environmental Research Technical Report Series No. 228.
Department of Sustainability and Environment, Heidelberg, Victoria
ISSN 1835-3827 (print)
ISSN 1835-3835 (online)
ISBN 978-1-74287-471-5 (print)
ISBN 978-1-74287-472-2 (online)
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Front cover photo: Wetland in south-west Victoria
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Contents
Acknowledgements...........................................................................................................................iv
Summary ............................................................................................................................................1
1
1.1
Introduction .............................................................................................................................2
Current knowledge of wetland hydrology in Victoria ..............................................................2
1.2
MODIS imagery .......................................................................................................................4
2
Methods....................................................................................................................................5
The steps involved in the development of models to predict water regime were as follows. .............5
2.1
Pre-processing MODIS data to align with VICGRID ..............................................................5
2.2
Analysis of imagery ..................................................................................................................5
2.3
Development of the Artificial Neural Networks (ANNs) .........................................................6
2.4
Hydrological classification used for this study .........................................................................7
2.5
Reasons for the failure to detect water ......................................................................................7
2.6
Validation and confidence ........................................................................................................8
3
3.1
Results ......................................................................................................................................9
Water regime categories ...........................................................................................................9
3.2
Validation: model discrimination and reliability ....................................................................10
4
Discussion ..............................................................................................................................10
5
References ..............................................................................................................................13
Appendix: Features of MODIS data relevant to wetlands ..........................................................15
iii
Acknowledgements
Funding for the project was provided by the Department of Sustainability and Environment’s
Natural Resources Investment Program. Comments on the draft were provided by Dr Kay Morris,
Dr Andrea White, Janet Holmes and Matt White (Department of Sustainability and Environment).
iv
Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
Summary
At present there is no empirical information about hydrology for the vast majority of wetlands in
Victoria. In this report we present a pilot study that explores an approach to estimating wetland
water regimes (wetting and drying cycles) from remotely sensed imagery. We applied this
approach to assessing the water regimes of Victoria’s wetlands over an 11-year period, from
March 2000 to February 2011. MODIS imagery was used as the primary source of data for
modelling the presence of surface water because its temporal resolution allows intra-annual and
inter-annual surface water variability to be assessed. The minimum spatial resolution of MODIS is
250  250 m (6.25 ha). Therefore the attribution of water regime data to wetlands using MODIS
imagery is constrained to wetlands larger than about 6 ha (approximately 40% of wetlands in the
Victorian Wetlands 1994 inventory). Additionally, MODIS sensors may not detect the presence of
water accurately if it is under vegetation cover.
The presence of water was estimated from a supervised classification of MODIS data, using an
artificial neural network (ANN). The classification used the four MODIS reflected solar bands
(blue, red, near infrared and medium infrared), Normalised Difference Vegetation Index (NDVI)
and Enhanced Vegetation Index (EVI). For training data we used a manual selection of 5000
pixels in many known wetlands of a variety of types and landscape contexts that appeared to
contain water in an average image, and a random selection of 10000 pixels of dry land.
MODIS detected water at 1204 (20%) of the 6740 wetlands greater than 6 ha. Of these 1204
wetlands, 39% (473) were classified as episodic, 31% (376) as seasonal and 22% (264) as
intermittent, and 7% (91) as permanent. Many wetlands may have remained unusually dry over the
period because of prevailing drought conditions.
Predictions were tested for validation and confidence against 71 wetlands that were observed to
contain some water during state-wide Index of Wetland Condition (IWC) field assessments in
2010 and 2011. MODIS correctly detected the presence of water at 32% of these wetlands. The
low proportion of wetlands in which water was correctly detected by MODIS was attributed
largely to the limitations of the training data. Vegetation cover (both canopy cover and emergent
wetland vegetation) also limit the ability of MODIS to detect water.
Steps for improving the utility of MODIS for detecting water and characterising wetland
hydrology are presented.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
1 Introduction
The hydrology of wetlands is defined by their water regime (duration, frequency and timing of
inundation), water source and depth of inundation (Reddy and DeLaune 2008). Understanding the
hydrological processes of wetlands is fundamental to effective ecosystem restoration and creation
(Mitsch and Gosselink 2000). The hydrology of wetlands in Victoria needs to be characterised to
inform policy, decision-making and the management of wetlands because hydrology is the key
driver of their development and continuing existence (Bedford 1996). A hydrogeological
understanding of wetlands also provides a framework for explaining the diversity of wetlands
across the landscape (Bedford 1996).
