mapping the condition of

MAPPING THE CONDITION OF RIVER RED GUM (Eucalyptus camaldulensis Dehnh.) AND BLACK BOX (Eucalyptus largiflorens F.Muell.) STANDS IN THE LIVING MURRAY ICON SITES

Stand Condition Report 2012

Shaun C. Cunningham, Peter Griffioen, Matt White and Ralph Mac Nally A Milestone Report to the Murray-Darling Basin Authority as part of Contract MD1114.

Shaun C. Cunningham* and Ralph Mac Nally Australian Centre for Biodiversity, School of Biological Sciences, Monash University, VIC 3800

Peter Griffioen Ecoinformatics Pty. Ltd., Heidelberg, VIC 3084

Matt White Arthur Rylah Institute, Department of Sustainability and Environment, Heidelberg, VIC 3084

*

Corresponding author: Tel.: +61 3 9902 0142: Fax: +61 3 9905 5613

E-mail address: [email protected]

This report should be cited as: Cunningham SC, Griffioen P, White M and Mac Nally R, (2013) Mapping the Condition of River Red Gum (Eucalyptus camaldulensis Dehnh.) and Black Box (Eucalyptus largiflorens F.Muell.) Stands in The Living Murray Icon Sites. Stand Condition Report 2012. Murray-Darling Basin Authority, Canberra.

Cover photograph: Flooded river red gum forest in Green Swamp, Gunbower Island taken by Shaun Cunningham.

© Copyright Commonwealth of Australia 2013. This work is copyright. With the exception of the photographs, any logo or emblem, and any trademarks, the work may be stored, retrieved and reproduced in whole or in part, provided that it is not sold or used for commercial benefit. Any reproduction of information from this work must acknowledge the Murray–Darling Basin Authority, the Commonwealth of Australia or the relevant third party, as appropriate, as the owner of copyright in any selected material or information. Apart from any use permitted under the Copyright Act 1968 (Cth) or above, no part of this work may be reproduced by any process without prior written permission from the Commonwealth. Requests and inquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney General’s Department, National Circuit, Barton ACT 2600 or posted at http://www.ag.gov.au/cca.

Disclaimer This document has been prepared by the Murray-Darling Basin Authority for general use and to assist public knowledge and discussion regarding the integrated and sustainable management of the Basin’s natural water resources. The opinions, comments and analysis (including those of third parties) expressed in this document are for information purposes only. This document does not indicate the Murray-Darling Basin Authority’s commitment to undertake or implement a particular course of action, and should not be relied upon in relation to any particular action or decision taken. Users should note that developments in Commonwealth policy, input from consultation and other circumstances may result in changes to the approaches set out in this document

Executive Summary Here we report on a model built to predict stand condition of forests and woodlands dominated by river red gum and black box across The Living Murray Icon Sites in 2012. This is the third of three years (2009, 2010 and 2012) of modelling towards the building of a tool that will allow the Murray-Darling Basin Authority to estimate stand condition across the Icon Sites from a combination of ground surveys of 175 reference sites and satellite data. Stands were not surveyed during 2011 due to limited access to the floodplains after the extensive flooding in 2010. A lack of Landsat coverage over Australia in early 2012 necessitated a change to Rapideye imagery.

The 2012 ground assessments were predicted successfully (R2 = 0.71) from Rapideye imagery, derived structural data and Landsat-derived historical indices using a predictive clustering tree. This demonstrates that the stand condition modelling approach works equally successfully after floods as it did during the drought. An independent survey of 50 new sites showed that the 2012 Stand Condition Model has strong predictive power (R2 = 0.83) outside of the reference sites used to build it. Together these results provide us with considerable confidence that the Stand Condition Tool will be able to predict stand condition under a range of environmental conditions and outside the 175 sites used to build it.

The 2012 Stand Condition Model predicted that: •

73% of the area covered by river red gum, black box and box communities in The Living Murray Icon Sites was in a stressed condition (moderate to severely degraded condition) in 2012.

•

Extent of good condition stands increased from 21% to 27% between 2010 and 2012 while the extent of poor to severely degraded condition stands remained stable at 36%.

Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 1

•

Of the forest types, river red gum forest had the largest extent (41%) of good condition stands, whereas good condition stands were rare (5%) in black box woodlands.

•

Stand condition differed among the Icon Sites in 2012, with the Barmah-Millewa Forest having the least extent of stressed stands (58%) and Hattah Lakes having the largest extent of stressed stands (99%).

