used as exclusions; or can be quantified, and <strong>the</strong> values incorporated asa co-variate in a numerical analysis.The falling limb may be useful in defining areas <strong>of</strong> greater flood durationbut <strong>the</strong>ir value <strong>for</strong> defining flood extent is likely to be compromised: by<strong>the</strong> tail <strong>of</strong> a flood, vegetation will have had time to respond, obscuring<strong>the</strong> inundation area. The problem <strong>of</strong> vegetation overhang or growingthrough water means <strong>the</strong> rising limb is preferred <strong>for</strong> inundationmapping, by satellite imagery.Table A1 – 3. Measured and predicted inundated areaComparison <strong>of</strong> measured and predicted inundation area <strong>for</strong> eight dates <strong>for</strong> <strong>the</strong> Great Cumbung Swamp. Observedinundated area was based on satellite imagery. Predictions were made using an empirically-derived multiple linearregression based on previous inundation area and changes to wetland storage volume, over <strong>the</strong> same time-frame.Changes in storage volume were based on measured in-flows from three gauging stations, adjusted <strong>for</strong> direct inputsand losses with a rainfall and evaporation factor (from Brady et al. 1998).M-area (ha) 3,560 6,320 4,240 13,100 4,400 2,800 3,790 4,160P-area (ha) 3,950 4,030 3,810 12,990 4,540 3,710 4,970 4,390% difference 10.8 –36.3 –10.1 –0.9 3.2 32.2 31.2 5.4In addition, if it is known that changes have occurred on <strong>the</strong> floodplainwhich might affect <strong>the</strong> passage or flood waters, such as vegetationclearance, erosion <strong>of</strong> channels, concrete structures, channel blockagesor clearing, <strong>the</strong>n it would be advisable to select images representingjust only <strong>the</strong> most recent situation.Commissioning new imagery. Current practice in terms <strong>of</strong>establishing a volume or flood inundation area relationship is to usehistorical data. The use <strong>of</strong> historical data can be restrictive, because <strong>of</strong><strong>the</strong> difficulty <strong>of</strong> standardisations, as described above, and because acomplete range may not be available. Commissioning a special set <strong>of</strong>images, using hyperspectral imagery or radar, has not beenimplemented yet in Australia. With falling costs, and greater need <strong>for</strong>precision, this is likely to become a real option in <strong>the</strong> future.An in-house cost–benefit analysis is suggested be<strong>for</strong>e making a decisionwhe<strong>the</strong>r to proceed with historical satellite or specially-commissionedimagery. This should consider not just technical issues such as dataquality, but additional benefits.InterpretationIn inundation mapping, <strong>the</strong> initial step is to interpret <strong>the</strong> images anddetermine areas that are water, and which are dryland. This can be doneby visual interpretation, or by computerised analysis. A visualrepresentation <strong>of</strong> this <strong>for</strong> a river–billabong–floodplain is shown inFigure A1 – 2.Visual interpretation. Visual interpretation is open to criticisms <strong>of</strong>subjectivity, observer bias, and problems <strong>of</strong> inter-observer consistency:<strong>the</strong>se can be important if a time series is being prepared, or if maps arebeing overlaid. Visual interpretation requires hard copy, ei<strong>the</strong>r <strong>of</strong> airphotos or satellite imagery, or a single-band (grey scale) satellite imageand <strong>the</strong> user visually discriminates and delineates inundated areas. Itrelies on <strong>the</strong> user to visually discriminate and delineate inundated areas.Despite <strong>the</strong> obvious subjectivity <strong>of</strong> this, some skilled practitioners find106 <strong>Estimating</strong> <strong>the</strong> <strong>Water</strong> <strong>Requirements</strong> <strong>for</strong> <strong>Plants</strong> <strong>of</strong> <strong>Floodplain</strong> <strong>Wetlands</strong>
visual interpretation <strong>of</strong> satellite images to be more effective than relyingon computerised analysis. The human eye can successfully integrate andinterpret complicated in<strong>for</strong>mation such as flow lines in shallow waterover submerged vegetation, and can account <strong>for</strong> and understandchanges in water quality across a flood front.The main difficulties are in defining boundaries when water is overhungwith vegetation, and in comparing RGB combinations with grey-scaleimagery.Computerised analysis. Computerised analytical techniques <strong>for</strong>processing satellite imagery are histogram slicing, band ratios orclassification.Histogram slicing uses single band grey-scale digital data images, andclusters pixels according to <strong>the</strong>ir brightness. Its advantages are only thatvery little data processing is required. Its