In keeping with contemporary Victorian wetland policy documents (DSE 2005), a wetland is
defined here as any natural or artificial area that (a) is permanently, periodically or intermittently
inundated, (b) when inundated, holds still or very slowly moving water, and (c) has or could
develop a biota adapted to inundation and the aquatic environment. This definition includes
waterbodies such as lakes, swamps, fens, marshes, peatlands, springs and some intertidal areas.
Not all these types of wetlands were included in the analysis (see Section 2.2).
This report outlines the methods and results for a pilot study that explored the utility of remotely
sensed data from MODIS imagery to determine wetland water regimes for Victoria’s wetlands.
Wetland water regimes were classified according to the new Victorian wetland classification (DSE
in prep.). The objectives of the project were to:



test the utility of MODIS imagery for detecting the presence of water in wetlands larger
than 6 ha
develop a data analysis procedure to assess seasonal and annual patterns of surface water
presence in wetlands during 2000–2011
assign water regime categories to wetlands (as defined here) that are represented on the
Wetland 1994 inventory, based on the outputs of the data analysis procedure.
1.1 Current knowledge of wetland hydrology in Victoria
While wetland hydrology is widely accepted as the most important driver of wetland function and
diversity, it has been difficult to elucidate at broad spatial scales. Expensive equipment required to
measure hydrology and the long time needed for such a study have made it prohibitive to collect
empirical data for characterising wetland hydrology for any more than a few wetlands in each
study (e.g. Williams 1978, 1981, 1992; Jones et al. 2001).
Where studies attempted to describe the hydrology of multiple wetlands at a landscape scale,
surrogates were used instead of hydrological data. These surrogates were based on assumed
geomorphic relationships (e.g. shallow depressions were assumed to be non-permanent wetlands),
or the use aerial photo analysis of one point in time and ancillary vegetation data to build a
qualitative temporal understanding of hydrology. The Corrick system, which has been used for the
last three decades to categorise wetlands in Victoria, provides an example of this non-empirical
method. Wetland categories developed by Corrick include water regime attributes such as water
depth and the duration and timing of inundation (Corrick 1981, 1982) (Table 1).
At present there is no empirical information about hydrology for the vast majority of wetlands in
Victoria. We present an empirical, longitudinal study and methods for estimating wetland water
regime (wetting and drying cycle) from remotely sensed imagery.
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
Table 1. The Corrick wetland classification system currently used in Victoria (Corrick 1981, 1982).
Category
Subcategory
Depth (metres)
Sewage ponds
Undefined
Undefined
Salt works
Undefined
Undefined
Herb-dominated
Sedge-dominated
Red gum-dominated
Lignum dominated
< 0.3
Herb-dominated
Sedge-dominated
Cane grass-dominated
Lignum-dominated
Red gum-dominated
< 0.5
Shrub-dominated
Reed-dominated
Sedge-dominated Rushdominated
Open water
Cane grass-dominated
Lignum-dominated
Red gum-dominated
<2
Shallow
Deep
Impoundment
Red gum
Cane grass
Dead timber
Black box
Rush
Reed
Sedge
Shrub
Lignum
<2
>2
Salt pan
Salt meadow
Salt flats
Sea rush
Hypersaline lake
Melaleuca
Dead timber
<2
Shallow
Deep
Intertidal flats
<2
>2
Freshwater meadow
These include shallow (up to 0.3 m) and temporary (less than
four months duration) surface water, although soils are generally
waterlogged throughout winter.
Shallow freshwater marsh
Wetlands that are usually dry by mid-summer but fill again with
the onset of winter rains. Soils are waterlogged throughout the
year, and surface water up to 0.5 m deep may be present for as
long as eight months.
Deep freshwater marsh
Wetlands that generally remain inundated to a depth of 1–2 m
throughout the year.
Permanent open freshwater
Wetlands that are usually more than 1 m deep. They can be
natural or artificial. Wetlands are considered permanent if they
retain water for longer than 12 months, although they can have
periods of drying.