•

Extent of good condition stands increased in the upper Murray Icon Sites (Barmah-Millewa Forest, Gunbower-Koondrook-Perricoota Forests), while they decreased in the lower Murray (Hattah Lakes to Chowilla Floodplain) between 2010 and 2012.

•

Good condition stands were concentrated along the river channel, and around permanent anabranch creeks and wetlands that were well connected to the main river channel.

These predictions of the 2012 Stand Condition Model suggest that managed and unmanaged floods during 2010 and 2011 lead to improvements in stand condition of small areas of floodplain forests and woodlands but large areas of the Murray River floodplain continued to decrease in stand condition.


Table of Contents Executive Summary

1

Introduction

4

Methods

7

Study area

7

Reference sites

8

Validation sites

8

Condition assessment

9

Satellite imagery

10

Environmental variables

14

Modelling

15

Map analysis

19

Results

21

Condition assessment survey

21

Modelling

21

Validation

22

Map characteristics

29

Discussion

40

Conclusion

45

Acknowledgements

46

References

47

Appendix

49


Introduction

The forests and woodlands of the Murray River floodplain have been declining rapidly in condition over the past two decades (Margules & Partners, 1990; Cunningham et al., 2009b). This dieback is associated with increased regulation and extraction of water from the Murray River, and an extended period of drought. Water availability on the floodplain has decreased with reduced flooding and increasing salinity of soils, ground water and river water in many areas. ‘The Living Murray’ program of the Murray-Darling Basin Commission (now Murray–Darling Basin Authority) was established in 2002 to restore the health of the basin by returning water to these floodplains (MDBC, 2002). This has involved water recovery, construction of environmental works and measures, and an environmental watering and monitoring effort across the six Icon Sites. The Murray-Darling Basin Authority decided in 2008 that a remote sensing approach was necessary to provide adequate monitoring of changes in the condition of forests and woodlands across the whole Murray River floodplain.

We had previously quantified the condition of river red gum stands across the Victorian Murray River floodplain using a combination of quantitative ground surveys, remotely-sensed data and several modelling methods (Cunningham et al., 2009b). This approach allowed us to predict forest condition on this floodplain (ca 100 000 ha), with high accuracy (R2 = 0.78) and resolution (25 m x 25 m pixels). This Living Murray project ‘Mapping Of Stand Condition For The Living Murray Icon Sites’ builds on the previous work, expanding into new forest types (black box and mixed box woodlands) and increasing predictive power by modelling data over three years.


The project aims to complement site-based ground surveys with annual maps of stand condition across the whole Murray River floodplain. The specific aims of the project are: 1. Survey condition of river red gum and black box stands across The Living Murray Icon Sites excluding the Lower Lakes, Coorong and Murray Mouth. 2. Predict and map stand condition of these Icon Sites in 2003, 2008, 2009, 2010 and 2012. 3. Build a Stand Condition Tool that can be used to predict stand condition of the Icon Sites annually using current ground assessments of reference sites and satellite imagery. In the first year of the project ‘Mapping Of Stand Condition For The Living Murray Icon Sites’, we assessed stand condition of forest types dominated by river red gum and black box using ground surveys of 175 reference sites (Cunningham et al., 2009a). These assessments were predicted successfully (R2 = 0.68) from Landsat imagery using an artificial neural network. The 2009 Stand Condition Model was then used to backcast stand condition of the Icon Sites in 2003 and 2008 from historical Landsat imagery. The 2009 Stand Condition Model predicted that 79% of the area covered by river red gum, black box and box communities in The Living Murray Icon Sites were in a stressed condition in 2009. Stand condition at the Icon Sites was similar between 2008 and 2009. In contrast, the extent of stressed stand condition across the Icon Sites was substantially lower (66%) in 2003 compared with 2009. Areas where stand condition changed from stressed to good stand condition between 2003 and 2009 corresponded to areas that received environmental watering over this period. These predictions suggested that although extensive areas of the Murray River floodplain continued to decline in stand condition, almost certainly due to a lack of flooding, the limited areas receiving environmental water improved in condition.


Surveys of stand condition in 2010 at the reference sites were less successfully predicted (R2 = 0.58) than the first year from Landsat imagery and derived structural data using an artificial neural network [Cunningham et al 2011. This was predominantly due to the number of references sites in good condition halving between 2009 and 2010 and the majority (77%) of sites being in poor to moderate condition. Consequently, the model fitted well at these intermediate values but poorly at extreme values (good and severe condition). This issue was addressed statistically by transforming the predictions towards equality (the line where observations equal predictions) but could be solved logistically by surveying additional good and degraded condition sites.

