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MAPPING FLOODPLAIN VEGETATION TYPES
ACROSS THE MURRAY-DARLING BASIN
USING REMOTE SENSING
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Shaun Cunningham, Matt White, Peter Griffioen, Graeme Newell and Ralph Mac Nally
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A Milestone Report to the Murray-Darling Basin Authority as part of Contract MD2245.
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Shaun C. Cunningham* and Ralph Mac Nally
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School of Biological Sciences, Monash University, VIC 3800
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Matt White and Graeme Newell
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Arthur Rylah Institute,
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Victorian Department of Environment and Primary Industries, Heidelberg, VIC 3084
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Peter Griffioen
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Ecoinformatics Pty. Ltd., Heidelberg, VIC 3084
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Corresponding author: Tel.: +61 3 9902 0142: Fax: +61 3 9905 5613
E-mail address: shaun.cunningham@monash.edu
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This report should be cited as: Cunningham SC, White M, Griffioen P, Newell G and Mac Nally R,
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(2013) Mapping Floodplain Vegetation Types across the Murray-Darling Basin Using Remote Sensing.
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Murray-Darling Basin Authority, Canberra.
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Cover image: The overall map of floodplain vegetation types across the Murray-Darling Basin
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showing the extent of the floodplain In grey and the various vegetation types in different colours.
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Published by the Murray‒Darling Basin Authority
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Postal Address: GPO Box 1801, Canberra ACT 2601
Telephone: (02) 6279 0100 international + 61 2 6279 0100
Facsimile: (02) 6248 8053 international + 61 2 6248 8053
Email: engagement@mdba.gov.au
Internet: http://www.mdba.gov.au
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All material and work produced by the Murray‒Darling Basin Authority constitutes Commonwealth copyright.
MDBA reserves the right to set out the terms and conditions for the use of such material.
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With the exception of the Commonwealth Coat of Arms, photographs, the Murray‒Darling Basin Authority
logo or other logos and emblems, any material protected by a trade mark, any content provided by third
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© Commonwealth of Australia (Murray‒Darling Basin Authority) 2013.
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The Murray‒Darling Basin Authority’s preference is that you attribute this publication (and any Murray‒
Darling Basin Authority material sourced from it) using the following wording within your work:
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Title: MAPPING FLOODPLAIN VEGETATION TYPES ACROSS THE MURRAY-DARLING BASIN
USING REMOTE SENSING
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Source: Licensed from the Murray‒Darling Basin Authority under a Creative Commons Attribution 3.0
Australia Licence
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As far as practicable, material for which the copyright is owned by a third party will be clearly labelled. The
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reproduced in this publication with the full consent of the copyright owners.
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Inquiries regarding the licence and any use of this publication are welcome by contacting the Murray‒Darling
Basin Authority.
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Cover image: The overall map of floodplain vegetation types across the Murray-Darling Basin showing the
extent of the floodplain In grey and the various vegetation types in different colours.
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Disclaimer
To the extent permitted by law, the Murray‒Darling Basin Authority and the Commonwealth excludes all
liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses
and any other compensation, arising directly or indirectly from using this report (in part or in whole) and any
information or material contained within it.
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Accessibility
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Mapping Floodplain Vegetation Types of the Basin 2
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accessibility difficulties or the information you require is in a format that you cannot access, please contact
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us.Executive Summary
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Here, we report on an effort to use remote sensing to predict characteristics of broad vegetation
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types of the floodplains of the Murray-Darling Basin. This is the first stage in a project that aims to a)
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map the extent of the floodplain vegetation types and b) map stand condition of the dominant
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forest and woodland types across the Murray-Darling Basin. These tools will inform the efficient
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allocation of environmental water to the floodplains as part of the Basin Plan.
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The target vegetation types included species-dominated forest, woodlands and shrublands, and the
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broader types of grasslands and wetlands. The distributions of the forests and woodlands will be
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used to inform the subsequent modelling of stand condition across the Basin. The distribution of
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these vegetation types was predicted using historical vegetation surveys and a range of satellite-
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derived reflectance, structural and elevation variables.
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A series of models were built successfully to predict the distribution of vegetation from a range of
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reflectance, structural and elevation variables derived from satellite data sets. We were able to
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accurately separate (96%) the distribution of native vegetation from non-native vegetation and
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water. Models of the distribution of the target species across the Basin were built, which provided
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useful variables (build R2 > 0.6) for the subsequent cover models. A multi-objective cover model was
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built using satellite-derived variables, including the above distribution models, that provided
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accurate predictions of cover for the majority of target and non-target vegetation types. The
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distribution of the target floodplain vegetation types was determined from the extent of native
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vegetation and the predicted cover values from the multi-objective model using a classification tree.
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This modelling approach produced a map that:
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1. accurately predicted the distribution of forests and woodlands of river red gum (84%), black
box (77%) and coolabah (89%).
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2. rarely predicted river cooba woodlands or river oak forests.
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3. provided weak predictions (62%) of the distribution of lignum shrublands, which included
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areas of chenopod shrublands and sparse floodplain.
4. predicted graminoid wetlands on lower areas of the floodplain but also on higher parts of
the floodplain that are dominated by native grasslands
Mapping Floodplain Vegetation Types of the Basin 3
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Our map provides the first consistent classification and mapping of river red gum, black box and
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coolabah woodlands across the Murray-Darling Basin. It demonstrates that satellite-derived data
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can be used to predict floodplain vegetation types across vast areas. The inability to predict the
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rarer tree types and to consistently distinguish shrublands, grasslands and wetlands revealed
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limitations of the current modeling. These limitations suggest improvements for future modeling of
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these floodplains that include:
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1. Compiling a more comprehensive quadrat data set.
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2. Targeted surveys of species and areas of interest.
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3. Increasing the number of structural types modeled.
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4. Increasing the number of both floodplain and non-floodplain vegetation types modeled.
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5. Including remote sensing data sets that better inform the prediction of structural types and
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flooding regimes (e.g. LiDAR).
Mapping Floodplain Vegetation Types of the Basin 4
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Table of Contents
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Executive Summary
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Introduction
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Methods
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Target floodplain vegetation types
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Study area
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Satellite-derived data sets
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Ground survey data sets
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Modelling
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Native vegetation extent model
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Target taxa distribution models
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Multi-objective cover model
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Mapping of target floodplain vegetation types
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Results
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Modelling
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Map accuracy
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Map characteristics
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Discussion
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Conclusion
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Acknowledgements
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References
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Mapping Floodplain Vegetation Types of the Basin 5
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Introduction
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The condition of floodplain vegetation across many areas of the Murray-Darling Basin has been
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decreasing over the past three decades. Substantial dieback of river red gum and black woodlands
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has occurred across the Murray River floodplain (Margules & Partners, 1990; Cunningham et al,
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2009a). These decreases in vegetation condition have been associated with dramatic reductions in
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the frequency of flooding. Over the last two decades, the Murray River floodplain has experienced
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two extended periods of below average rainfall (1991-1995, 2001-2009) with record low inflows (Cai
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& Cowan 2008). During the Millennium drought (1997-2009), floodplain ecosystems throughout the
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Murray-Darling Basin were pushed close to collapse.
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In response to the decreasing condition of many floodplains across the Basin, State Governments
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and the Murray-Darling Basin Authority has carried out a program of environmental watering of
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ecologically significant floodplains. This includes The Living Murray initiative and Water for Rivers
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program. The goal of environmental watering is to protect and restore the resilience of the Basin's
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ecosystems including rivers, lakes, wetlands and floodplains, including the plants and animals that
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depend on them. The Basin Plan under The Water Act 2007 increases the allocation of water to the
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environment from the 823 GL yr-1 recovered by 2009 to a total of 2,750 GL yr-1. This dramatically
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increases the amount of water availability and should lead to substantial improvements in the
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condition of the vegetation on many floodplains of the Basin.
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The efficient use of the environmental water resource will require an accurate method for assessing
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vegetation condition and its response to flooding across the Basin. A consistent vegetation map of
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the floodplains of the Murray-Darling Basin is an essential tool for allocating environmental water
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and assessing the response of vegetation to this water. Currently, there is a range of vegetation
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maps produced by different government departments that cover sections of the Basin. Each State
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Government uses distinct classification systems making it difficult to produce a consistent map of
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floodplain vegetation across the Basin. The spatial and taxonomic resolution of these vegetation
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maps differs widely across the Basin. In particular, there is limited vegetation mapping of the
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floodplains of the Darling, Warrego and Paroo Rivers. Therefore, to effectively execute the Basin
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Plan a consistent map of floodplain vegetation needs to be developed across the whole Basin.
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Mapping Floodplain Vegetation Types of the Basin 6
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Knowing where vegetation is on the floodplains of the Basin is essential information for deciding on
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environmental water allocations under the Basin Plan. To ensure this water is allocated efficiently,
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accurate measures of vegetation response are required. A method for measuring vegetation
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condition across the Basin is needed to assess a) which floodplains are in poor condition and require
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additional water, b) whether vegetation of these floodplains responds to the additional water and c)
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over the longer term how much water is enough to keep the vegetation of these floodplains in good
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condition.
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We have developed a method for accurately predicting stand condition of the forest and woodlands
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across the whole Murray River floodplain (Cunningham et al, 2009b). It involves a combination of
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ground surveys, remote sensing and modelling using machine learning. The maps produced using
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this approach showed that forest dieback was extensive (79% of the area) on the Murray River
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floodplain across the river system. This approach was able to detect improvements in stand
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condition across areas receiving environmental water during the recent extended drought.
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The Murray River floodplain includes areas of river red gum forests and woodlands, and black box
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woodlands. We created a continuous map of these vegetation types from existing vegetation maps
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produced by the three State governments across the Murray River floodplain. This map was used to
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build probability distribution models for these forest type based on Landsat reflectance variables.
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These distribution models proved to be useful in improving the predictions of stand condition across
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the floodplain.
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The current project aims to use a similar approach to predict the extent and condition of floodplain
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vegetation types across the Murray-Darling Basin. In particular, we aim to:
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1. Map the extent of the broad vegetation types of the floodplains of the Murray-Darling Basin.
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2. Map the stand condition of the dominant forest and woodland types across the Murray-
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Darling Basin.
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Here, we report on our effort to predict the distribution of broad vegetation types across the
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floodplains of the Basin. The target vegetation types included species-dominated forest, woodlands
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and shrublands, and the broader types of grasslands and wetlands. The distributions of the forests
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and woodlands will be used to inform the subsequent modelling of stand condition across the Basin.
Mapping Floodplain Vegetation Types of the Basin 7
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The distribution of these vegetation types was predicted using historical vegetation surveys and a
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range of satellite-derived reflectance, structural and elevation variables. The following four-staged
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approach was used to model the vegetation of the Murray-Darling Basin.
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1. Predict the distribution of native and non-native vegetation from satellite-derived variables.