Semi-permanent saline
These wetlands may be inundated to a depth of 2 m for as long
as eight months each year. Saline wetlands are those in which
salinity exceeds 3000 mg/L throughout the year.
Permanent saline
These wetlands include coastal wetlands and part of intertidal
zones. Saline wetlands are those in which salinity exceeds
3000 mg/L throughout the year.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
1.2 MODIS imagery
The MODIS instrument takes images of the entire surface of the Earth every one to two days
(NASA 2012). Images obtained over a 16-day period are made into one composite image and are
freely available from NASA. Data acquired by MODIS was used to model surface water because
of its spatial coverage and because its high temporal resolution allowed intra and inter-annual
surface water variability to be assessed. The minimum spatial resolution of MODIS is 250  250 m
(6.25 ha). Therefore, the attribution of water regime data to wetlands using MODIS imagery is
constrained to wetlands larger than about 6 ha.
Another constraint was that vegetation cover in a wetland (i.e. canopy or emergent vegetation) will
produce an estimate of dry rather than wet, whether or not the wetland is holding water. This is
because the training data defined open water as wet and terrestrial areas as dry to supervise the
classification.
The MODIS dataset used for this research contains four solar reflectance bands, centred at
wavelengths of approximately 469, 645, 858, and 2114 nm. The utility of indices derived from
MODIS composite images within these bands for assessing hydrologic conditions has been
demonstrated in several studies (Johnston and Barson 1993, Ordoyne and Friedl 2008) (see
Appendix).
MODIS data has a number of benefits, namely:
1.
2.
3.
4.
it has a relatively high temporal resolution/coverage (16 days)
it is open-access
the original raw images are pre-filtered for clouds and artefacts
a variety of complete MODIS products such as composite images are available at three
resolutions: 250 m, 500 m and 1000 m
5. efficient tools exist to obtain MODIS products and import them to GIS applications.
In contrast, higher spatial resolution imagery (e.g. De Alwis et al. 2007, Proisy et al. 2007, Poulin
et al. 2010, Lu et al. in press) will produce highly accurate estimates of water extent but has
limited utility for assessing wetland hydrological dynamics. This is because the the images are not
taken often enough to enable an analysis over short periods such as seasons or years.
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
2 Methods
The steps involved in the development of models to predict water regime were as follows.
1. Align MODIS data to the VICGRID projection.
2. Identify the most suitable (cloud free) images for each season and year.
3. Modify the Wetland 1994 dataset to exclude tidal wetlands and assign a 250 m buffer to each
wetland to account for incorrect positioning of some wetlands.
4. Develop Artificial Neural Networks (ANN) to predict presence of water.
5. Assign a hydrological classification (i.e. permanent, seasonal, intermittent, episodic) based on
model results.
2.1 Pre-processing MODIS data to align with VICGRID
Terra satellite image composites (MOD13Q1.005) from March 2000 to February 2011 were
acquired. MODIS is provided in sinusoidal map projection in which image pixels are distorted
when compared to more common map projections such as VICGRID94 which is widely used in
Victoria. To overcome this, MODIS raw images were first resampled onto VICGRID94.
2.2 Analysis of imagery
The presence of water was estimated from MODIS 16-day atmospherically corrected bi-directional
surface reflectances that had been masked for clouds, heavy aerosols and cloud shadows. In order
to ensure reasonable processing time, one average best-quality (cloud free) image was selected for
each season and calendar year. Images were created by first using a novel algorithm to search
through each set of images acquired for a season to select pixels with the highest possible quality.
These were then averaged to produce a single best-quality image for that season and year.
The Wetlands 1994 spatial dataset was modified to include only non-tidal wetlands greater than
6 ha. This left 6740 wetlands to be submitted to the model. A 250 m buffer was delineated around
these wetlands to overcome a potential displacement error in the dataset that can result in a
wetland occurring up to 250 m from its true location. This is a large buffer and represents a
conservative approach to correcting the error. A 100 m buffer may have sufficed in most instances,
but it was decided to maximise the chance that a pixel being counted as having water present
actually did so (Figure 1).
