Using remote sensing to map wetland water clarity and permanence

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					Using remote sensing to map wetland water clarity and permanence: approaches
for identifying wetlands requiring management in large catchments
Leo Lymburner, Damien Burrows and Barry Butler

Australian Centre for Tropical Freshwater Research, James Cook University, Townsville, QLD. 4811. Web:,


Protecting wetlands is a major NRM activity across Australia, especially under NHT funding. Programs that
provide funding for on-ground works and encourage best management practices are intended to enable
improved management of wetlands. However, the reality is that of the many thousands of potential candidate
wetlands that would benefit from funding, only relatively few will be the subject of such investments, thus
requiring the dispersion of the limited funds to be more strategic. This is a very difficult task, especially
when current knowledge of the number, location, variety, ecological character and management needs of
wetlands are poorly known. In such situations, and especially in large, remote catchments where little of the
required information exists, an archive of satellite imagery is the only feasible means of obtaining the
required information in a timely and cost effective manner. Using a combination of satellite imagery, local
wetland knowledge, and scientific knowledge of ecological processes, we have developed a process to aid in
gathering of strategic wetland knowledge for large remote catchments. Developing such an inventory also
contributes to increased land manager knowledge and understanding of regional aquatic resources and
wetland management needs, and can feed into other common NRM programs. This paper deals with issues
of how to cheaply and rapidly capture substantial wetland inventory information across large remote
catchments, and proposes a scheme, based on the principles of wetland permanency, rarity and uniqueness,
to prioritise their values and investment priority. Results of ongoing trials of the scheme in the Burdekin and
Gulf of Carpentaria catchments of north Queensland are presented.


Wetlands, ecological character, remote sensing, inundation frequency, turbidity


Wetlands in the dry tropics region of Queensland provide a series of important ecological and recreational
functions. Protecting and maintaining these functions requires targeted management action that is appropriate
to the needs of specific waterbodies. However there are thousands of wetlands within Queensland and
relatively limited resources available to support on-ground works. Consequently there is a need to prioritize
the implementation of on-ground works, and such prioritization should be based on (amongst other things)
permanence and water clarity dynamics. The remote sensing/GIS approach is used because retrospectively
gathering information about inundation extent and water clarity dynamics across large (>100 000 km2)
catchments for numerous waterbodies is not possible any other way.

The GIS database provides a framework for integrating field data with detailed spatial and multi-temporal
data collected using remote sensing. The following two examples illustrate how remote sensing data can be
used to gather information about the temporal dynamics of wetlands. These temporal dynamics, combined
with additional information in the database will be used to guide the allocation of devolved grant funding and
on-ground works to best protect the environmental, aesthetic and economical values of complex wetland
systems. Two of the most important features controlling the ecology of dry tropics waterholes are their
flow/permanence regime and their water clarity regime. Both of these are not only major drivers of aquatic
ecology in this region, but they are visually apparent to observers and are often used by on-ground managers
in making their day-to-day land management decisions. Fortunately, both of these ecological properties can
be characterised from remotely-sensed imagery (Finch et al., 1997). The strong seasonality experienced in
the dry tropics means that there are very large differences in surface water availability and water clarity over
the course of a year. Thus waterbodies need to be characterised by their regime rather than by temporally-
static measures. Remotely-sensed imagery can be used to gain this temporal view over large geographic

    Wilson, A.L., Dehaan, R.L., Watts, R.J., Page, K.J., Bowmer, K.H., & Curtis, A. (2007). Proceedings of the 5th Australian Stream   235
       Management Conference. Australian rivers: making a difference. Charles Sturt University, Thurgoona, New South Wales.
areas and considerable time-scales (e.g. up to 34 years since LANDSAT was first launched)(Wass et al.,

Water clarity dynamics
Understanding the dry season turbidity dynamics of isolated water bodies is important because the optical
water quality that occurs towards the end of the dry season can have significant impacts on the aquatic
community that survive in these refugia until the next wet season (Erskine et al., 2005). The temporal
dynamics of water clarity are an important feature of wetlands as they determine the euphotic depth and
thereby determine the amount of primary production and the structure of the food web. It is not possible to
fully characterize the optical water quality of wetlands using archival Landsat TM because of the relatively
small size, variable depth and variable substrates (Brando pers. comm. 2007). However it is possible to use
archival Landsat TM to identify various optical water quality scenarios. In this paper, the following four
optical water quality scenarios for the mid-late dry season (June-November) are presented: the waterbody is
relatively clear in all available scenes; the wetland is highly turbid in all available scenes; the wetland is
usually clear but occasionally turbid; or the wetland usually has high turbidity but occasionally goes clear.
These scenarios are potentially useful from a management perspective, because they allow the identification
of wetlands that are switching between two states and may therefore be more responsive to management
intervention as opposed to wetlands that are in a steady state and may be less sensitive to changes in

This information also provides a valuable starting point for dialogue with landholders about water bodies on
their properties and any information/observations they may have about how those waterbodies a. respond to
existing management practices b. behaved historically (of particular interest if the landholder has a long
association with the water body(ies) in question), and c. whether there was anything exceptional about the
year(s) in which a water body went turbid.