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2. Predict the distribution of the target species from satellite-derived variables.
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3. Predict the cover of the target floodplain vegetation types and adjacent vegetation types
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from satellite-derived variables and predicted distributions from the previous modelling.
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4. Use a classification tree to determine the most probable vegetation type from the various
predicted values of cover.
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Methods
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Producing accurate maps of the distribution of native floodplain vegetation types involved a ten step
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process.
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1. Determine the extent of floodplains across the Basin (area of interest).
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2. Compile a data set of accurate locations of native species across the Basin.
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3. Determine attributes for all the native species on these floodplains.
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4. Create a set of satellite-derived variables to predict vegetation across the Basin.
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5. Predict the extent of native vegetation across the Basin.
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6. Build species distribution models for the target species.
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7. Build an ensemble model that predicted cover of the target floodplain vegetation types.
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8. Create cover maps for target and non-target vegetation types.
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9. Predict the extent of the native floodplain of the Murray-Darling Basin.
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10. Predict the extent of the floodplain vegetation types across the native floodplain.
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Target floodplain vegetation types
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The floodplains of the Murray-Darling Basin contain a diverse range of plant communities. Many of
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which would be difficult to distinguish using the reflected or emitted radiation detected by satellites.
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Here, we focused on predicting the distribution of forest, woodland and shrubland types dominated
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by single species, as well as a generic grassland and wetland type across the native floodplains of the
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Basin (Table 1).
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Table 1 Dominant taxa that defined the target floodplain vegetation types.
Floodplain vegetation type
Dominant taxa
River red gum forest and woodlands
Eucalyptus camaldulensis
Black box woodlands
Eucalyptus largiflorens
Coolabah woodlands
Eucalyptus coolabah
River Cooba woodlands
Acacia stenophylla
River Oak Forest
Casaurina cunninghamia
Lignum shrublands
Muehlenbeckia florulenta & Muehlenbeckia horrida
Grasslands
Poaceae
Wetlands
Species tolerant of > 6 months inundation
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Study area
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A spatial mask of the study area was created to include the floodplains of the Basin and
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neighbouring dryland vegetation. The buffer of dryland vegetation was included to ensure an
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adequate sample of dryland species, so that the modelling could distinguish native floodplain
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vegetation from other local types. The study area was first confined to the Murray-Darling Basin and
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then to areas below 500 m of elevation using the GEODATA 9 Second DEM Version 3 (Fenner School
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of Environment and Society & Geoscience Australia, 2008). A ‘height above river’ data set was
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created from the nine second DEM and the nine second DEM stream network (Stein, 2006) for
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Australia. The study area was determined as areas that are « 5 m above river across the low
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elevation (< 500 m) Basin.
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Satellite-derived data sets
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A range of satellite-derived variables with coverage across the floodplains of the Basin were used in
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the modeling. These included reflectance bands and derived indices from the Landsat satellite,
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synthetic aperture radar from the ALOS satellite and digital elevation models (DEMs) derived from
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ground surveys and airborne LiDAR.
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An historical Landsat composite was produced by Geosciences Australia, which included images over
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the period January 1st 2000 to December 31st 2010. This extended period of satellite imagery was
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required because of the dynamic nature of floodplain vegetation. For example, wetlands or the
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understorey of floodplain forests are quite visually distinct between wet and dry years. Therefore, it
Mapping Floodplain Vegetation Types of the Basin 9
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is not possible to create a floodplain vegetation map based on a single year of imagery that will
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predict well in subsequent years.
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The historical composite included median, quartile, minimum and maximum values for six spectral
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bands (Table 2) and five indices calculated from ratios of these bands (Table 3). Minima and maxima
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tend to be corrupted by errors in the data stream (random high and low values), outages (generally
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zero values or interpolated value from adjacent pixels) and clouds (generally high values). Median
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and quartile values are less susceptible to these errors, particular given the large number of images
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included. It was decided there was sufficient data in the median values alone and only these were
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used in the modelling.
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Native and non-native vegetation types of the floodplain are characterised by differences in their
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plant cover during different seasons of the year. For example, native grasslands, exotic dryland
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pastures and irrigated pastures can be distinguished by their growing seasons. Median values were
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calculated for four contrasting periods of the year for all bands and indices from the historical
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Landsat composite. These were named by the closest season: summer (December 1 to March 31),
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autumn (March 1 to June 30), winter (June 30 to September 30) and spring (September 1 to
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December 31).
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ALOS-PALSAR data was included because it detects microwaves in the L-band, which provides
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structural information beneath the canopy on the biomass of a forest. This information would be
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useful in distinguishing among different structural types (grassland, woodland and forests) and
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within them (river red gum versus black box woodland). The PALSAR 50 m Orthorectified Mosaic for
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Australia created by ALOS Kyoto and Carbon Initiative Project was used
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(http://www.eorc.jaxa.jp/ALOS/en/kc_mosaic/kc_50_australia.htm). This is a mosaic of images from
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June to September 2009 and included dual polarisations of HH and HV.
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The Shuttle Radar Topography Mission (SRTM)-derived 1 Second Digital Elevation Models Version
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1.0 (Gallant et al., 2011) was resampled at a 325 m cell size using bilinear interpolation. This DEM
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was used to help distinguish between lowland and montane forest types. The height above river
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data set, describe in the previous section, was used to distinguish the floodplain vegetation types
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from surrounding dryland vegetation.
Mapping Floodplain Vegetation Types of the Basin 10
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Table 2 Median seasonal band values derived from the Landsat composite (2000-2010) used in all
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the models.
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Variable
Explanation
Period
SummerB1
Reflectance in the blue spectrum (0.45-0.52 µm)
Dec 1 to Mar 31
SummerB2
Reflectance in the green spectrum (0.52-0.60 µm)
Dec 1 to Mar 31
SummerB3
Reflectance in the red spectrum (0.63-0.69 µm)
Dec 1 to Mar 31
SummerB4
Reflectance in the near infrared (0.76-0.90 µm)
Dec 1 to Mar 31
SummerB5
Reflectance in the middle infrared (1.55-1.75 µm)
Dec 1 to Mar 31
SummerB7
Reflectance in the far infrared (2.08-2.35 µm)
Dec 1 to Mar 31
AutumnB1
Reflectance in the blue spectrum (0.45-0.52 µm)
Mar 1 to Jun 30
AutumnB2
Reflectance in the green spectrum (0.52-0.60 µm)
Mar 1 to Jun 30
AutumnB3
Reflectance in the red spectrum (0.63-0.69 µm)
Mar 1 to Jun 30
AutumnB4
Reflectance in the near infrared (0.76-0.90 µm)
Mar 1 to Jun 30
AutumnB5
Reflectance in the middle infrared (1.55-1.75 µm)
Mar 1 to Jun 30
AutumnB7
Reflectance in the far infrared (2.08-2.35 µm)
Mar 1 to Jun 30
WinterB1
Reflectance in the blue spectrum (0.45-0.52 µm)
Jun 30 - Sept 30
WinterB2
Reflectance in the green spectrum (0.52-0.60 µm)
Jun 30 - Sept 30
WinterB3
Reflectance in the red spectrum (0.63-0.69 µm)
Jun 30 - Sept 30
WinterB4
Reflectance in the near infrared (0.76-0.90 µm)
Jun 30 - Sept 30
WinterB5
Reflectance in the middle infrared (1.55-1.75 µm)
Jun 30 - Sept 30
WinterB7
Reflectance in the far infrared (2.08-2.35 µm)
Jun 30 - Sept 30
SpringB1
Reflectance in the blue spectrum (0.45-0.52 µm)
Sept 1 to Dec 31
SpringB2
Reflectance in the green spectrum (0.52-0.60 µm)
Sept 1 to Dec 31
SpringB3
Reflectance in the red spectrum (0.63-0.69 µm)
Sept 1 to Dec 31
SpringB4
Reflectance in the near infrared (0.76-0.90 µm)
Sept 1 to Dec 31
SpringB5
Reflectance in the middle infrared (1.55-1.75 µm)
Sept 1 to Dec 31
SpringB7
Reflectance in the far infrared (2.08-2.35 µm)
Sept 1 to Dec 31
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Mapping Floodplain Vegetation Types of the Basin 11
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Table 3 Median seasonal indices derived from the Landsat composite used in all models.
Variable
Explanation
Period
SummerNDVI
Normalised Difference Vegetation Index
Dec 1 to Mar 31
= (B4 – B3) / (B3 + B4)
SummerEVI
Enhanced Vegetation Index
Dec 1 to Mar 31
= (B4 – B3) / (B4 + 6*B3 – 7.5*B1 + 1)
SummerSATVI
Soil Adjusted Total Vegetation Index
Dec 1 to Mar 31
= [ [ (B5-B3) / (B5-B3+0.5) ] * 1.5] - (B7/2)
SummerSLAVI
Specific Leaf Area Vegetation Index
Dec 1 to Mar 31
= B4 / (B3 + B5)
SummerNDMI Normalised Difference Moisture Index
Dec 1 to Mar 31
= (B4 – B5) / (B4 + B5)
SummerNDSI
Normalised Difference Soil Index
Dec 1 to Mar 31
= (B3 – B5) / (B3 + B5)
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AutumnNDVI
Normalised Difference Vegetation Index
Mar 1 to Jun 30
AutumnEVI
Enhanced Vegetation Index
Mar 1 to Jun 30
AutumnSATVI
Soil Adjusted Total Vegetation Index
Mar 1 to Jun 30
AutumnSLAVI
Specific Leaf Area Vegetation Index
Mar 1 to Jun 30
AutumnNDMI
Normalised Difference Moisture Index
Mar 1 to Jun 30
AutumnNDSI
Normalised Difference Soil Index
Mar 1 to Jun 30
WinterNDVI
Normalised Difference Vegetation Index
Jun 30 - Sept 30
WinterEVI
Enhanced Vegetation Index
Jun 30 - Sept 30
WinterSATVI
Soil Adjusted Total Vegetation Index
Jun 30 - Sept 30
WinterSLAVI
Specific Leaf Area Vegetation Index
Jun 30 - Sept 30
WinterNDMI
Normalised Difference Moisture Index
Jun 30 - Sept 30
WinterNDSI
Normalised Difference Soil Index
Jun 30 - Sept 30
SpringEVI
Enhanced Vegetation Index
Sept 1 to Dec 31
SpringSATVI
Soil Adjusted Total Vegetation Index
Sept 1 to Dec 31
SpringSLAVI
Specific Leaf Area Vegetation Index
Sept 1 to Dec 31
SpringNDMI
Normalised Difference Moisture Index
Sept 1 to Dec 31
SpringNDSI
Normalised Difference Soil Index
Sept 1 to Dec 31
SpringEVI
Enhanced Vegetation Index
Sept 1 to Dec 31
Mapping Floodplain Vegetation Types of the Basin 12
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Ground survey data
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To successfully map native floodplain vegetation types across the Murray-Darling Basin, we needed a
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spatially accurate data set of native floodplain and dryland species across the study area. Vegetation
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survey data sets covering the Basin were obtained by the MDBA from the four State Governments
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across the Basin (Table 4). These data sets included quadrats records (presence and cover
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abundance) and species records (presence only). The study area mask was used to determine which
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species records would be used in the modelling. Records with low spatial accuracy (apparently > 150
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m) were excluded from any analyses.