Figure 1. An example of the application of the 250 m
and 100 m buffers. Each pixel represents yearly
average value of relevant MODIS bandwidths. The
black line is the Wetland 1994 boundary, the red line
is a 100 m buffer and the green line is a 250 m buffer.
Bright purple indicates water present and all other
colours indicate water is absent. The wetland is
mapped approximately 250 m south-east of its true
location (the location that holds water on average
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
throughout the year).
2.3 Development of the Artificial Neural Networks (ANNs)
From a MODIS image we considered a pixel that could either contain water (labelled 1) or be dry
(labelled 0). Satellite images provide radiance measurements from that pixel at various
wavelengths, which constitute the inputs to the ANNs. A radial basis function neural network
(Broomhead 1988, Moody and Darken 1989, Haykin 1994) was parameterised with six variables
as input (MODIS bands), 500 neurons in the hidden layer and one variable as output (presence of
water where ‘0’ selected as dry and ‘1’ selected as water present). The ANN performed a
regression. The output of each node in one layer is connected to the inputs of all the nodes in the
next layer. The strengths of the connections are represented by continuously variable, signed
multipliers known as ‘weights’. The values of these weights are adjusted when training the ANN.
The ANN was built in two phases: the training phase and the testing phase. Training data was
manually selected from known wet and dry pixels. Five thousand pixels were selected from
wetlands that contained water by visual inspection of three randomly selected averaged yearly
images (see Section 2.2), which was the maximum number possible in the time available for the
study. Ancillary hydrology information was also used if it was readily available. Ten thousand
pixels of dry land (not within wetlands or streams) were randomly selected. The ‘wet’ points were
used in a back-propagation method, and a gradient descent algorithm was applied to a sum-ofsquares error function to automatically adjust the weights and thresholds of the links in the
network to minimise error. This process is equivalent to fitting the model represented by the
network to the training data available. The error of a particular configuration of the network can be
determined by running all the training cases through the network and comparing the actual output
generated with the desired or target outputs.
The ANN considered a series of labelled samples (0 = no water, 1 = water) and generated an
output value from each set of input values After each such presentation its internal weights were
adjusted until the ANN’s behaviour was stable and its output matched the true values maximally
without overfitting the model (Gardner and Dorling 1998). The ANN thus ‘learned’ to recognise
the correct output from the input feature-values. The weights are adjusted during this process.
In the testing phase the weights remained fixed at the values established in the training phase, and
the ANN computed output values for a series of samples from an independent data set. We then
were able to model the (unknown) function that related the input variables to the output variables,
and to make predictions when the output is not known (www.statsoft.com/textbook/neuralnetworks). From the ANN output, values less than or equal to 0.5 were considered to correspond to
no water, and those more than 0.5 to water (Capacci and Conway 2005).
ANNs make no assumptions regarding the underlying frequency distribution of the data being
classified. This attribute is particularly important at large scales because hydrology typically has a
multimodal frequency distribution and therefore violates assumptions required by traditional
supervised approaches such as maximum likelihood classification. An excellent example of this
property is illustrated by systems such as wetlands, which are sensitive to the amount and timing
of precipitation (Goward and Prince 1995). This sensitivity leads to distinct spectral–temporal
patterns (subclasses) within each hydrological class (Friedl et al. 2002).
A total of 6740 non-tidal wetlands were submitted to the ANN models to investigate whether
water could be detected between 2000 and 2011 using MODIS satellite imagery. A wetland was
considered to have water present where one or more pixel within the wetland boundary (including
the buffer) was estimated to be wet by the model (≥ 50% probability that the reflectance signature
was water).
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
2.4 Hydrological classification used for this study
Four categories are used to classify the water regime of wetlands in the new Victorian wetland
classification framework: one permanent category and three non-permanent (seasonal, intermittent
and episodic) (Table 2). The frequency of inundation descriptions formed the basis of the queries
used to characterise water regime (Table 3).
The number of seasons and years in which the wetlands were indicated by MODIS as having water
present provided information on whether they were permanently, seasonally, intermittently or
episodically inundated, based on the criteria described in Table 2.
Table 2 Water regime categories with their descriptions for each water regime
component to be used in the classification framework and 2012 wetland inventory (DSE
in prep.).