Inundation dynamics of shallow ephemeral wetlands
Shallow ephemeral wetlands are a feature of semi-arid and dry tropics floodplains. They typically form
immediately following a rainfall or flood event, and then dry out in the following months as the dry season
progresses. In some instances these ephemeral wetlands are connected to smaller permanent water bodies, or
they may be large areas of impeded drainage that dry out completely every year and may remain dry for
many years before the next rainfall event. Large ephemeral wetlands provide valuable habitat for many local
and migratory birds (Jansen & Robertson, 2001). However collecting information on these wetlands has
been hampered by difficult access and their episodic nature. To characterise these large ephemeral wetlands
requires large scale, high-return-interval data. The Moderate Resolution Imaging Spectroradiometer
(MODIS) sensor provides such data, and can be used to provide insight into the inundation and subsequent
growth of large macrophyte dominated wetlands. These data can be analysed to provide information on how
frequently the wetland is inundated and how long it remains inundated for. When placed in a spatial context
this provides valuable information about the local importance and uniqueness of each wetland. Identifying
and characterising these wetlands is of particular importance because of a. their unique habitat values and b.
their distinct management needs.


Water clarity dynamics
The approach described in this study is designed for application to archival Landsat data. To make the
approach applicable across large areas at low cost necessitates the use of a relatively simple and robust
approach. The approach requires the use of a large number of Landsat TM scenes to characterize the water
clarity of the waterholes throughout each scene. Landsat TM is relatively cheap to acquire for large areas
and its archive extends back to 1986 with earlier versions of Landsat data available back to 1972. With a
pixel resolution of 30 metres, smaller waterbodies are not able to be included, but for the limited cost and
extensive temporal and geographic coverage, it provides useful management data for a significant proportion
of waterbodies within a catchment. To demonstrate this method dry season scenes from seven different
years between 1989 and 2001 were analysed. These scenes cover the main channel of the Mitchell River and
four off-channel lagoons (from 800 metres to 2.5 kms long) at the intersection of the Mitchell and the Palmer
Rivers in northern Queensland to determine the consistency of dry season water clarity between years.
When applying this technique across large catchments, to atmospherically correct such a large number of

scenes would be prohibitive, so an analysis technique is used that can be applied to the uncorrected digital
numbers (raw data) and will allow characterization of the water clarity dynamics using the ratio of Band
3/Band 2 (high values of this ratio indicate higher suspended sediment, leading to lower clarity). The
approach used in this paper is similar to that described in Nellis et al. (1998) but has been simplified to
enable calculation from raw digital numbers rather than atmospherically corrected scenes.

Inundation dynamics
Data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite can provide new
insight into the dynamics of large wetlands and wetland complexes. The MODIS satellite generates a large
number of products, the one used in this study is the 16 day composited Normalised Difference Vegetation
Index (NDVI) product (MOD13Q1)(Zhang et al., 2003). The data has 250 metre pixels which means that it
can only be analysed for larger wetlands, however its main advantage lies in its temporal coverage. The data
is collected daily and then composited every 16 days to get rid of clouds, smoke and atmospheric effects,
meaning that cloud free data is typically available for most areas throughout most of the year. The
MOD13Q1 data archive extends back to May 2000 and a time series of NDVI for each 250x250 metre pixel
is available Australia-wide at no cost. For vegetated areas NDVI ranges between 0.4-1 (3000 to 10000) and
increases with increasing leaf area or ‘greenness’. Areas of bare soil range between 0.2-0.4 (2000 to 4000),
and in the timeseries shown below values below 2000 indicate that the pixel contains predominantly water.
An example of the application of this product is shown from a wetland complex in the Burdekin catchment
associated with a basalt spring province known as the Great Basalt Wall, approximately 100 km SW of
Townsville. The 16-day composite MODIS imagery from May 2000 to the end of 2005 for the area was
acquired for the analysis.