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The important data for determining the extent of floodplain vegetation types are quadrat records,
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which provide a measure of abundance and, therefore, dominance of individual species. The data
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set assemble included 11,752 quadrat records, with 65% of these from New South Wales (Table 5).
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This is consistent with the majority of the Murray-Darling Basin being in New South Wales. A
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substantial portion of the Basin is within Queensland but only 208 quadrats on floodplains were
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obtained. The distribution of quadrats across the floodplains of the Basin shows limited sampling on
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the floodplains of the Warrego, Paroo and Nebine Rivers, and on the Interesecting Rivers away from
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the Darling River (Figure 1).
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An understanding of the attributes of the component species within a quadrat would improve the
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prediction of the floodplain types. In particular, knowing the inundation tolerance of the species
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present at a site was a potential discriminator among types (e.g. wetlands, lignum, black box
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woodland and river red gum woodland). For each species, a total of 14 attributes were determined
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from the literature (Table 6), ranging from life form to inundation tolerance. Many of the species
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found across the Basin were already attributed in the DSE database but ca 800 new species had to
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be attributed.
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Mapping Floodplain Vegetation Types of the Basin 13
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Table 4 Vegetation survey data sets used in the modelling.
State
Data sets used
Custodian
Queensland
Qld Herbarium CORVEG Primary (cover), Queensland Herbarium
Secondary and Tertiary (presence only).
New South Wales
NSW Vegetation Information System
Office of Environment and Heritage
Victoria
Victorian Biodiversity Atlas
Dept. Environment and Primary
Industries
South Australia
NatureMaps
Dept. Environment, Water and
Natural Resources
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Figure 1 Distribution of quadrat data for floodplain vegetation across the Murray Darling Basin.
Darker areas indicate places where a large number of quadrats were obtained.
Mapping Floodplain Vegetation Types of the Basin 14
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Table 5 Number of quadrats obtained for the Murray-Darling Basin Water Resource Plan Areas.
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Water resource plan area
Number of quadrats
Australian Capital Territory
76
Victorian Murray
899
Northern Victoria
928
Wimmera-Mallee
1232
South Australian Murray Region
310
South Australian River Murray
403
Eastern Mount Lofty Ranges
55
NSW Murray and Lower Darling
1604
Murrumbidgee
2356
Lachlan
448
Macquarie-Castlereagh
1395
Barwon-Darling Watercourse
58
Intersecting Streams
591
Namoi
473
Gwydir
414
NSW Border Rivers
302
Queensland Border Rivers
34
Moonie
21
Condamine-Balonne
113
Warrego-Paroo-Nebine
40
Total
11752
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Mapping Floodplain Vegetation Types of the Basin 15
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Table 6 Species attributes determined for all the native species included in the modelling.
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Species Attribute
Definition
Aquat
Aquatic species
WL_0
Does not experience water logging
WL_<1
Tolerates less than a month of water logging
WL_1-6
Tolerates one to six months of water logging
WL_>6
Tolerates more than six months of water logging
Inun_0
Does not experience inundation
Inun_<1
Tolerates less than a month of inundation
Inun_1-6
Tolerates one to six months of inundation
Inun_>6
Tolerates more than six months of inundation
Inun_perm
Tolerates permanent inundation
Woody
Trees and shrubs
Graminoid
Grasses (Poaceae), sedges (Cyperaceae) and rushes Juncaceae)
Chenopod
Chenopodioideae
Samphire
Tecticornia species
336
337
Mapping Floodplain Vegetation Types of the Basin 16
338
Modelling
339
Random forests were used to build all the models because these select relevant environmental
340
variables and can model interactions among variables. Random forests overcome the inherent
341
inaccuracies in seeking a single parsimonious model by constructing an ensemble of models.
342
Bootstrap aggregating (or bagging), which is similar to model averaging, is used to improve the
343
accuracy of predictions. Models of multiple bootstrap samples were fitted (individual trees) to
344
create an ensemble tree (forest) that predicted the variable of interest. Individual trees relate
345
values of a response (leaves) to its predictors through a series of binary decisions or branches
346
(Friedman, 2001). We used a particular type of regression tree known as predictive clustering trees
347
(Kocev et al., 2007) in the program Clus. While most decision tree learners induce classification of
348
regression trees, predictive clustering trees generalizes this approach by learning trees that are
349
interpreted as cluster hierarchies.
350
351
Random forests are well suited to modelling large sets of independent variables, many of which may
352
be highly correlated. This modelling technique creates a forest of regression trees. At each branch
353
of the tree, the algorithm randomly selects a small number of independent variables from all of
354
those available and creates the node on the basis of which variable minimises the model error.
355
While over-fitting is often seen as a problem in statistical modelling, predictions of regression trees
356
for independent data sets are not compromised by using a large number of variables and are
357
generally superior to other methods (e.g. GLM, GAM and MARS, Elith et al., 2006). In contrast, the
358
neural network models used in our previous study (Cunningham et al., 2009a) must consider all
359
independent variables supplied simultaneously. Without an order of magnitude more exemplars
360
than predictor variables, neural networks may produce overly complex models, which are less robust
361
and validate poorly compared with models produced using a carefully selected subset of
362
independent variables.
363
364
Native vegetation extent model
365
A model was built to determine the extent of native vegetation within the study area. This was used
366
to a) define the native floodplain, b) determine the native species within the floodplain and c) select
367
a spatially accurate data set of native vegetation records. The data set Land Use of Australia,
368
Version 4, 2005/2006 (ABARES, 2010) was used to classify the land uses across the Basin. This map
369
was reclassified into the following land-use categories: native grassland, native forest, urban,
Mapping Floodplain Vegetation Types of the Basin 17
370
irrigated cropping, dryland cropping, pasture and water (Table 7). These land-use categories were
371
then grouped into three land-use types: native vegetation, non-native vegetation and water.
372
373
A total of 37793 exemplars were sampled across this floodplain area, with 19,695 samples within
374
native vegetation, 16,098 samples within non-native vegetation and 2000 samples within water.
375
Sampling was stratified across the floodplain within hexagonal regions (200 km x 200 km) to ensure
376
representative spatial coverage. Within each hexagon, samples were taken evenly across the
377
following land-use categories: native vegetation, dryland cropping, irrigated cropping, pasture,
378
urban and water.
379
380
A random forest was used to predict the target land-use types (native vegetation, non-native
381
vegetation and water) from 48 satellite-derived variables from the historical composite (Tables 2 &
382
3). The random forest was built from 50 bootstrapped trees using the program Clus. The data set
383
was divided into a training data set (89%) and test data set (11%) to independently validate the
384
performance of the model. The accuracy of the native vegetation extent model was tested using
385
confusion matrices of observed and predicted land-use types for exemplars used in the build (N =
386
63891) and the validation (N = 6841). A sensitivity analysis was done to determine which variables
387
were important for predicting the land-use types. For each variable, the number of trees, the
388
number of branches and the percentage of branches across the forest that used the variable were
389
calculated.
390
Mapping Floodplain Vegetation Types of the Basin 18
391
Table 7 Classification of the land-use types from the ‘Land Use of Australia’ layer into native
392
vegetation, non-native vegetation and water.
Land-use type
Land-use category
Native vegetation
LU_DESC2
Grazing natural vegetation
Managed resource protection
Marsh / wetland
Nature conservation
Production forestry
Other minimal use
Non-native vegetation
Dryland cropping
Cropping
Irrigated cropping
Intensive horticulture
Irrigated cropping
Irrigated modified pastures
Irrigated plantation forestry
Irrigated perennial horticulture
Irrigated seasonal horticulture
Perennial horticulture
Seasonal horticulture
Pasture
Grazing modified pastures
Urban
Residential
Manufacturing and industrial
Services
Water
Water
Estuary / coastal waters
Lake
River
Reservoir / dam
393
Mapping Floodplain Vegetation Types of the Basin 19
394
Target taxa distribution models
395
The ground survey data obtained included far more presence only than cover abundance records.
396
Cover abundance records were necessary to determine the dominance of a species within a quadrat
397
and, therefore, determining floodplain vegetation types. The spatial coverage of the quadrat data
398
was limited and in many areas we had no vegetation records. The lack of spatial coverage was not
399
necessarily a problem when modeling floodplain vegetation from satellite data, as the types were
400
likely to have similar reflectance characteristics across the study area. The main issue was the
401
limited number of quadrat records for the target species. We created species distribution models
402
for the six mono-dominant vegetation types (Table 8) to capitalize on the large amount of presence
403
data and to increase our understanding of their distribution across the Basin. Distribution models
404
were built for the broad vegetation types of woody, dryland and wetland species to help predict the
405
floodplain types and to differentiate wetlands and grasslands from the species-dominated types.
406
These taxa distribution models were then used as inputs to the final model that predicted the cover
407
of the target floodplain vegetation types.
408
409
Random forests were used to build the taxa distribution models. To improve the accuracy of
410
predictions, 150 870 random absences were selected randomly across the study area. A suite of
411
satellite-derived data sets were used to predicted species presence. The 48 reflectance variables
412
derived from the historical Landsat composite were included as potential predictors (Tables 2 and 3).
413
Synthetic aperture radar, elevation data and the three outputs from the Native Vegetation Model
414
were used as potential predictors (Table 9). Random forests had a total of 55 potential predictor
415
variables to predict taxa distributions. Random forests were built from 20 bootstrapped trees using
416
the program Clus. The data set was divided into a training data set (89%) and test data set (11%),
417
which was used to independently validate the performance of the model. The strength of the
418
forests were determined from a) the model fits (R2) and b) the proportion of presence observation
419
within different predicted probability classes. A sensitivity analysis based on forest statistics was
420
done to determine which variables were important for predicting the distribution of the taxa.
421
422
Mapping Floodplain Vegetation Types of the Basin 20
423
Multi-objective cover model
424
As the target floodplain vegetation types were defined by the dominance of particular taxa, an
425
appropriate approach was to model the cover of important taxa in the study area. A multi-objective
426
regression tree was used to predict the cover of a) the target vegetation types, b) other vegetation
427
types present in the study area and c) functional types that could be useful in distinguishing among
428
the types (Table 10).
429
430
A random forest was used to predict the various cover types from the 48 reflectance variables
431
derived from the historical Landsat composite (Tables 1 and 2) and 16 satellite-derived variables
432
used in or produced by the Native Vegetation Extent and the Taxa Distribution Models (Table 11).