Category
Frequency of inundation
Duration of inundation
Permanent
Constant, annual or less frequently
but before wetland dries
Almost never dries (holds water >8 years in every
10), but levels may fluctuate within or between
years
Seasonal
Annual or near annual inundation
1–8 months, then dries
(8-10 years in every 10)
Intermittent
Fills 3–7 years in every 10
>1 month to more than 1 year, then dries
Episodic
Fills less than 3 years in every 10
>1 month to more than 1 year, then dries
Table 3 Queries used to categorise the water regime for this study.
Category
Criteria
Permanent
Wetlands that held water in every season for at least 80 % of the years for which data
was acquired
Seasonal
Wetlands that held water for less than four seasons per year but for more than 80% of
the years for which data was acquired
Intermittent
Wetlands that held water for 30 % to 70 % of the years for which data was acquired
Episodic
Wetlands that held water for only 10 % to 30 % of the years for which data was acquired
2.5 Reasons for the failure to detect water
The failure of MODIS to detect water was likely to be caused by one or more of the following:
 the wetland being legitimately dry
 the wetland being covered by vegetation (emergent or canopy)
 the presence of islands in the wetland that were large relative to the size of the wetland
 the limitations of the training data (see section 4.1).
Training data limitations led to large, deep, permanent water bodies being assessed merely as
wetted areas in the training data. The Native Vegetation Extent layer, NV2005_EXTENT (DSE
2009) was used to identify wetlands that were surrounded or covered by woody vegetation canopy.
The Wetlands 1994 Inventory was used to identify shallow marshes and meadows which were
likely to have emergent vegetation cover (Table 5).
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
2.6 Validation and confidence
Field data collected for 485 wetlands between October 2010 and April 2011 for the state-wide
Index of Wetland Condition assessment (DSE 2012) were used to test the validity and confidence
of the method. Seventy-one wetlands larger than 6 ha were found to contain some water during
these assessments and were used to validate the MODIS predictions of water presence. A
confidence rating for the MODIS water regime, and reasons for the failure to detect water by
MODIS (sources of error) were determined using the following rules:
Assignment of confidence to regime:
 If the IWC assessment of the inundation phase matched the detection of water by MODIS,
confidence in the MODIS prediction was considered to be high.
 If the IWC assessment did not match the detection of water by MODIS, confidence in the
MODIS prediction was considered to be low.
Assignment of confidence to reasons for water not being detected:
 If MODIS did not detect water and native vegetation extent spatial data indicated native tree
cover, confidence that MODIS could not detect water because of the presence of canopy cover
was considered to be high because the native vegetation spatial data has been rigorously
checked and validated.
 Confidence that MODIS could not detect water because of the presence of emergent vegetation
at wetlands associated with the Corrick categories of freshwater meadow or shallow freshwater
marsh was considered to be low because the presence of emergent vegetation in wetlands of
these categories was not validated.
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
3 Results
3.1 Water regime categories
MODIS detected water at 1204 of the 6740 wetlands greater than six hectares on at least one of the
44 sampling periods. Of those for which water was detected, 473 were classified as episodic, 376
as seasonal, 264 as intermittent and 91 as permanent. A source of error leading to MODIS failing
to detect water when it may have been present was allocated to 99% of wetlands in the Wetland
1994 inventory (Table 4). Wetlands larger than 6 ha for which the models showed that water was
present at some stage during assessment period were assumed to be legitimately dry when the
model showed no water was detected.
Table 4 Reason for failure to detect water (where dry conditions would have been
predicted even if a wetland was inundated) and number of wetlands where this was the
likely source of error.
Reason for failure
Number of wetlands
Emergent vegetation
3168
Canopy cover
2168
Island within wetland
37
Intertidal wetland*
4
Unknown/dry
159
Total
5536
*Most intertidal wetlands were removed from the Wetland 1994 dataset
submitted for MODIS analysis, however a small fraction were not
detected during the initial filtering of this wetland type.