In 2011 there were two significant delays to the development of the Stand Condition Tool. First, the extensive floods that began in spring 2010 prevented access to the majority of reference sites during early 2011. It was decided to postpone ground surveys until early 2012 when flood waters should have receded and any positive growth response would still be apparent. Second, the Landsat 5 satellite stopped providing imagery over Australia in November 2011 due to a declining power source. This necessitated the shift to the equivalent imagery provide by the Rapideye satellite constellation. Consequently, in order to build a Stand Condition Tool based on Rapideye imagery, stand condition needed to be remodelled for the three years (2009, 2010 and 2012) using Rapideye imagery to ensure strong predictive power in subsequent years.

Here, we report the details of a) methods used to model stand condition data collected in forests and woodlands of The Living Murray Icon Sites between January and May 2012 from Rapideye data, b) validation of the predictions from the 2012 Rapideye-based Stand Condition Model using an independent ground survey of 50 new sites, and c) mapping of stand condition across the Icon Sites in 2012 using the 2012 Stand Condition Model.


Methods

Study area The study area included forests and woodlands across the Murray River floodplain in southeastern Australia. The area comprised the five Icon Sites of The Living Murray (TLM) program (MDBC, 2005a) that contain treed vegetation: BarmahMillewa Forest, Gunbower-Koondrook-Perricoota Forests, Hattah Lakes, Chowilla floodplain and Lindsay-Wallpolla Islands, and the River Murray Channel (Figure 1). For the purpose of the current project, the River Murray Channel Icon Site included all forest and woodlands within 2 km of the Murray River channel. This area encompassed 276 140 ha of forest from the Hume Dam in Victoria (36⁰ 06´S 147⁰ 01´E) to the lower end of the Murray River at Wellington, South Australia (35⁰ 19´S 139⁰ 23´E). All forest types that are dominated by river red gum (Eucalyptus camaldulensis) or black box (E. largiflorens) were included.

The distribution of river red gum and black box across The Living Murray Icon Sites was defined using existing digital vegetation maps (Cunnnigham et al., 2009a). Mapping for the Middle Murray in New South Wales side did not distinguish black box woodlands from other box woodlands, so two additional forest types were included for Millewa and Koondrook-Perricoota only. Distributions for the following forest types were created for the five Icon Sites: 1. River red gum forest – stands dominated by E. camaldulensis with 30-45% projective foliage cover. 2. River red gum woodland – stands dominated by E. camaldulensis with 20-25% projective foliage cover. 3. River red gum / black box woodland – mixed stand of E. camaldulensis and E. largiflorens. 4. Black box woodland – stands dominated by E. largiflorens.


5. River red gum / box woodland – stands dominated by E. camaldulensis, E. largiflorens and E. macrocarpa included in Millewa and Koondrook-Perricoota only. 6. Box woodland – stands dominated by E. largiflorens and E. macrocarpa included in Millewa and Koondrook-Perricoota only.

Reference sites A total of 174 reference sites were surveyed across The Living Murray Icon Sites to inform the Stand Condition Model. Within each Icon Site, reference sites were distributed across the forest types according to how much area they covered. In 2009, reference sites were chosen to be representative of the range of forest types, forest condition and landscape positions (e.g. riverine, wetland and floodplain) at each Icon Site. This approach provided sites with a full range of current stand condition (Cunningham et al., 2009b). Thirteen reference sites were not accessible in early 2012 due to continued flooding of many areas. These flooded reference sites were predominantly river red gum forests (9 sites) and woodlands (3 sites) in the middle Murray River, with the exception of a single black box woodland at Chowilla Floodplain. Replacement sites were selected using the 2010 Stand Condition Map for the same forest types within the same region of the Icon Site (e.g. Gunbower Forest).

Validation sites A total of 50 new sites were surveyed in early 2012 to provide an independent test of the predictive power of the 2012 Stand Condition Tool. The site selection was stratified to provide the full range of forest types and stand condition, and, therefore, a robust validation of the tool’s predictive power. Sites were located in two contrasting floodplains: Gunbower Forest and Chowilla Floodplain. Gunbower Forest is dominated by river red gum forests of relatively high density whereas Chowilla Floodplain is dominated by sparse black box woodlands. Within a floodplain, the numbers of validation sites chosen were distributed across the forest


types according to how much area they covered. Then within each forest type, the 2010 stand condition map (Cunningham et al 2011) was used to stratify selection of sites covering a range of condition.