Water clarity dynamics
In Figure 1, the Mitchell River main channel has greater clarity than the off channel lagoons. Windermere
Lagoon has a low clarity in all years but one (2000), possibly indicating that under a different management
regime it may clarify more often. Reedy Lagoon on the other hand has a higher degree of clarity in all
scenes except 1995 when the ratio rises above 1, indicating increased suspended sediment. It is interesting to
note that the Purumu Lagoon, which is adjacent to Reedy Lagoon, and is located on the same flow path, has
an entirely different water clarity regime. It has persistently high suspended sediment (ratio at or near 1.5)
for all scenes. Finally, Kingfish Lagoon, which is on the other side of the river, has a relatively greater
clarity for all scenes. These results demonstrate that this approach can be used to separate water bodies
based on their water clarity regimes. Field verification is required to validate the ecological significance of
different ratio values. Once this validation has been achieved eCognition ™ can be used to define inter-
annual water clarity dynamics for all large (> 2500 m2) waterbodies in the Mitchell catchment. These results
provide an example of how the method can be used to identify the range of water clarity regimes that may be
encountered in order to guide site selection for field validation programs.


       Ratio of Band 3/Band 2

                                  1                                    Mitchell River
                                                                       Windermere Lgn
                                0.75                                   Kingfish Lgn
                                                                       Purumu Lgn
                                 0.5                                   Reedy Lgn


                                  1989 1991 1993 1995 1997 1999 2001                        Figure 2. Water bodies at the intersection
                                                                                            of the Mitchell and Palmer rivers.

 Figure 1. Water clarity dynamics in 5 water bodies (1.5 = low
 clarity, 0 = high clarity).

                               2000    2001        2002     2003            2004        2005


  NDVI (x10,000)

                    4000                                                                               Lake Eumara 2
                                                                                                       Lake Eumara 1
                    2000                                                                               Burdekin Dam



                           1   10 19 28 37 46 55 64 73 82 91 100 109 118 127
                                              Time (16 day intervals)

Figure 3. MODIS timeseries for the locations shown in Figure 4, the Burdekin Dam signature is included
as an example of an area that remains permanently inundated.

Inundation frequency
The MODIS NDVI time series for three wetland areas is shown in Figure 3. The top two time series
represent different areas of Lake Eumara. Locations for these time series are indicaterd by the arrows in
Figure 4. The Burdekin Dam (located on the Burdekin River ~100km downstream of Lake Eumara but not
shown in Figure 4) time series has been included as an example of a permanently inundated area (NDVI
value always below 2000). The two Lake Eumara time series show cycles of macrophyte growth and
senescence that are punctuated by periods of inundation. Taking the year 2003 as an example the wetland
was inundated to the point where location 2 in Figure 4 was inundated, but location 1 was not. Note that the
time series of location 2 drops below the value 2000, whereas the timeseries for location 1 does not. After
this period of inundation both sites experience a growth flush (rapidly rising NDVI values) and then senesce
off as the dry season progresses.






                                      Lake Eumara

Figure 4. Wetland complex in the Burdekin                                          Figure 5. Wet season inundation frequency of
catchment (January 2003).                                                          different wetlands (number indicates the number of
                                                                                   wet seasons the wetland was inundated out of
                                                                                   possible total of 6).

It is interesting to note that the inundated location experienced a stronger growth flush (higher NDVI values)
than the non inundated site. Neither site is inundated in 2004, but both experience a wet-season associated
growth flush. The black diamond and smooth time series shown in Figure 3 are associated with the Lake
Eumara wetland, notice that the smooth series that comes from a drier part of the wetland and only drops
below an NDVI value of 2000 in early 2005 (hence a value of 1 in Figure 5) whereas the black diamond time
series drops below an NDVI value of 2000 in late 2000, early 2003 and early 2005 (hence a value of 3 in
Figure 5).

Figure 4 shows the wetland complex at the eastern end of the Great Basalt Wall. The wetlands shown
include the Burdekin river (diagonal feature in the top right), Spring Lake, (bottom right) and Lake Eumara
(complex system moving from top left through to middle right. These three represent riverine, lacustrine and
palustrine wetlands respectively. The results shown in Figure 5 were calculated by using eCognition ™ to
generate polygons that describe the extent of the wetlands shown in the Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) data. These polygons were then used as a basis for analysing
the MODIS data. End of wet season imagery was used to identify which areas were inundated at the end of
every wet season. It is interesting to note that both the Burdekin river (riverine) and Spring lake (lacustrine)
are inundated during every wet season for the period of observation, however Lake Eumara and its
associated wetlands shows a more complex mosaic of inundation frequencies.