433
The random forest was built from 50 bootstrapped trees using Clus. A total of 1176 samples (11%)
434
were left out of the build to independently validate the fit of the forest. The strength of the forests
435
was determined from the model fits (R2). The accuracy of predictions for the vegetation types from
436
the cover type model was tested using a confusion matrix of observed and predicted land-use types
437
for exemplars used in the build (N = 11752). A sensitivity analysis based on forest statistics was done
438
to determine which variables were important for predicting the distribution of the taxa.
Mapping Floodplain Vegetation Types of the Basin 21
439
Table 8 Details of the taxa distribution models created.
440
Model
Taxa included
Records
Woody
Trees and shrub species
75043
Wetland
Species tolerant of > 6 months inundation
48323
Dryland
Species intolerant of flooding
70823
River red gum
Eucalyptus camaldulensis
4562
Black box
Eucalyptus largiflorens
2197
Coolabah
Eucalyptus coolabah
748
River cooba
Acacia stenophylla
868
River oak
Casaurina cunninghamia, Casaurina cunninghamia subsp.
247
cunninghamia
Lignum
Muehlenbeckia florulenta, Muehlenbeckia horrida,
1918
Muehlenbeckia horrida subsp. horrida
441
442
443
444
445
Table 9 Satellite-derived variables used, in addition to those from the historical Landsat composite,
446
to predict taxa distributions across the study area.
447
Variable
Explanation
Pr(Water)
Probability of water body
Pr(Native)
Probability of native vegetation
Pr(Non-Native)
Probability of non-native vegetation and cleared areas
PALSAR LL_HH
Horizontal-horizontal polarisation of L-band
PALSAR LL_HV
Horizontal-vertical polarisation of L-band
DEM
Elevation above sea level (< 10 m accuracy)
HAR
Height above river (< 10 m accuracy)
448
Mapping Floodplain Vegetation Types of the Basin 22
449
Table 10 Cover types predicted by the multi-objective model and the taxa that defined them.
Cover type
Taxa used to calculate cover within quadrat
Total Plant Cover
All vascular plants
Red Gum Cover
Eucalyptus camaldulensis
Black Box Cover
Eucalyptus largiflorens
Coolabah Cover
Eucalyptus coolabah
Lignum Cover
Muehlenbeckia florulenta, Muehlenbeckia horrida, Muehlenbeckia
horrida subsp. horrida
River Cooba Cover
Acacia stenophylla
River Oak Cover
Casaurina cunninghamia, Casaurina cunninghamia subsp.
cunninghamia
Woody Cover
All trees and shrubs
Grass Cover
Cyperaceae, Juncaceae and Poaceae
Wetland Cover
Total cover of species tolerant of at least a month of inundation,
including aquatic species
Dryland Cover
Species that never experience inundation
Chenopod Cover
Chenopodioideae
Samphire Cover
Tecticoma species
Mallee Cover
Mallee tree
Wet 0 Cover
Species that do not experience water logging
Wet <1 Cover
Species that tolerate less than a month of water logging
Wet 1-6 Cover
Species that tolerate one to six months of water logging
Wet >6 Cover
Species that tolerates more than six months of water logging
Inun 0 Cover
Species that do not experience inundation
Inun <1 Cover
Species that tolerate less than a month of inundation
Inun 1-6 Cover
Species that tolerate one to six months of inundation
Inun >6 Cover
Species that tolerate more than six months of inundation
Inun perm Cover
Species that tolerate permanent inundation
450
Mapping Floodplain Vegetation Types of the Basin 23
451
Table 11 Satellite-derived variables used, in addition to those from the historical Landsat composite,
452
to predict cover types across the study area.
453
Variable
Explanation
Pr(Water)
Probability of water body
Pr(Native)
Probability of native vegetation
Pr(Non-Native)
Probability of non-native vegetation and cleared areas
PALSAR LL_HH
Horizontal-horizontal polarisation of L-band
PALSAR LL_HV
Horizontal-vertical polarisation of L-band
DEM
Elevation above sea level (< 10 m accuracy)
HAR
Height above river (< 10 m accuracy)
Distribution models
Pr(Woody)
Probability of woody species
Pr(Wetland)
Probability of wetland species
Pr(Dryland)
Probability of dryland species
Pr(RRG)
Probability of river red gum
Pr(BB)
Probability of black box
Pr(Cool)
Probability of coolabah
Pr(Cooba)
Probability of river cooba
Pr(Oak)
Probability of river oak
Pr(Lignum)
Probability of lignum
454
Mapping Floodplain Vegetation Types of the Basin 24
455
Mapping distribution of the target floodplain vegetation types
456
A map of the current distribution of the target floodplain types was created from models of a) the
457
distribution of native vegetation, b) the cover of floodplain vegetation and c) the cover of the target
458
floodplain vegetation types using the following classification tree (Figure 2). Initially, the logical
459
hierarchical structure for the tree was conceived. This process informed the list of regression models
460
that were created. For example, some of the species to be modelled such as river red gum and black
461
box are not confined to floodplains across most of their distribution in south-eastern Australia.
462
Consequently, we needed to model vegetation attributes relevant to inundation in addition to
463
species predominance. Threshold values for the various models at each decision node were
464
determined by sequentially adjusting the ‘test’ value until the boundaries of the daughter classes
465
substantially matched their geographic distribution at familiar locations across the study area. The
466
following sequence of decisions provided a satisfactory representation of the floodplain vegetation
467
types across the Basin. The use of a classification tree to produce the final map means the decision
468
structure and threshold values may be altered to potentially provide a more accurate map of the
469
target floodplain types. Conversely, a more complex tree could be constructed to elicit a locally
470
nuanced solution.
471
472
The distribution of the target floodplain types was determined using the following sequence of
473
decisions.
474
1. IF Pr(Non-native) > 50% THEN ‘Non-native’.
475
2. Determined by highest probability of ‘Water’ and ‘Native’.
476
3. Determined by highest proportional cover of ‘Dryland Cover’ and ‘Wetland Cover’.
477
4. Within ‘Floodplain’, IF (Total Plant Cover – Total Plant Cover SD) « 20% THEN ‘Sparse
478
479
480
481
482
483
484
485
486
floodplain’ ELSE ‘Lush floodplain’.
5. Within ‘Dryland’, IF (Total Plant Cover – Total Plant Cover SD) « 20% THEN ‘Sparse plain’ ELSE
‘Lush plain’.
6. Within ‘Lush floodplain’, IF (Woody Cover – Woody Cover SD) « 10% THEN ‘graminoid
wetland’ ELSE ‘woody wetland’.
7. Within ‘Lush plain’, IF (Woody Cover – Woody Cover SD) « 10% THEN ‘Grassland / herbland’
ELSE ‘Dry woodland’.
8. Determined by highest value of Chenopod Cover, Samphire Cover and (Woody Cover –
(Chenopod Cover + Samphire Cover).
Mapping Floodplain Vegetation Types of the Basin 25
487
488
489
9. IF Red gum cover > (Lignum Cover + Coolabah Cover + Black Box Cover + River Oak Cover +
River Cooba Cover) THEN ‘River red gum
10. Determined by maximum cover value among the remaining woody target types.
490
491
The accuracy of the Floodplain Vegetation Type map was tested using a confusion matrix of
492
observed and predicted types for the quadrat data (N = 11752). These quadrats were used to build
493
to the Cover Type models. Each quadrat was classified using the classification tree (Figure 2) based
494
on the actual recorded values of cover for the various vegetation types. The first two decisions are
495
based on the native vegetation extent model that was based on the ‘Land Use of Australia’ layer, so
496
these values had to be estimated from this layer for each quadrat.
497
498
The extent of the floodplains of the Murray-Darling Basin was determined as areas where Wetland
499
Cover was predicted to be a higher proportion of the Total Plant Cover than Dryland Cover. The area
500
was further refined by removing wetland cover that was not contiguous with the larger floodplain.
Mapping Floodplain Vegetation Types of the Basin 26
501
502
503
Figure 2 Classification tree used to classify the study area into the various vegetation types.
504
Decisions indicated by numbered diamonds are explained in the text. Pale green boxes indicate
505
types included on the map.
Mapping Floodplain Vegetation Types of the Basin 27
506
Results
507
508
Modelling
509
The native vegetation extent model produced very strong predictions (accuracy > 96.0%) for native
510
vegetation, non-native vegetation and water bodies (Tables 12 & 13). Important predictors for
511
distinguishing among these land-use types were reflectance in the blue, green and near infrared
512
spectra during winter, and reflectance in the blue and green spectra during spring (Table 14).
513
514
Strong distribution models (Build R > 0.6) were built for all the target taxa (Table 15). There was a
515
strong relationship between the number of records included in a distribution model and the strength
516
of the validation test (Figure 3). There was steep increase in the model strength up to 5000 records.
517
The broad species groups (woody, wetland and dryland) showed the strongest validation with the
518
separate test data sets (Test R > 0.6), which reflects the order of magnitude higher number of
519
records than the individual species models. The individual species models produced much weaker
520
validation tests (Test R < 0.5). River oak had the least samples (247 records) and consequently its
521
distribution model validated poorly (Test R = 0.1). All taxa distribution models, except the model for
522
river cooba, provided useful variables for the subsequent modelling of cover for the target
523
vegetation types. That is, the predicted probabilities were related strongly to the observed presence
524
of the taxa (Figures 4 & 5). Important predictors of taxa distribution were elevation, height-above-
525
river, reflectance in the blue spectrum, horizontal-horizontal polarisation of the L-band and
526
probability of native vegetation (Table 16).
527
528
The multi-objective model produced accurate predictions (Test R > 0.5) of cover for the majority of
529
the 23 cover types (Table 17). The cover models for river cooba, samphire and permanently-
530
inundated vegetation did not validated successfully with the test data sets (Test R < 0.5). Therefore,
531
we had useful cover models to predict the distribution of all the target floodplain types except for
532
River Cooba. The cover models, although not providing absolute estimates of cover (observed ≈
533
predicted), showed strong relationships between observed and predicted cover (Figures 6 & 7).
534
Important predictors of the cover types included the probability of the broad taxa groups woody,
535
wetland and dryland, and reflectance in blue and green spectra during winter (Table 18).
536
537
Mapping Floodplain Vegetation Types of the Basin 28
538
Map accuracy
539
The floodplain vegetation type map had an overall accuracy of 64.8% (7620 of 11752 quadrats were
540
correctly classified). Half of the predicted types had a higher accuracy: river red gum woodlands and
541
forests (83.7%), black box woodlands (77.2%), coolabah woodlands (89.1%), dry woodlands (75.0%),
542
grasslands/herblands (74.4%) and water (88.9%, Table 16). Lignum scrublands were not as well
543
resolved (62.2%) and were most often confused with black box woodlands (16.6%). River cooba
544
shrublands, river oak forests, chenopod shrublands, samphire shrublands, graminoid wetlands and
545
sparse plains were poorly resolved (< 33%). Graminoid wetlands were mainly confused (28.5%) with
546
grasslands/herblands.