Condition assessment Reference sites were assessed by regional land managers and consultants (see Acknowledgements) between January and May 2012. Surveys were conducted by South Australian Department for Water (Chowilla floodplain), Murray-Darling Freshwater Research Centre (Lindsay, Mulcra and Wallpolla Islands, and Hattah Lakes), Australian Ecosystems (Gunbower Island, and Koondrook- Perricoota Forests), and NSW National Parks and Wildlife Service (Millewa and Barmah Forests). At each site location, the established 0.25 ha plot was assessed. Most plots were 50 x 50 m plots but four rectangular plots (125 x 20 m) were used to assess linear stands along watercourses on the Chowilla floodplain. The stand condition assessment involved measuring the three indicators percentage live basal area, plant area index and crown extent, which are known to be reliable and objective indicators of condition in stands of river red gum (Cunningham et al., 2007).

Plant area index (PAI) is the area of leaves and stems per unit ground area

without adjustment for clumping of canopy components. PAI was estimated from hemispherical photographs of the canopy, which were first classified using image analysis (MultiSpec Application Version 3.1, Purdue University, Indiana), with the program Winphot 5.00 (ter Steege, 1996). Crown extent is the percentage of the potential crown, which is determined by the extent of the existing branching structure, that contains foliage. Crown extent was estimated by two observers using an interval scale (0%, 1-20%, 21-40%, 41-60%, 61-80%, 81-100%) from 30 trees representative of the range of tree size and condition within a plot. Percentage live basal area (%LBA) is the percentage of a stand’s basal area that is contributed by live trees. Trees were considered alive if there was live foliage within the crown. PAI was standardized relative to the maxima measured for each Forest Type within a Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 9

Bioregion (Riverina, Murray Mallee). This accounted for the historical reduction in PAI owing to the decline in productivity associated with reduced rainfall, flooding and increased evaporation downstream along the Murray River floodplain (Bioregion) and local differences in water availability within a floodplain (Forest Type). Scores for each condition indicator were converted to values out of 10 (PAI and %LBA x 10, and crown extent x 2). A stand condition score (SCS) was calculated from the average score of the three condition indicators, which had a maximum of 10 points.

Satellite imagery Rapideye imagery covering the Murray River floodplain was obtained from the AAM Group to allow stand condition to be modelled across The Living Murray Icon Sites. Sixty-seven tiles (25 km x 25 km each) of Rapideye data were required to cover the whole floodplain (Table 1). Ideally tiles should be selected from the same period as the ground survey (January-April 2012). However, selected tiles covered the period December 2011 to December 2012 due to the lack of cloud-free images or regular acquisitions in many areas. The tiles were supplied by AAM as a top of atmosphere corrected mosaic of the floodplain. Rapideye imagery provides spectral information at a 5 m pixel resolution. This is a much finer resolution than the actual ground surveys (50 m x 50 m plots). Consequently, the Rapideye imagery was resampled at 25 m x 25 m scale converting it to the same scale as Landsat imagery. We considered this to be an appropriate pixel resolution to ensure a pixel fell within plot locations (50 x 50 m) and to estimate stand condition across the Icon Sites, which were at least five orders of magnitude larger in area. The rescaling allowed the estimation of a mean and stand deviation for each pixel.


SA

Chowilla floodplain and Lindsay-Wallpolla

NSW MALLEE

RIVERINA

Hattah Lakes Barmah-Millewa Forest Wellington

Vic River Murray Channel Hume Dam

Gunbower-KoondrookPerricoota Forests

100 km

Figure 1 Study area indicating the location of The Living Murray Icon Sites that contain treed vegetation across the Murray River floodplain in New South Wales, Victoria and South Australia.


Table 1 Rapideye satellite imagery obtained for the 2012 Stand Condition model.