disadvantages includesubjectivity in slicing images, difficulties in maintaining consistencybetween images, and potential to underestimate water area if obscuredby overhanging leaf canopy. Band ratios improve <strong>the</strong> detection <strong>of</strong> waterpixels.Classification refers to a range <strong>of</strong> numerical techniques, generally <strong>the</strong>seare based on two or more bands <strong>of</strong> digital data, ie. cannot be done on ahard copy. Classification, if properly done, requires field back-up orreference areas to ‘train’ <strong>the</strong> image which can also be limiting andrequires a degree <strong>of</strong> familiarity with remote sensing data or packages.Checking interpretationIn inundation mapping, where water boundaries can be hard to define(see above), a simple check on mapping can be implemented byoverlaying maps.Overlay checkThe assumption behind this procedure is that successively larger floodswill inundate <strong>the</strong> same area as a smaller flood plus some ‘new’ area.Overlays done with hard copy only (eg. on light table) give a qualitativeindication <strong>of</strong> error. Overlays done using a GIS indicate magnitude <strong>of</strong>error.Sources <strong>of</strong> discrepancy between successive flood maps are: errors inestimating hydrologic variables (storage volume, wetland inflow); errorsin estimating inundation area, such as ambiguous data, subjectiveinterpretation, poor quality imagery, incorrect rectification; naturalchanges to flood patterns, such as fallen trees, minor channel avulsions;anthropogenic changes on <strong>the</strong> floodplain, such as flowpath aggradationor degradation; flowpaths completely or partly obstructed, <strong>for</strong> exampleby structures such as levees or bridges, or simply fences or fallen trees;and vegetation clearance affecting roughness and water distribution.A process <strong>of</strong> elimination is needed to determine which <strong>of</strong> <strong>the</strong>se are <strong>the</strong>sources, in particular which are operator and technical errors, andwhich are site-specific factors.Overlaying can also be used to compare different methods (Table A1 –5). Agreement within 5% looks robust but larger discrepancies meritattention.Appendix 1: Remote Sensing 107
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Estimating the WaterRequirements fo
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ContentsPreface 7Acknowledgments 8G
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List of Tables1 Spatial variability
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Note that the guide is concerned pr
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ecomes a matter of how to use what
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Figure 1. Floodplain featuresThe fl
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Figure 4.Wanganella Swamps, souther
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Floodplain wetlands, being a mosaic
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Section 2:Introducing theVegetation
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size and vigour rarely reach their
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floodplains survive there because t
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The lagoon floor is then colonised
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Note 11Growth-formsField guides to
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identical conditions. PFTs differ f
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Note 13Changes in depthSome herbace
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Focusing on depthWater regime analy
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Note 15Internet dataEnvironmental d
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Step 3: Vegetation-hydrologyrelatio
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Note 19Modelling and time-stepsIn s
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Section 4: Old andNew DataOne of th
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see Figure 15), despite a three-fol
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frequency. This is rather limiting,
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Figure 13. Lippia, a floodplain wee
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single measure of the vegetation to
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Section 5:ObtainingVegetation DataW
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However, if the chosen species has
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Figure 15. Range of tree condition
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- Page 92 and 93: ReferencesPrefaceArthington AH and
- Page 94 and 95: Section 3Roberts J and Marston F (1
- Page 96 and 97: Kunin WE and Gaston KG (1993). The
- Page 98 and 99: Singh VP (1995).“Computer models
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