547
548
Looking at the accuracy of the map on a state-by-state basis, river red gum woodlands and forests,
549
black box woodlands, coolabah woodlands, dry woodlands, grasslands/herblands and water were
550
accurately predicted (> 65%, Tables 20-23). The exception being grassland/herbland in Victoria, with
551
an accuracy of 51.0%. Predictions for lignum in South Australia were highly accurate (90.6%). In
552
Queensland, the predictions for black box woodland had no accuracy (0%) but this is a result of a
553
single quadrat being available in this state.
554
555
Map characteristics
556
The multi-objective model and subsequent classification tree provided a map of the floodplain
557
vegetation across the Murray-Darling Basin (Figure 8). Black box woodland was predicted accurately
558
as occurring across most of these floodplains. The map of river red gum forests and woodlands
559
showed a more extensive distribution in the southern Basin than the north. In contrast, coolabah
560
woodlands were restricted predominantly to the northern Basin. At a finer scale, there are scattered
561
pixels of coolabah woodland in the southern Basin into Victoria. This suggests the model was unable
562
to distinguish coolabah woodland from some areas of river red gum or black box woodland.
563
564
To explore the strengths and limitations of the predicted map we have presented maps of a number
565
of significant floodplains across the Basin (Figures 9-24). The map of Chowilla shows a floodplain
566
dominated by black box woodlands and lignum shrublands (Figure 9). River red gum is restricted to
567
the Murray River channel and scattered patches along the anabranch creeks. A large area of
568
samphire shrubland is predicted on Lake Limbra. The modelling did not predict many of the sparse
569
and poor condition river red gum woodlands along anabranchs such as Punkah Creek. Many of the
Mapping Floodplain Vegetation Types of the Basin 29
570
areas predicted to be lignum shrublands are dominated by chenopods or have sparse vegetation
571
cover.
572
573
Hattah Lakes is predicted to have river red gum along the Murray Channel, Chalka Creek and fringing
574
the various lakes (Figure 10). Black box woodland is predicted higher up on the floodplain while
575
lignum shrublands are predicted within most of the lakes. The distribution of river red gum and
576
black box is representative of this floodplain. They are generally not lignum shrublands on the lakes.
577
The distribution of river red gum and black box are predicted well for the Gunbower-Koondrook-
578
Perricoota Forests (Figure 11). However, the cover model was unable to distinguish between the
579
black box and grey box woodlands of southern Gunbower Forest. In surrounding areas outside the
580
forest, the cover model predicted areas of native grasslands to be graminoid wetlands.
581
582
The river red gum forests and woodlands of Barmah-Millewa Forest were predicted well by the
583
cover model (Figure 12). The grassy wetlands of Moira and Barmah Lake are clearly shown by the
584
map. The large cleared area in southern Gulpa Island was predicted by the model. The cleared
585
property in central Barmah and the grasslands surrounding the forest are misrepresented as grassy
586
wetlands. The maps shows similar predictions for the Lower Ovens River (Figure 13), with river red
587
gum forests predicted well while surrounding native grasslands are shown as grassy wetlands.
588
589
The map of Yanga National Park shows a floodplain dominated by river red gum forests and
590
woodlands with substantial areas of grassy wetlands (Figure 10). The model successfully predicted
591
the large areas of dry woodland. Areas of lignum shrubland were predicted on the eastern side of
592
the floodplain.
593
594
The Great Cumbungi Swamp is represented well with large areas of graminoid wetland (Figure 15).
595
Surrounding the wetlands are large areas of lignum, which are fringed by river red gum and further
596
out on the floodplain by black box woodland.
597
598
The map of Mennindee Lakes shows lakes fringed predominantly by black box woodlands with
599
scatter river red gum woodland (Figure 16). Along the Darling River, there is river red forest with
600
black box woodland found higher up on the floodplain. Predictions for the inside of the lakes
601
differed, with Lakes Cawndilla and Menindee having mix of lignum, chenopods and samphire
Mapping Floodplain Vegetation Types of the Basin 30
602
shrublands, Pamamaroo and Tadure Lakes filled with water, and Tandou Lake is shown with little
603
native vegetation, which reflects its agricultural use.
604
605
At Toorale National Park, river red gum was predicted predominantly along the main channel of the
606
Darling River (Figure 17). In contrast, coolabah woodlands were predicted along the Warrego River
607
channel, as well as on the higher ground of both rivers. Black box woodlands were predicted further
608
away from the river channel beyond the coolabah woodlands.
609
610
The map of Macquarie Marshes shows a floodplain containing large areas of river red gum forest,
611
graminoid wetlands, coolabah woodlands and black box woodlands (Figure 18). There is a large
612
central expanse of river red gum forest while large areas of graminoid wetlands are distinguished
613
from these forests. Beyond these areas coolabah and black box woodlands are predicted.
614
615
The floodplains of the Namoi River east of Wee Wea are predicted to have a distinct separation of
616
forest types (Figure 19). River red gum is found along the river channel and tributaries from the
617
south. Beyond the river red gum there are dry woodlands south of the river while large areas of
618
coolabah woodlands are predicted north of the river.
619
620
Gwydir Wetlands were mapped as having river red gum along the channel of the Gwydir River and
621
over an extensive area of the central wetland (Figure 20). Coolabah woodlands were predicted
622
surround these areas of river red gum. Many treeless areas were predicted as graminoid wetlands.
623
The Macintyre River east of Goondiwindi was predicted to have river red gum along the channel,
624
with coolabah woodlands further away from the river (Figure 21).
625
626
Large areas of lignum shrubland were predicted by the model in Narran Lakes (Figure 22). Coolabah
627
woodlands were mapped along creeks and the Narran River. Coolabah woodland was also predicted
628
to occur within Lake Narran and Clear Lake.
629
630
Yantabulla Swamp is predicted to have extensive areas of lignum and chenopod shrublands (Figure
631
23). The map shows a large area of coolabah woodland fanning out from Cuttaburra Creek as it
632
enters the swamp from the east. Moving westward across the swamp there is a loose transition
633
westward from coolabah woodland to lignum shrubland and then to chenopod shrubland.
Mapping Floodplain Vegetation Types of the Basin 31
634
635
Nocoleche Nature Reserve on the Paroo River is predicted to have coolabah woodlands close to the
636
river, defining water courses and fringing lakes (Figure 24). Black box woodlands are mapped
637
scattered beyond the coolabah channels.
638
639
640
641
Table 12 Confusion matrix of the vegetation classes predicted by native vegetation extent model by
642
the samples used to build the model (N = 63891).
643
Native vegetation
Non-native vegetation
Water
Accuracy
Native vegetation
Non-native vegetation
Water
38033
13
13
242
23046
7
2
0
2535
99.4%
99.9%
99.2%
644
645
646
647
Table 13 Confusion matrix of the vegetation classes predicted by native vegetation extent model by
648
the samples used to validate the model (N = 6841).
649
Native vegetation
Non-native vegetation
Water
Native vegetation
4181
114
10
Non-native vegetation
156
2390
0
Water
13
2
233
96.4%
99.4%
99.9%
Accuracy
650
651
Mapping Floodplain Vegetation Types of the Basin 32
652
Table 14 Sensitivity analysis for variables used in the native vegetation extent model. Sensitivity for
653
random forests was assessed by the proportion of branches in a forest that used a variable.
654
Variable
No. Trees
No. branches
% of branches
WinterB1
50
2612
2.91
WinterB4
50
2423
2.7
SpringB4
50
2395
2.67
SpringB1
50
2378
2.65
WinterB2
50
2361
2.63
WinterB7
50
2232
2.48
SummerB1
50
2174
2.42
WinterB3
50
2145
2.39
WinterB5
50
2096
2.33
SpringNDMI
50
2058
2.29
SpringEVI
50
2008
2.23
WinterEVI
50
1993
2.22
SummerB4
50
1957
2.18
AutumnB1
50
1939
2.16
SpringB2
50
1926
2.14
SpringSLAVI
50
1924
2.14
SpringB5
50
1904
2.12
SpringB3
50
1878
2.09
WinterNDMI
50
1853
2.06
AutumnEVI
50
1824
2.03
WinterNDSI
50
1788
1.99
SummerB2
50
1773
1.97
SpringB7
50
1762
1.96
SummerEVI
50
1759
1.96
655
656
Mapping Floodplain Vegetation Types of the Basin 33
657
Table 14(cont.) Sensitivity analysis for variables used in the native vegetation extent model.
658
Sensitivity for random forests was assessed by the proportion of branches in a forest that used a
659
variable.
660
Variable
No. Trees
No. branches
% of branches
PALSAR LL_HV
50
1750
1.95
SpringNDVI
50
1733
1.93
WinterNDVI
50
1728
1.92
AutumnSLAVI
50
1726
1.92
AutumnB4
50
1717
1.91
SummerB7
50
1704
1.9
AutumnB7
PALSAR LL_HH
50
1708
1.9
50
1699
1.89
AutumnNDMI
50
1687
1.88
SummerB5
50
1684
1.87
SummerB3
50
1670
1.86
AutumnB2
50
1613
1.79
WinterSATVI
50
1584
1.76
AutumnNDVI
50
1586
1.76
WinterSLAVI
50
1573
1.75
AutumnB5
50
1544
1.72
SummerNDVI
50
1518
1.69
SpringNDSI
50
1497
1.67
SpringSATVI
50
1483
1.65
AutumnB3
50
1467
1.63
SummerNDMI
50
1464
1.63
SummerSLAVI
50
1409
1.57
SummerNDSI
50
1358
1.51
AutumnNDSI
50
1312
1.46
AutumnSATVI
50
1271
1.41
SummerSATVI
50
1216
1.35
661
Mapping Floodplain Vegetation Types of the Basin 34
662
Table 15 Correlations (R) between observed and predicted values for the taxa distribution models.
663
Model
Build R
Test R
Woody
0.800
0.721
Wetland
0.723
0.604
Dryland
0.789
0.705
River red gum
0.704
0.476
Black box
0.614
0.413
Coolabah
0.723
0.354
River cooba
0.646
0.275
River oak
0.809
0.107
Lignum
0.631
0.338
664
665
Mapping Floodplain Vegetation Types of the Basin 35
(R2 = 0.91)
666
667
668
669
Figure 3 Relationship between the strength (Test R) of the taxa distribution models and the number
of records used to build them. A power function was fitted to illustrate the trend.
670
Mapping Floodplain Vegetation Types of the Basin 36
671
672
673
Figure 4 Relationship between the proportion of presences among observations and the probability
of presence predicted by the taxa distribution models and for the broad taxa groups.