Tile ID 5522414 5522413 5522513 5522512 5522412 5522511 5522411 5522510 5522509 5522508 5522507 5522607 5522407 5522406 5522405 5522505 5522605 5522504 5522604 5522603 5522703 5422725 5422724 5422824 5422823 5422923 5423023 5423024 5423123 5423122 5423022 5423121 5423021

Location Albury Chiltern Howlong Corowa Rutherglen Mulwala Yarrawonga Barooga Tocumwal Barmah-Millewa east Barmah-Millewa west Millawa north Nonoka Echuca Terrick Gunbower Tantonan Cohuna Koondrook Kerang Campbells Island Pental Island Swan Hill Tyntynder Nyah West Piangil Yungera Yanga Waldaira Lake Benanee Wandown Robinvale Pound Bend

Acquisition Date 22/07/2012 7/06/2012 7/06/2012 7/06/2012 7/06/2012 12/12/2012 12/12/2012 6/06/2012 8/12/2011 8/12/2011 12/12/2011 12/12/2011 12/12/2011 12/12/2011 5/12/2011 5/12/2011 5/12/2011 5/12/2011 5/12/2011 15/01/2012 15/01/2012 12/01/2012 12/01/2012 12/01/2012 14/01/2012 14/01/2012 16/01/2012 12/01/2012 16/01/2012 12/01/2012 16/01/2012 16/01/2012 15/01/2012


Tile ID 5423020 5423120 5423019 5423119 5423220 5423219 5423319 5423318 5423317 5423316 5423416 5423315 5423415 5423414 5423314 5423413 5423313 5423213 5423312 5423212 5423311 5423310 5423410 5423409 5423309 5423209 5423109 5423009 5422909 5423008 5422908 5422808 5422809 5422708

Location Lake Hattah Tarpaulin Bend Hattah Nowingi Lamber Island Red Cliffs Mildura Wentworth Mullaroo Mulcra Lake Victoria Lindsay Island Cal Lal Chowilla Murtho Calperum Renmark Katarapko Loch Luna Kingston Waikerie Glen-Lee Bungunnia Morgan Woods Flat Brookfield Swan Reach Walkers Flat Ettrick Brae Bonython Wall Murray Bridge Tailem bend Lake Alexandra

Acquisition Date 15/01/2012 16/01/2012 16/01/2012 20/04/2012 16/01/2012 16/01/2012 16/01/2012 18/04/2012 18/04/2012 24/12/2012 24/12/2012 24/12/2012 24/12/2012 17/01/2012 17/01/2012 19/04/2012 19/04/2012 19/04/2012 23/12/2011 23/12/2011 23/12/2011 12/03/2012 12/03/2012 4/12/2011 4/12/2011 4/12/2011 4/12/2011 4/12/2011 4/12/2011 4/12/2011 4/12/2011 4/12/2011 12/03/2012 4/12/2011


Environmental variables We used a combination of reflectance and derived structural data to inform the Stand Condition Model (Table 2, see Cunningham et al. 2009a for further detail). From the Rapideye imagery, we extracted mean and standard deviation values of reflectance for six spectral bands: blue (0.44-0.51 µm), green (0.52-0.59 µm), red (0.63-0.69 µm), red edge (0.69-0.73 µm) and near infrared (0.76-0.85 µm). Reflectance values from the red and near infrared bands were used to calculate the normalised difference vegetation index (NDVI). Values of NDVI were calculated from 2010 and 2009 Landsat imagery to provide historical information on vegetation condition at the sites. Historical NDVI was used to help distinguish between stands of good condition from stands with poor overstorey condition but green understoreys following the wet conditions from stands with, which would both have high reflectance in the red spectrum in 2012.

Previous modelling of stand condition of forests and woodlands across The Living Murray Icon Sites was strengthened by the inclusion of estimates of tree density and species mixes in addition to reflectance data (Cunningham et al., 2009a, 2011). A layer of tree probability, Pr(MurrayTree), was created for the whole floodplain using an existing Tree/No Tree layer for Victoria (P. Griffioen & M. White, unpublished). A neural network that predicted the probability of each Forest Type (e.g. river red gum woodland) occurring at a site was built using distribution maps and Landsat data within the forest types (Cunningham et al. 2009a). Although only the probability maps for river red gum forest and black box woodland had high accuracy (84%), all forest type probability maps were included in the modelling to potentially provide better context for each pixel. For example, stand condition at a pixel of river red gum woodland may not be predicted well by its probability map alone but in combination with other distribution maps it may be improved.


Modelling To map stand condition of the forest types across The Living Murray Icon Sites, relationships between stand condition assessed at reference sites during the field survey and remotely sensed environmental variables with coverage across the floodplain had to be determined. Here, we used both artificial neural networks (Rumelhart et al., 1986) and regression trees (Rokach & Maimon, 2008) to predict stand condition.