45
All w etlands (n = 16775)
40
Wetlands submitted (n = 6740)
Wetlands w ater detected (n = 1204)
Percentage of wetlands (%)
35
30
25
20
15
10
5
0
Open w ater
Deep
freshw ater
marsh
Freshw ater
meadow
Shallow
freshw ater
marsh
Permanent
saline
Semipermanent
saline
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
Figure 2 Proportion of wetlands in each Corrick category in the wetland inventory,
proportion of wetlands submitted to the ANN and proportion of wetlands where MODIS
detected water.
The distribution of wetlands among Corrick wetland categories (see Table 1 on page 3) where
MODIS detected water were compared with the distribution of all wetlands in the Wetland 1994
inventory and wetlands submitted to the MODIS analysis (Figure 2). Water was detected in a
smaller proportion of freshwater meadow and shallow freshwater marsh categories, when
compared to all other categories.
3.2 Validation: model discrimination and reliability
We quantified the ability to correctly classify training locations (‘discrimination’ or ‘accuracy’) as
well as the agreement between the predicted probabilities of water in new sites not used in training
and the actual occurrence of water (‘reliability’). The confusion matrix, a standard output of ANN
modelling, indicated that the ANN correctly classified dryland and inundated areas provided as
training data more than 99% of the time. This is a very high level of discrimination.
Of the 485 wetlands assessed for the IWC between October 2010 and April 2011, 71 were selected
to use in the validation because they had valid wetland identification numbers, were
georeferenced, and were assessed during the period for which we had MODIS data for wetlands.
Of these 71 wetlands, 24% were assessed by the IWC as full, 46% with water present and
receding, 17% with water present and filling and 13% with no water.
The ANN correctly assessed the presence or absence of water at 32% of these wetlands. Time
constraints precluded the collation and of use ancillary data to characterise hydrology where
MODIS was unable to detect water.
4 Discussion
The use of multi-temporal MODIS images allowed simple, rapid classifications of water regime to
be developed on the basis of seasonal changes in water regime for wetlands larger than 6 ha with
no vegetation cover. The technique is conceptually simple, and the classes can be related directly
to empirical observations of the presence of water. By using multiple images that captured
averaged seasons, information on seasonal variability could be obtained. This is very valuable for
Victorian wetlands, many of which fill intermittently.
However, most of Victoria’s wetlands (about 60%) were too small for the minimum resolution of
the MODIS imagery, and about 32% could contain emergent vegetation or canopy cover that is
likely to obscure the MODIS signal. One of the major sources of error in the correct detection of
wetlands that contained water was the limitation of the training data. Due to time constraints and
the provisional nature of the data-mining software available at the time of the study, only yearly
average images could be used. Also, the model was trained to distinguish between terrestrial zones
and open water rather than wetting and drying within wetlands. These constraints meant the
reliability of estimates was low. Despite these constraints, water regime category was attributed to
1204 of the 16775 wetlands in the Wetland 1994 inventor. It is envisaged this result will be greatly
improved, with recent improvements to the data mining software that enable us to select training
data within wetlands over time.
The high number of wetlands for which water was not detected may have been also been caused
by many shallow wetlands being dry throughout the modelled period. The remotely sensed data
used in this study were collected in a period characterised by drought, and wetlands may have been
assigned to categories that reflect these dry conditions. For example, wetlands classified as
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
permanent (e.g. within the Corrick system) during decades with average rainfall may be classified
as seasonal in this study, and wetlands that are normally seasonal may have filled episodically.
This highlights the need to revisit this work regularly (as the data sequence lengthens) to develop a
better understanding of wetland hydrology during periods of average and above-average rainfall as
well as during drought conditions.
An approach that should increase the reliability of estimating wetland hydrology is outlined below.
In the current project static yearly average base maps were used, and samples taken inside
inundated wetlands were labelled ‘wet’ and samples taken in dry land were labelled ‘dry’. Since
the completion of the study, improvements in the data-mining software have enabled the temporal
training data selected to estimate the wet and dry cycle within a wetland and achieve a dynamic
training dataset where signatures of inundated and dry wetlands could be more accurately
captured. Other methods such as subpixel analyses and spatial generalisation can also be used to
include wetlands that were not captured in this project. This approach is outlined in the following
steps.
Step 1
Refine ANN models to detect water in a greater proportion of genuine wetlands. Limit ANN
training data involving ‘wet’ pixels to genuine wetlands by using base maps from recent flood
events in which inundation of all wetlands is certain.