674
Mapping Floodplain Vegetation Types of the Basin 37
675
676
677
Figure 5 Relationship between the probability of presence predicted by the taxa distribution models
and the proportion of records that were presences for the individual species.
Mapping Floodplain Vegetation Types of the Basin 38
678
Table 16 Sensitivity analysis for variables used in the taxa distribution model. Sensitivity for random
679
forests was assessed by the proportion of branches in a forest that used a variable.
680
Variable
No. Trees
No. branches
% of branches
DEM
20
5406
6.56
WinterB1
20
4870
5.91
HAR
20
4344
5.27
PALSAR LL_HH
20
3271
3.97
Pr(Native)
20
3244
3.94
PALSAR LL_HV
20
3168
3.84
SpringB1
20
2691
3.27
WinterB2
20
2574
3.12
WinterB4
20
2385
2.89
WinterB3
20
2245
2.72
SummerB1
20
2197
2.67
WinterB7
20
2086
2.53
WinterB5
20
2015
2.44
SpringB4
20
1924
2.33
AutumnB1
20
1807
2.19
SpringB2
20
1728
2.10
SpringB5
20
1729
2.10
SummerB4
20
1594
1.93
SpringB3
20
1574
1.91
SpringB7
20
1519
1.84
SummerB5
20
1426
1.73
AutumnB4
20
1390
1.69
SummerB7
20
1375
1.67
SummerB3
20
1318
1.60
AutumnB7
20
1319
1.60
SummerB2
20
1304
1.58
AutumnB5
20
1246
1.51
681
Mapping Floodplain Vegetation Types of the Basin 39
682
Table 16(cont.) Sensitivity analysis for variables used in taxa distribution model. Sensitivity for
683
random forests was assessed by the proportion branches in a forest that used a variable.
684
Variable
No. Trees
No. branches
% of branches
AutumnB3
20
1217
1.48
AutumnB2
20
1132
1.37
Pr(Water)
20
1122
1.36
WinterNDMI
20
1025
1.24
WinterSLAVI
20
986
1.20
WinterNDSI
20
940
1.14
WinterNDVI
20
921
1.12
SummerNDMI
20
860
1.04
SpringNDMI
20
809
0.98
WinterEVI
20
786
0.95
SpringEVI
20
765
0.93
SpringNDSI
20
752
0.91
AutumnNDMI
20
754
0.91
SpringNDVI
20
742
0.90
SpringSLAVI
20
722
0.88
SummerNDSI
20
674
0.82
AutumnNDSI
20
666
0.81
AutumnSLAVI
20
659
0.80
SummerSLAVI
20
654
0.79
AutumnNDVI
20
647
0.79
SummerEVI
20
604
0.73
AutumnEVI
20
551
0.67
SummerNDVI
20
548
0.66
Pr(Non-Native)
20
520
0.63
WinterSATVI
20
458
0.56
SpringSATVI
20
406
0.49
SummerSATVI
20
385
0.47
AutumnSATVI
20
363
0.44
Mapping Floodplain Vegetation Types of the Basin 40
685
Table 17 Correlations (R) between observed and predicted values for the cover types predicted by
686
the multi-objective model.
687
Cover type
Build R
Test R
Total Plant Cover
0.798
0.564
Red gum Cover
0.862
0.601
Black box Cover
0.867
0.498
Coolabah Cover
0.905
0.513
Lignum Cover
0.872
0.686
River Cooba Cover
0.868
0.243
River Oak Cover
0.897
0.653
Woody Cover
0.843
0.611
Grass Cover
0.826
0.597
Wetland Cover
0.894
0.729
Dryland Cover
0.904
0.721
Chenopod Cover
0.843
0.618
Samphire Cover
0.877
0.433
Mallee Cover
0.885
0.524
Wet 0 Cover
0.903
0.72
Wet <1 Cover
0.870
0.632
Wet 1-6 Cover
0.859
0.657
Wet >6 Cover
0.86
0.622
Inun 0 Cover
0.904
0.721
Inun <1 Cover
0.879
0.664
Inun 1-6 Cover
0.886
0.713
Inun >6 Cover
0.863
0.629
Inun perm Cover
0.851
0.249
688
689
Mapping Floodplain Vegetation Types of the Basin 41
690
691
692
Figure 6 Relationship between cover predicted by the cover type model and observed cover for the
target vegetation types.
Mapping Floodplain Vegetation Types of the Basin 42
693
694
695
Figure 7a-h Relationship between cover predicted by the cover type model and observed cover for
additional cover types.
Mapping Floodplain Vegetation Types of the Basin 43
696
697
698
Figure 7i-o Relationship between cover predicted by the cover type model and observed cover for
additional cover types.
Mapping Floodplain Vegetation Types of the Basin 44
699
Table 18 Sensitivity analysis for variables used in cover type model. Sensitivity for random forests
700
was assessed by the proportion of branches in a forest that used a variable.
701
Variable
No. Trees
No. branches
% of branches
Pr(Wetland)
50
2723
2.91
Pr(Woody)
50
2640
2.82
Pr(Dryland)
50
2590
2.77
WinterB1
50
2527
2.70
WinterB2
50
2342
2.50
WinterB4
50
2331
2.49
SpringB1
50
2231
2.38
WinterB3
50
2188
2.34
WinterB5
50
2172
2.32
SummerB1
50
2105
2.25
WinterB7
50
2099
2.24
Pr(Native)
50
1991
2.13
SpringB4
50
1972
2.11
SpringB2
50
1961
2.10
SpringB3
50
1951
2.09
SpringB5
50
1935
2.07
SpringB7
50
1907
2.04
Pr(RRG)
50
1901
2.03
Pr(Non-Native)
50
1878
2.01
SummerB4
50
1851
1.98
PALSAR LL_HH
50
1856
1.98
AutumnB1
50
1818
1.94
SummerB2
50
1805
1.93
PALSAR LL_HV
50
1720
1.84
SummerB3
50
1698
1.82
SummerB5
50
1682
1.80
Pr(Lignum)
50
1669
1.78
AutumnB4
50
1649
1.76
Pr(BB)
50
1627
1.74
SummerB7
50
1628
1.74
AutumnB2
50
1520
1.62
702
Mapping Floodplain Vegetation Types of the Basin 45
703
Table 18(cont.) Sensitivity analysis for variables used in cover type model. Sensitivity for random
704
forests was assessed by the proportion of branches in a forest that used a variable.
705
Variable
No. Trees
No. branches
% of branches
AutumnB3
50
1489
1.59
AutumnB5
50
1483
1.59
AutumnB7
50
1447
1.55
WinterNDMI
50
1388
1.48
WinterSLAVI
50
1337
1.43
WinterNDVI
50
1311
1.40
WinterEVI
50
1280
1.37
SpringNDMI
50
1163
1.24
WinterNDSI
50
1134
1.21
SpringNDVI
50
1106
1.18
SpringSLAVI
50
1108
1.18
SpringEVI
50
1050
1.12
SummerNDMI
50
1043
1.11
Pr(Cooba)
50
1031
1.10
SpringNDSI
50
1014
1.08
SummerSLAVI
50
1014
1.08
SummerNDVI
50
997
1.07
SummerEVI
50
973
1.04
SummerNDSI
50
976
1.04
AutumnNDMI
50
964
1.03
AutumnNDVI
50
959
1.03
WinterSATVI
50
953
1.02
AutumnSLAVI
50
940
1.00
AutumnNDSI
50
900
0.96
SpringSATVI
50
866
0.93
AutumnEVI
50
833
0.89
SummerSATVI
50
708
0.76
Pr(Cool)
50
703
0.75
AutumnSATVI
50
625
0.67
Pr(Water)
50
547
0.58
Pr(Oak)
50
235
0.25
Mapping Floodplain Vegetation Types of the Basin 46
706
707
Table 19 Confusion matrix for the floodplain vegetation types across the Murray-Darling Basin predicted by the classification tree (Figure 2) for the
quadrats (N= 11752) used to build the model.
Grassland/herbland
Graminoid wetland
Sparse plain
Water
119
283
8
20
1
151
44
9
9
Black box
83
312
8
31
51
375
65
105
18
82
28
14
Coolabah
10
8
180
21
13
77
8
31
1
21
20
6
River cooba
1
Samphire
16
Chenopod
Dry woodland
3
Lignum
River oak
27
River cooba
Black box
1049
Coolabah
River red gum
Observed class
Predicted class
River red gum
29
River oak
2
47
16
2
6
1
Dry woodland
36
31
6
4
154
3685
10
96
7
165
24
130
Lignum
23
15
2
18
51
50
244
25
29
25
19
2
Chenopod
1
4
3
2
37
111
25
360
8
47
34
6
1
Samphire
Grassland/herbland
42
7
101
26
297
32
468
1
Graminoid wetland
19
Sparse plain
water
Accuracy
708
1
2
1551
86
14
37
22
7
38
28
9
83.7%
72
77.2%
89.1%
24.0%
8.2%
75.0%
62.2%
32.5%
28.6%
74.4%
12.3%
12.0%
88.9%
Mapping Floodplain Vegetation Types of the Basin 47
709
710
Table 20 Confusion matrix for the floodplain vegetation types predicted by the classification tree (Figure 2) for the South Australian quadrats (N = 781)
used to build the model.
Chenopod
5
12
2
1
Black box
1
47
5
6
44
8
8
6
7
1
1
2
1
9
21
5
1
Coolabah
River cooba
4
Dry woodland
1
1
216
11
33
17
Lignum
7
125
3
Samphire
1
1
3
1
12
12
4
20
1
1
1
Chenopod
Grassland/herbland
6
2
2
3
1
55
water
Accuracy
Water
Lignum
2
Sparse plain
River oak
2
Graminoid wetland
River cooba
6
Grassland/herbland
Black box
Predicted class
River red gum
Samphire
River red gum
Dry woodland
Observed class
34
75.0%
83.9%
8.7%
0%
73.2%
90.6%
20.0%
40.0%
65.5%
0%
0%
97.1%
711
Mapping Floodplain Vegetation Types of the Basin 48
River cooba
Water
173
2
7
1
89
18
4
6
13
128
26
34
10
40
5
5
2
3
1
2
1
37
10
71
886
1
11
1
69
4
32
8
13
35
7
3
5
4
2
Chenopod
1
7
1
13
7
4
Samphire
1
Dry woodland
15
4
Lignum
9
3
Grassland/herbland
20
2
5
11
2
6
51
3
5
Graminoid wetland
Sparse plain
Water
Accuracy
714
Graminoid wetland
81
3
River oak
Grassland/herbland
Sparse plain
1
Samphire
6
Chenopod
86
Lignum
22
River cooba
15
Dry woodland
Coolabah
507
River oak
Predicted class
River red gum
Black box
Black box
Table 21 Confusion matrix for the floodplain vegetation types predicted by the classification tree (Figure 2) for the Victorian quadrats (N = 3034) used to
build the model.