We used feed-forward multilayer perceptron artificial neural networks learned by a backpropagation algorithm (MLP neural network, Rumelhart et al., 1986) in the program Statistica 10.0. MLP neural networks are useful for modelling ecological data, which rarely meet parametric statistical assumptions and commonly involve non-linear relationships. They make no prior assumptions about the relationship between the input variables and the underlying mathematical distributions of the data (Özesmi et al., 2006). A MLP neural network is best conceptualized as a series of layers of nodes, with connections (neurons) between each adjacent layer. Here, the neural network included an input layer of environmental variables, hidden layers and an output layer of the stand condition score.

Regression trees were used because these select relevant environmental variables and can model interactions among variables. Regression trees overcome the inherent inaccuracies in seeking a single parsimonious model by constructing an ensemble of regression trees, which relate values of a response (leaves) to its predictors through a series of binary decisions or branches (Friedman, 2001). We used a particular type of regression tree known as predictive clustering trees (Kocev et al., 2007) in the program Clus. While most decision tree learners induce classification of regression trees, predictive clustering trees generalizes this approach by learning trees that are interpreted as cluster hierarchies.


The relationships between stand condition and environmental variables were determined using the following procedure. The independent environmental variables (reflectance and derived structural data) and dependent variables (condition indicator) were standardized by the range to values between 0 and 1. For MLP neural networks, the dataset was then divided into training (60%), selection (20%) and test data (20%). The training data were used to build the neural network, the selection data were used to check that the procedure did not over fit (i.e. the model has limited predictive power because it is fitting all the idiosyncrasies of the training data) and the test data were used to test the final fit of the model. Models were built from 200 random starts and the model with the best statistical fit (R2 values) was chosen to predict condition across the Living Murray Icon Sites. In the case of the predictive clustering trees, the data set was divided into training (70%) and test data (30%). Bootstrap aggregating (or bagging), which is similar to model averaging, was used to improve the accuracy of predictions from the regression tree. Models of ten bootstrap samples were fitted to create an ensemble tree that predicted stand condition.

To investigate whether the Stand Condition Tool could improve the predictions of condition in 2012, two types of models were fitted using both MLP neural networks and predictive clustering trees. First, ground surveys of the reference sites in 2012 were modelled against Rapideye imagery from 2012 , and structural data and a vegetation index (NDVI) derived from Landsat. Second, ground surveys from the three years (2009, 2010 and 2012) were modelled against Rapideye imagery from the year of survey and the above data derived from Landsat. The multi-year models used more observations (175 + 175 + 174 = 524 sites) than the single year model.


The model with the strongest predictive power (predictive clustering tree based on ground surveys from 2012 only) was used to predict stand condition across the Murray River floodplain. This model, like all the above models under-predicted the majority of good condition stands and over-predicted degraded stands (compare the regression lines with the unity lines). A linear transformation of the predictions of this model towards unity (i.e. observed equals predicted) improved the predictions, with the good condition stands no longer under-predicted. The relationship between stand condition score measured in the field and that predicted by the model for the reference sites was used to estimate the error associated with the model’s predictions for stand condition. Linear regression using Bayesian models in the WinBUGS software package (Spielgelhalter et al., 2003) was used to determine the lower (2.5%, worst case scenario) and upper (97.5%, best case scenario) credible intervals of the predictions for stand condition. Regression parameters were assigned uninformative normal priors. The equations for the lower and upper credible intervals were used to produce the worstcase and best-case predictions for stand condition across The Living Murray Icon Sites.


Table 2 Environmental variables used to model stand condition in 2012. SD = standard deviation of the mean. Environmental

Explanation

variable Rapideye Band 1 mean (2012)

Reflectance in the blue spectrum (0.44-0.51 µm) for 2012

Band 2 mean (2012)

Reflectance in the green spectrum (0.52-0.59 µm) for 2012

Band 3 mean (2012)

Reflectance in the red spectrum (0.63-0.69 µm) for 2012

Band 4 mean (2012)

Reflectance at the red edge (0.69-0.73 µm) for 2012

Band 5 mean (2012)

Reflectance in the near infrared (0.76-0.85 µm) for 2012

NDVI mean (2012)

Normalised difference vegetation index for 2012

Band 1 SD (2012)

SD of reflectance in the blue spectrum for 2012

Band 2 SD (2012)

SD of reflectance in the green spectrum for 2012

Band 3 SD (2012)

SD of reflectance in the red spectrum for 2012

Band 4 SD (2012)

SD of reflectance at the red edge for 2012

Band 5 SD (2012)

SD of reflectance in the near infrared for 2012

NDVI SD (2012)