Because training data for dry sites in this study were obtained from outside wetlands, limit training
data involving ‘dry’ pixels to wetlands which are known to be dry during dry periods. Training
data may also be derived from other remotely sensed base maps where appropriate, to crossvalidate training before presenting data to the ANN.
Step 2
Investigate whether a subpixel analysis of MODIS data can detect water in unvegetated wetlands
smaller than 6 ha.
Step 3
Use spatial generalisation techniques to determine whether the frequency and duration of filling of
neighbouring wetlands (that have been attributed with a water regime) is similar. Synchronous
inundation between neighbouring wetlands is likely to be related to the distance between them (i.e.
distance from known inundated wetland) and whether they are similar in size, wetland type
(Corrick), landscape context, water source or climate.
If these factors are able to give accurate estimations of synchronous inundation, we can apply the
relationships to predict inundation for other wetlands. An extrapolation of this relationship could
be used to predict the inundation of other wetlands.
Step 4
If no relationship between these factors and synchronous wetland inundation can be detected, one
or more of the following additional steps need to be taken:
 Explore the use of variables other than those considered in Step 3.
 Limit the model to a particular region (e.g. a bioregion or climate zone).
 Use hydrological surface water (rainfall–runoff) and groundwater models to predict the water
regime.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
 Use other higher spatial resolution remotely sensed data (e.g. data from Landsat, Rapid Eye,
Spot) in conjunction with MODIS to improve the training data.
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Appendix: Features of MODIS data relevant to wetlands
Remote sensing uses measurements of the electromagnetic radiation, usually sunlight or solar
radiation reflected in various bands, to characterise the landscape, infer surface properties, or in
some cases actually estimate hydrologic state variables. Sensors measure reflected solar radiation
(visible and short wave infrared) which can be related to land-cover, extent of surface
imperviousness and albedo (Johnston and Barson 1993).
MODIS terrestrial delivers reflectance data in 7 bands
 Band 1 (red) 630-690 nm (250-metre)
 Band 2 (NIR) 780-900 nm (250-metre)
 Band 3 (blue) 450-520 nm (500-metre)
 Band 4 (green) 530-610 nm (500-metre)
 Band 5 (NIR) 1230 - 1250 nm (500-metre)
 Band 6 (Mid IR) 1550-1750 nm (500-metre)
 Band 7 (Mid IR) 2090-2350 nm (500-metre)
A summary of the bands and their relevance to wetlands is provided by (Johnston and Barson
1993):
 Visible blue — provides information on water depth and turbidity. Blue light penetrates clear
water to depths of 50 m (Drury 1990 in (Johnston and Barson 1993), so reflectance is a
function of water depth, substrate brightness and turbidity. This allows reflectance in the blue
band to be used for mapping water depth in clear, shallow water (Jupp et al. 1985 in (Johnston
and Barson 1993) or for assessing suspended-solid loads in turbid waters (Blake and
Bainbridge 1990 in (Johnston and Barson 1993).
 Visible red — provides information on soil colour (Wright and Birnie 1986 In (Johnston and
Barson 1993) and calculation of a vegetation index (see below).
 Near infrared (NIR) — provides information on plant condition as it is strongly reflected by
photosynthetically active plants (Tucker and Sellers 1986 in (Johnston and Barson 1993).
 Medium infrared (MIR) — strongly absorbed by water, so that MIR response is very low from
inundated areas or areas of high soil moisture.
 Visible red — provides information on soil colour (Wright and Birnie 1986 in (Johnston and
Barson 1993) and comprises the vegetation index (see below).
There is little penetration of water by NIR and the NIR: visible red ratio (vegetation index VI),
which represents dominantly emergent and floating vegetation in waterbodies. Submerged
vegetation may have some influence on the visible blue reflectance, depending on the depth and
clarity of the water (Johnston and Barson 1993).
Arthur Rylah Institute for Environmental Research Technical Report Series No. 228
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Assessing the hydrology of Victorian wetlands using remotely sensed imagery: a pilot study
ISSN 1835-3827 (print)
ISSN 1835-3835 (online)
ISBN 978-1-74287-471-5 (print)
ISBN 978-1-74287-472-2 (online)
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