Observed class
River red gum
712
713
13
1
244
15
10
13
13
5
8
21
4
87.7%
27
76.1%
27.3%
16.4%
68.9%
51.5%
16.7%
21.4%
51.0%
20.0%
29.2%
81.8%
Mapping Floodplain Vegetation Types of the Basin 49
715
716
Table 22 Confusion matrix for the floodplain vegetation types predicted by the classification tree (Figure 2) for the Australian Capital Territory and New
South Wales quadrats (N = 7731) used to build the model.
12
Black box
60
179
8
20
32
201
31
63
Coolabah
8
7
165
20
8
68
8
29
Water
4
Sparse plain
97
Graminoid wetland
33
Grassland/herbland
12
Samphire
3
Chenopod
Dry woodland
10
Lignum
River oak
524
River cooba
Black box
River red gum
Coolabah
River red gum
Observed class
56
25
5
2
35
22
8
16
20
6
4
1
Predicted class
River cooba
19
River oak
2
10
6
Dry woodland
21
27
5
3
80
2456
9
83
5
90
18
Lignum
14
5
2
5
10
20
82
9
5
8
11
Chenopod
1
4
3
2
36
101
24
342
43
34
6
Grassland/herbland
20
2
86
240
26
463
1239
68
30
4
22
9
2
Graminoid wetland
1
Sparse plain
Water
Accuracy
717
2
6
5
80.2%
85
7
0
76.5%
88.7%
23.5%
3.4%
76.9%
44.6%
34.1%
0%
82.4%
11
9.9%
4.8%
84.6%
Mapping Floodplain Vegetation Types of the Basin 50
Table 23 Confusion matrix for the floodplain vegetation types predicted by the classification tree (Figure 2) for the Queensland quadrats (N = 215) used to
build the model.
Predicted class
River red gum
12
2
Sparse plain
1
2
Coolabah
1
River cooba
1
1
15
1
3
6
1
3
Dry woodland
1
2
1
2
127
Lignum
3
1
13
3
1
2
1
2
4
Graminoid wetland
Accuracy
Graminoid wetland
Grassland/herbland
Chenopod
1
Black box
Grassland/herbland
Lignum
Dry woodland
River oak
River cooba
Coolabah
Black box
Observed class
River red gum
718
719
2
80.0%
0%
93.8%
50.0%
0%
90.7%
100.0%
0%
68.4%
28.6%
0%
Mapping Floodplain Vegetation Types of the Basin 51
720
721
722
723
724
Figure 8 Distribution of a) floodplain vegetation, b) black box woodlands, c) river red gum forests
725
and woodlands and d) coolabah woodlands across the Murray-Darling Basin predicted by the
726
combination of the multi-objective model and the classification tree (Figure 2).
727
Mapping Floodplain Vegetation Types of the Basin 52
river red gum
black box
Punkah Creek
lignum
grassland
chenopod
samphire
water
Murray River
2 km
728
729
Figure 9 Extent of floodplain vegetation types at the Chowilla Floodplain on the lower Murray River.
Mapping Floodplain Vegetation Types of the Basin 53
river red gum
black box
lignum
wetland
grassland
chenopod
samphire
Murray River
water
Lake Mournpall
5 km
Chalka Creek South
730
.
Figure 10 Extent of floodplain vegetation types at Hattah Lakes on the lower Murray River.
Mapping Floodplain Vegetation Types of the Basin 54
river red gum
black box
lignum
Thule Creek
wetland
grassland
chenopod
water
Murray River
Gunbower Creek
5 km
731
Figure 11 Extent of floodplain vegetation types at Gunbower-Koondrook-Perricoota Forests on the middle Murray
River.
Mapping Floodplain Vegetation Types of the Basin 55
Tuppal Creek
river red gum
black box
wetland
grassland
chenopod
water
Moira Lake
Murray River
Barmah Lake
5 km
732
Figure 12 Extent of floodplain vegetation types Barmah-Millewa Forests on the middle Murray
River.
Mapping Floodplain Vegetation Types of the Basin 56
Murray River
Lake Mulwala
river red gum
Ovens River
wetland
grassland
water
5 km
733
Figure 13 Extent of floodplain vegetation types on the Lower Ovens
River.
Mapping Floodplain Vegetation Types of the Basin 57
Macommon Lake
Piggery Lake
river red gum
black box
lignum
wetland
grassland
chenopod
water
Murrumbidgee River
5 km
Tala Lake
734
Figure 14 Extent of floodplain vegetation types at Yanga National Park on the Lower Murrumbidgee River.
Mapping Floodplain Vegetation Types of the Basin 58
Lachlan River
river red gum
black box
lignum
wetland
grassland
chenopod
water
735
Figure 15 Extent of floodplain vegetation types at the Great Cumbung Swamp on the Lachlan River.
Mapping Floodplain Vegetation Types of the Basin 59
Pamamaroo Lake Tadure Lake
Lake Menindee
river red gum
black box
lignum
Lake Cawndilla
grassland
chenopod
samphire
Lake Tandou
water
Darling River
10 km
736
Figure 16 Extent of floodplain vegetation types at Meenidee Lakes on the lower Darling River.
Mapping Floodplain Vegetation Types of the Basin 60
river red gum
Warrego River
black box
coolabah
lignum
wetland
grassland
chenopod
samphire
water
Ross Billabong
Darling River
2 km
737
Figure 17 Extent of floodplain vegetation types at Toorale National Park at the junction of the Darling and Warrego Rivers.
Mapping Floodplain Vegetation Types of the Basin 61
river red gum
black box
coolabah
lignum
wetland
grassland
chenopod
water
Macquarie River
5 km
738
Figure 18 Extent of floodplain vegetation types at the northern section of the Macquarie Marshes on the Macquarie River.
Mapping Floodplain Vegetation Types of the Basin 62
river red gum
black box
Wee Wea
coolabah
lignum
wetland
grassland
chenopod
dry woodland
water
Namoi River
739
Figure 19 Extent of floodplain vegetation types on the Namoi River between Pilliga and Wee Wea.
Mapping Floodplain Vegetation Types of the Basin 63
river red gum
black box
coolabah
Gwydir River
River
lignum
wetland
chenopod
dry woodland
water
2 km
Mehi River
740
Figure 20 Extent of floodplain vegetation types at Gwydir Wetlands, west of Moree on the Gwydir River.
Mapping Floodplain Vegetation Types of the Basin 64
river red gum
black box
Callandoon Branch
coolabah
lignum
wetland
grassland
chenopod
Goondiwindi
dry woodland
water
2 km
Mcintyre River
741
Figure 21 Extent of floodplain vegetation types on the Macintyre River west of Goondiwindi.
Mapping Floodplain Vegetation Types of the Basin 65
river red gum
Narran River
black box
coolabah
Clear Lake
lignum
wetland
grassland
chenopod
water
Narran Lake
2 km
742
Figure 22 Extent of floodplain vegetation types at Narran Lake Nature Reserve at the end of the Narran River.
Mapping Floodplain Vegetation Types of the Basin 66
river red gum
black box
coolabah
lignum
grassland
chenopod
water
5 km
743
Figure 23 Extent of floodplain vegetation types at Yantabulla Swamp on the Cuttaburra Creek.
Mapping Floodplain Vegetation Types of the Basin 67
river red gum
black box
coolabah
Caibocaro Billabong
wetland
dry woodland
chenopod
Paroo River
water
2 km
744
745
Figure 24 Extent of floodplain vegetation types at Nocoleche Nature Reserve on the Paroo River.
Mapping Floodplain Vegetation Types of the Basin 68
746
Discussion
747
748
The combination of remote sensing and machine learning (i.e. random forests) was successful in
749
predicting the distribution of floodplain vegetation and some of the component vegetation types on
750
the floodplains of the Murray-Darling Basin. The multi-objective cover model and classification tree
751
were used to successfully map the extent of forest and woodlands dominated by river red gum,
752
black box and coolabah (Table 19). The multi-objective cover model was unable to consistently
753
distinguish among grass-dominated and shrub-dominated vegetation types. The floodplain
754
vegetation type map presented here attempted to provide a consistent vegetation classification
755
across the whole Murray-Darling Basin. The maps of tree-dominated vegetation types will provide a
756
useful tool for managing and monitoring the responses of these ecosystems to changes in water
757
availability. The limitations of the current map suggest that predictions of the distribution of
758
floodplain vegetation types could be improved by expanding what vegetation types were modelled,
759
and improving both the ground survey and remote sensing data sets used.
760
761
The modelling approach was most successful in predicting the distribution of the most common
762
tree-dominated vegetation types of the Basin's floodplains. The predictions for river red gum
763
woodlands and forests (84%), black box woodlands (77%) and coolabah woodlands (89%) were
764
highly accurate (Table 19). The distribution of river red gum was clearly mapped along the channels
765
of rivers of the Basin (Figures 10 & 19). Large expanses of river red gum, such as the Barmah-
766
Millewa Forest, Yanga National Park and Macquarie Marshes, were well predicted (Figures 12, 14 &
767
18). The transition from river red gum to black box or coolabah woodlands away from rivers was
768
shown clearly on the map (Figure 17). At the scale of individual pixels, there were misclassifications
769
among these types, with scattered areas of coolabah being in the southern Basin. This minor
770
limitation of the modelling could be corrected by including a latitudinal rule to the classification tree.
771
It would be useful to reduce the pixel resolution from 25 m to 100 m using some type of local
772
averaging to produce more consistent predictions of the vegetation types across a particular
773
floodplain. The successfully prediction of the distribution of majority of forests and woodlands
774
demonstrates the usefulness of the modelling approach in providing a consistent classification
775
across the vast expanse of the floodplains of the Murray-Darling Basin.
776
777
Strong models were not built for the distribution or cover of river oak forests and river cooba
778
woodlands (R2 < 0.3, Table 15 & 17). The lack of success with river oak was due to a limited available
779
data set (278 records). The failure to predict river cooba well was not a result of having few records
Mapping Floodplain Vegetation Types of the Basin 69
780
but a reflection of the ecology of the species. River cooba is found predominantly as a subdominant
781
tree in association with other floodplain dominants, such river red gum and black box. Therefore,
782
without a good sample of the mono-dominant woodlands of this species it was unlikely that this
783
type could be predicted. Given the restricted nature of river cooba woodlands, we do not think this
784
is a major limitation of the modelling and map. River oak was rarely predicted across the map and
785
certainly not in the eastern headwaters of the Murray-Darling floodplain. It appears that stands of
786
river oak have been confused with other riparian trees. River oak may have been better predicted
787
by building cover models for associated forest types found at higher elevation in the Basin.
788
789
Although the distinction between woody and non-woody vegetation was robust, the modelling
790
approach was not particularly successful in distinguishing among the tree-less vegetation types.