SD of normalised difference vegetation index for 2012

Landsat5 NDVI (2009)


NDVI (2010)


Tree density Pr(MurrayTree)

Probability of trees being present

Forest Type probabilities Pr(RF)

Probability of river red gum forest

Pr(RW)

Probability of river red gum woodland

Pr(RGBB)

Probability of river red gum / black box woodland

Pr(BB)

Probability of black box woodland

Pr(RGBX)

Probability of river red gum / box woodland

Pr(BX)

Probability of box woodland


Map analysis Stand condition maps were produced by using the 2012 Stand Condition Model to calculate stand condition for each 25 x 25 m pixel of the study area based on its values of reflectance (Rapideye) and derived structural data. The 2012 Stand Condition Model predicted a stand condition score (SCS) that was based on the variables: percentage live basal area, plant area index and crown extent. Values of SCS ranged between 0 (dead) to 10 (excellent condition). For analysis, pixels with a tree probability of less than 10% according to the Murray Tree Model were removed from the maps because these are unlikely to contain trees. Maps were classified into five condition classes: good (SCS = 8.1-10.0), moderate (SCS = 6.1-8.0), poor (SCS = 4.1-6.0), degraded (SCS = 2.1-4.0) and severely degraded (SCS = 0-2.0) using ArcGIS (ESRI, Redlands, California). The characteristics of an average stand in each condition class are given in Table 3. Maps were produced for The Living Murray Icon Sites and the different forest types across the Murray River floodplain. Maps of stand condition for Icon Sites only except the River Murray Channel are presented to avoid duplication. From each map, the percentage of the total area included in each condition class was determined from pixel numbers in the attribute table of the rasters. This was determined for the whole Living Murray area, forest types across this area, Icon Sites and subregions of interest within Icon sites (e.g. Koondrook-Perricoota Forests), including values for each forest type with the areas (see Appendix).


Table 3 Stand condition classes used in the maps, their associated stand condition scores and the condition of an average stand within a condition class.

Stand condition of an average stand Stand condition class

Stand condition score

Plant area index

% live basal

Crown extent

area Good

8.1-10.0

80-100% of Forest Type - Bioregion max.

80-100%

80-100%

Moderate

6.1-8.0


60-80%

60-80%

Poor

4.1-6.0


40-60%

40-60%

Degraded

2.1-4.0


20-40%

20-40%

Severely degraded

0.0-2.0


0-20%

0-20%


Results

Condition assessment survey Due to continued flooding in many areas, only 162 of the 175 reference sites established at the Icon Sites in 2009 were resurveyed by regional land managers in 2012, with 12 replacement sites being surveyed. Values of condition indicators that make up the stand condition score changed between the 2010 and 2012 surveys (Figure 2). Crown extent showed increases in the majority of sites (Figure 2a). The proportion of live basal area and PAI were less variable between the two survey years (Figure 2b & 2c), with the standard deviation of the difference between years being 37% and 41% of the mean value in 2012. When combined as the stand condition score, there was not a consistent change in condition across the reference sites between 2010 and 2012 (Figure 2d). This lack of consistent improvement in condition was observed among the reference sites within each Icon Site. Overall, there were only nineteen sites that had increases in stand condition score of at least 1 point.

Modelling Predictive clustering trees were consistently better predictors of stand condition in 2012 than the MLP neural networks (Table 4). Including all three years of data into a model produced small improvements in the prediction of stand condition compared with using a single year of data. All the models under-predicted the majority of good condition stands and over-predicted degraded stands (Figure 3a, compare the regression line with the unity line). A linear transformation of the predictions of this model towards unity (i.e. observed equals predicted) improved the predictions, with the good condition stands no longer being under-predicted (Figure 3b).

The transformed 2012 Stand Condition Model predicted the stand condition score successfully (R2 = 0.71) from reflectance and derived structural data using a


predictive clustering tree (Figure 3c). Similarly, the predictions of the stand condition classes for the reference sites were 79.9% accurate (139 of 174 sites, Table 5). Any misclassifications were only predicted to be one class different to the observed value. Important variables in defining the lower branches of the predictive clustering tree were NDVI in 2009 and 2010, mean Rapideye reflectance in the blue, red and red edge spectra and the probability of river red gum forest (Table 6).