The example of water clarity dynamics provided here shows how much useful information and
understanding of the water clarity regime can be obtained from a desktop study using the approaches
outlined. The results of the water clarity dynamics analysis also raise some interesting questions. For
example why do Purumu lagoon and Reedy lagoon have such markedly and consistently different water
clarity regimes? They are closely located on the same flow path and on the same floodplain of the Mitchell
River, so it is likely that they will receive similar input water quality during flood events. However during
this series of dry season images, one has persistently high suspended sediment, whereas the other has a
greater degree of clarity. There are a number of possible causes, including water depth differences (and
associated wind resuspension), cattle access or differences in ground water flushing. It is not possible to
distinguish between the different causes using remote sensing, however knowing that these water bodies do
have such different water quality trajectories enables targeted fieldwork and on-ground surveys to try and
identify the cause(s). Being able to identify these factors from a rapid desktop study greatly enhances the
efficiency of field work.

The results presented in Figure 5 provide valuable insight into the maximum extent and frequency of wet
season inundation. Both of these attributes are particularly important for migratory birds, particularly when
the wetlands are placed in a broader regional context. Both attributes contribute to the uniqueness of a
wetland, and the spatial context can further highlight specific wetlands of being of particular importance.
The results presented in Figure 5 represent one of a series of inundation frequency and duration products that
can be generated from analysis of MODIS data. There is also potential to map the growth and senescence
phases of emergent macrophytes in large shallow wetlands using MODIS data too. These data are not
presented here in the interests of brevity, but they are being included in our overall wetland characterisation
scheme. There are other, more cloud-resistant methods of mapping wetland inundation, including LIDAR,
microwave and radar. Whilst the analysis of MODIS data has its limitations, its lower cost and ease of
calculation make it attractive by comparison. The main limitation of the MODIS approach is the size of
wetlands to which it can be applied and that some portions of northern Australia experience more than 16
consecutive cloudy days, leading to a null value for the NDVI in the areas where this occurs. This limits
detection of the true maximum extent of inundation, because of the clouds typically associated with flood
events. However, for an ephemeral wetland to be effective bird habitat the wetland needs to persist for a
period of time, increasing the likelihood of its detection using the MODIS NDVI threshold approach. The
approaches presented here are being used by the Australian Centre for Tropical Freshwater Research to assist
in characterisation and classification of diverse and complex wetland systems across the dry tropics, to
generate hypothesis for field verification and increase the broader knowledge base and understanding of
tropical wetlands. This approach has been developed specifically to provide regional NRM groups with a

cost-effective means of prioritizing wetland protection/restoration projects. In future eCognition ™ will also
be used to describe the extent of riparian vegetation that surrounds each water body, and will also be used to
place the water body into a hydrological context (floodplain, in-channel, non-fluvial).


The information about water clarity and inundation dynamics generated by this approach provides new
insight into the ecological character of large wetlands distributed throughout semi-arid catchments. This
information can be coupled with field observations through a GIS data base to develop an ecological
character description for each wetland. The overall description of ecological character can be used to
identify and prioritize management actions.


The authors would like to acknowledge Vittorio Brando from CSIRO Land and Water for his suggestions
and insight into remote sensing of optical water quality. The authors also wish to thank Mike Stewardson for
reviewing the document, and Alan Marks, Damian Barret and Guy Byrne from the Environmental Remote
Sensing Group at CSIRO for providing the MODIS and ASTER data that this analysis was performed on.


Erskine, W. D., Saynor, M. J., Erskine, L., Evans, K. G., & Moliere, D. M. (2005). A preliminary typology
  of Australian tropical rivers and implications for fish community ecology. Marine and Freshwater
  Research 56: 253-267.
Finch, J. W. (1997.) Monitoring small dams in semi-arid regions using remote sensing and GIS. Journal of
  Hydrology 195: 335-351.
Jansen, A., & Robertson, A. I. (2001). Riparian bird communities in relation to land management practices in
  floodplain woodlands of south-eastern Australia. Biological Conservation 100: 173-185.
Wass, P. D., Marks, S. D., Finch, J. W., Leeks, G. J. L., & Ingram, J. K. (1997). Monitoring and preliminary
  the Humber catchment. The Science of the Total Environment 194-195: 263-283.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., Reed, B. C., & Huete, A.
  (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84: 471-475.