791
Graminoid (grass, sedge or rush dominated) wetlands were confused with native grasslands in many
792
catchments of the Basin. This issue was common in the eastern Basin and not apparent in the arid
793
floodplains of the Warrego, Paroo, Lower Darling and Lower Murray Rivers. The ground survey data
794
set used to build the model included few vegetation surveys of more permanent wetlands.
795
Therefore, the majority of samples from graminoid wetlands would have had similar floristic and
796
reflectance characteristics to a lot of the samples from native grasslands. Prediction of graminoid
797
wetlands would be improved by a larger sample of surveys from within wetlands.
798
799
The modelling predicted large areas of lignum outside of the woodlands, which included areas
800
dominated by chenopods and samphire or with sparse vegetation cover. A moderate sample of
801
lignum records was available (1918 records), so predictions could be improved by more records for
802
this taxa. Improvements are likely to be gained by obtaining more records of the types (chenopods,
803
samphire and bare ground) that were confused with lignum shrublands. The failure to predict
804
lignum shrublands accurately is a reflection of the ecology of these communities. First, lignum
805
shrublands are often interspersed with areas of bare ground or low shrubs and, therefore, are poorly
806
resolved at the scale of the imagery (25 m pixels). Second, the appearance of lignum varies
807
substantially from thick and green following flood to dying back to dead aerial stems during periods
808
of drought. We were largely attempting to predict the fine scale distribution of this species based on
809
a long-term reflectance composite. The 2000-2010 Landsat composite may not have been a long
810
enough chronosequence of images to distinguish the distribution of this variable species. That is,
811
over this period the signal from lignum was probably noisy due different locations having sufficient
812
inundation, temporarily benefitting from inundation or not being inundated. The combination of
Mapping Floodplain Vegetation Types of the Basin 70
813
imagery from individual years following floods and drought may be useful in predicting this and
814
other floodplain vegetation types.
815
816
The above shows that the major limitations of the floodplain vegetation type map of the Basin and
817
associated modelling were an inability a) to predict less common tree-dominated communities and
818
b) to distinguish among types of shrublands (lignum, chenopod, samphire) or grasslands (native
819
grasslands, graminoid wetlands). Both these limitations could be improved in future modelling and
820
mapping by increasing the amount and breadth of samples for these vegetation types, using
821
additional remote sensing data sets and refining the modelling approach. The target floodplain
822
types were a mixture of structural types (e.g. grasslands), species-dominated types (e.g. river red
823
gum) and inundation types (e.g. wetlands). This classification needs to be refined and rendered
824
consistent to enable clear separation of types and ensure certain floodplain types do not fall outside
825
of the classification. A hierarchical classification in the order: broad landscape types (e.g. native
826
vegetation), inundation types (wetland/dryland), structural types (e.g. woodlands) and finally
827
species-dominated types (e.g. river red gum) is likely to improve the separation of floodplain
828
vegetation types.
829
830
Given the short time frame for this project, the most easily obtained (e.g. databased) vegetation
831
data sets were included. A more extensive search for vegetation data sets, including contacting
832
various regional government offices, consultants and universities may provide a more
833
comprehensive data set of the Basin's floodplain vegetation. Even such an increased data set is
834
likely to be limited both in species records and spatial representation (few records in the arid west of
835
the Basin). If certain vegetation types or regions require more accurate mapping, then targeted
836
ground surveys will be needed to improve their prediction. There was strong relationship between
837
the number of records included in a taxa distribution and the strength of its validation (Fig. 3). There
838
was a steep improvement in the strength of the taxa distribution models up to 5000 records
839
suggesting that increasing the number of records for a given taxa towards this number will provide
840
substantial improvements in predicting its extent.
841
842
The mapping approach taken here was implemented in four stages. We modelled the extent of
843
native vegetation, non-native vegetation and water across the Basin. The distribution of the target
844
taxa were modelled across the Murray-Darling Basin to utilise the presence data set, which was
845
much larger than the quadrat data set, and provide additional information on the extent of the
Mapping Floodplain Vegetation Types of the Basin 71
846
vegetation types. These two data sets were then used as inputs to help predict the cover of the
847
target vegetation types in the multi-objective cover model. The multi-objective model predicted the
848
cover of the target taxa (e.g. river red gum), as well as some structural types (e.g. woody
849
vegetation), inundation types and non-target taxa (e.g. chenopods). The extent of the floodplain
850
vegetation types was then determined using a classification tree informed by a) the extent of native
851
vegetation and b) the cover models from the multi-objective model. This approach was partially
852
successful but would be improved by predicting a more comprehensive range of structural types,
853
floodplain vegetation types and adjacent non-floodplain vegetation types. To improve the
854
prediction and mapping of floodplain vegetation types, we suggest the modelling approach takes the
855
following hierarchical approach.
856
1. Predict the extent of native vegetation, non-native vegetation, bare ground/ephemeral and
857
water.
858
2. Predict the extent of floodplain and dryland vegetation.
859
3. Predict the extent of structural types: forest, woodland, tall shrublands, low shrublands and
860
861
grassland.
4. Within each structural types, predict a representative range of vegetation types on the
862
floodplain and from the adjacent dryland communities.
863
864
This differs from the approach taken here by including bare ground implicitly, modelling the full
865
range of structural types, and including more target and non-target vegetation types. Including
866
more structural types is likely to improve the separation of lignum from other communities on the
867
floodplain such as chenopods and samphire. Modelling the extent of non-floodplain forests and
868
woodlands should help delimit the edge of the floodplain and the extent of the associated tree
869
types.
870
871
The prediction of the structural types would be improved by including remote sensing data sets
872
known to provide information beneath the canopy unlike reflectance data such as that provided by
873
Landsat. Synthetic aperture radar data (e.g. ALOS PALSAR) provides structural data across the Basin.
874
Here, the use of PALSAR imagery with a resolution of 50 m was an important predictor of the
875
distribution of the target taxa. Imagery of a finer resolution may be more appropriate for detecting
876
the changes from woodland to low shrubland found over short distances from the rivers in the arid
877
parts of the Basin. LiDAR is the best data for providing structural information about vegetation.
878
Although current LiDAR coverage of the floodplains of the Basin is patchy, it could be incorporated
Mapping Floodplain Vegetation Types of the Basin 72
879
into future modelling. Ideally, you would have LiDAR coverage across the whole floodplain. We
880
strongly suggest that there is future investment in obtaining such a valuable data set to improve the
881
prediction of the floodplain vegetation and other important information such as flow patterns.
882
883
Some useful non-floodplain tree types to include in future modelling would be the surrounding box
884
woodlands [Eucalyptus microcarpa (grey box), Eucalyptus populnea (bimble box) and Eucalyptus
885
conica (fuzzy box)], open forests [Eucalyptus viminalis (manna gum), Eucalytpus obliqua (messmate)
886
and Eucalyptus radiata (narrow-leaved peppermint)], mallee woodlands (Eucalyptus incrassata,
887
yellow mallee) and buloke (Allocasuarina luehmannii). It would be helpful to model additional
888
floodplain tree types such as yapunyah (Eucalyptus ochrophloia), which is common in the north-west
889
of the Basin. The success of modelling these additional vegetation types would depend on the
890
availability of adequate quadrat data.
891
892
Conclusion
893
A series of vegetation models were built successfully from a range of reflectance, structural and
894
elevation variables derived from satellite data sets. We were able to accurately predict the
895
distribution of native vegetation and produce useful models of the distribution of the target species
896
across the Basin. A multi-objective cover model, which was informed by the above satellite-derived
897
variables, provided strong models of cover for the majority of target and non-target vegetation
898
types. The distributions of the target floodplain vegetation types were determined from the extent
899
of native vegetation and the predicted cover values from the multi-objective model using a
900
classification tree. This modelling approach produced a map that:
901
902
1. successfully predicted the distribution of forests and woodlands of river red gum, black box
and coolabah.
903
2. rarely predicted river cooba woodlands or river oak forests
904
3. predicted the distribution of lignum shrublands to include areas of chenopod shrublands and
905
906
907
sparse floodplain.
4. predicted graminoid wetlands on lower areas of the floodplain but also on higher parts of the
floodplain that are dominated by native grasslands
908
909
Our map provides the first consistent classification and mapping of river red gum, black box and
910
coolabah woodlands across the Murray-Darling Basin (Table 24). It demonstrates that satellite-
911
derived data can be used to predict floodplain vegetation types across vast areas. The inability to
912
predict the rarer tree types and to consistently distinguish shrublands, grasslands and wetlands
Mapping Floodplain Vegetation Types of the Basin 73
913
revealed limitations of the current modeling. These limitations suggest improvements for future
914
modeling of these floodplains that include:
915
1. Compiling a more comprehensive quadrat data set.
916
2. Targeted surveys of species and areas of interest.
917
3. Increasing the number of structural types modeled.
918
4. Increasing the number of both floodplain and non-floodplain vegetation types modeled.
919
5. Including remote sensing data sets that better inform the prediction of structural types and
920
flooding regimes (e.g. LiDAR).
921
922
923
924
925
Table 24 Summary of the success of mapping the target floodplain vegetation types.
926
Floodplain vegetation
Accuracy
Strength of predictions
type
River red gum forest and 83.7%
Predicted known distribution well
woodlands
Black box woodlands
77.2%
Predicted known distribution well
Coolabah woodlands
89.1%
Predicted well across known distribution but in scatter pixels
outside of this area across the southern Basin
River Cooba woodlands
24.0%
Predict in limited areas
River Oak Forest
8.2%
Not predicted across known distribution
Lignum shrublands
62.2%
Confused with other shrublands and sparse floodplain
Grasslands/herblands
74.4%
Often predicted as graminoid wetlands
Graminoid wetlands
12.3%
Predicted well on lower floodplain but confused with native
grasslands higher up on the floodplain
927
928
929
Mapping Floodplain Vegetation Types of the Basin 74
930
Acknowledgments
931
This research was funded by the Murray-Darling Basin Authority (MD2245). We thank our
932
collaborators at the Murray-Darling Basin Authority (Michael Wilson, Paul Carlile, Ashraf Hanna,
933
Anastasia Stramarcos and Lex Cogle) and Geosciences Australia (Leo Lymburner, Rachel Melrose, Jeff
934
Kingwell and Medhavy Thankappan). We thank Geosciences Australia for their efforts in creating a
935
consistent historical Landsat composite across the Basin. We thank the Queensland Herbarium, New
936
South Wales Office of Environment and Heritage, Victorian Department of Environment and Primary
937
Industries, and the South Australian Department of Environment, Water and Natural Resources for
938
providing vegetation survey data sets across the Basin. Without this combination of data sets, it
939
would not have been possible to distinguish and map the floodplain vegetation types as successfully.
940
Mapping Floodplain Vegetation Types of the Basin 75
941
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942
943
944
945
946
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Cunningham SC, Mac Nally R, Griffioen P and White M (2009b) Mapping the Condition of River Red
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Mapping Floodplain Vegetation Types of the Basin 77