Validation An independent ground survey by Monash University of 50 stands (25 stands at Gunbower Forest and 25 at Chowilla floodplain) in February and March 2012 showed a strong correlation (R2 = 0.83) with the predictions of 2012 Stand Condition Model (Figure 4). The model predicted well across the condition scale, with only a slight under-prediction of good condition stands and a slight over-predicted for severely degraded stands. Furthermore, the predictions of the stand condition classes for the validation sites were 66.0% accurate (33 of 50 sites, Table 7). Any misclassifications were only predicted to be one class different to the observed value.


Figure 2 Relationships between the values for condition indicators a) crown extent, b) % live basal area c) plant area index (PAI) and d) stand condition measured at the reference sites (N = 162) during the 2010 and 2012 surveys. The thirteen replacement reference sites were not included in the analysis. The dashed line indicates equality. Variability (V) of the data between years was estimated by the standard deviation of the difference between years standardized by the mean value in 2012.


Figure 3 Relationships between stand condition surveyed at the reference sites in early 2012 and that predicted by two versions of the 2012 Stand Condition Model (PCT based on ground surveys from 2012, see Methods for details). Models include a) a model built using the reference sites (N = 174) and b) the same model transformed to equality (2012 Stand Condition Model used to produce all maps). Solid line is the result of linear regression and the dashed lines indicate equality.


Figure 4 Relationship between stand condition surveyed at the independent validation sites (N = 50) in early 2012 and that predicted by the 2012 Stand Condition Model. See the Methods for details of the validation survey). The solid line represents a linear regression and the dashed line indicates equality (predicted = observed).


Table 4 Comparison of fit (surveyed versus predicted condition) among the 2012 Stand Condition Models.

Model

Model fit (R2)

Inputs

type

Crown extent

PAI

%LBA

Condition

Single year

NN

0.59

0.67

0.37

0.60

Single year

PCT

0.61

0.74

0.52

0.71

Multi-year

NN

0.61

0.77

0.45

0.63

Multi-year

PCT

0.64

0.76

0.52

0.69

Table 5 Confusion matrix showing the accuracy of stand condition class predictions from the 2012 Stand Condition Model for the build sites (N = 175 stands). Values are the number of pixels. Observed Stand Condition

Predicted Stand Condition

severe

Accuracy

degraded

poor

moderate

good

severe

3

degraded

4

6

5

poor

1

8

14

4

1

6

104

3

6

9

91%

75%

moderate good 38%

40%

56%


Table 6 Important variables in defining the predictive clustering trees that underliethe 2012 Stand Condition Model. Numbers indicate how many of the 10 models of the ensemble included the variable at the branch level.

Environmental variable

Primary branches

Secondary branches

Landsat NDVI (2010)

6

4

Landsat NDVI (2009)

5

10

Rapideye Band 3 mean (2012)

2

2

Pr(RF)

1

4


1

3


1

2

Pr(RW)

1

1

Pr(MurrayTree)

5


2

Pr(RGBB)

1

Pr(Box)

1


Table 7 Confusion matrix showing the accuracy of stand condition class predictions from the 2012 Stand Condition Model for the independent validation sites (N = 50). Values are the number of pixels. Observed Stand Condition

Predicted Stand Condition

severe

Accuracy

severe

0

degraded

6

poor

degraded

poor

6

3

2

10

moderate

2

good 0%

75%

67%

moderate

good

9

3

1

8

90%

73%


Map characteristics The 2012 Stand Condition Model for The Living Murray Icon Sites provided an estimate that 73% of the area covered by forest and woodland was in a stressed condition (moderate to severely degraded condition) in 2012 (Table 8). Of the area containing stressed stands, 36% was in poor to severely degraded condition. In comparison to predictions for condition in 2010, the extent of good condition stands has increased by 6% while the extent of moderate condition stands has decreased by the same amount while the extent of poor to severely degrade stands remained similar.

There were large differences in the extent of condition classes in 2012 among the different forest types (Table 9). River red gum forest had the largest extent of good condition stands (41%) among the forest types. In contrast, river red gum – black box woodlands and black box woodlands were predominantly in poor to severely degraded condition (63 and 74% respectively). River red gum – box woodlands and box woodlands were predominantly in moderate condition (64% and 76% respectively). The extent of good condition stands was predicted to have increased substantially within river red gum forest between 2010 and 2012 while the extent of good condition stands of river red gum – black box woodlands and black box woodlands decreased substantially (Table 10).

The 2012 Stand Condition Model predicts a downstream decline in the condition of The Living Murray Icon Sites (Table 11). Barmah-Millewa Forest had the smallest proportion of stressed stands (moderate to severely degraded condition, 58%) while Hattah Lakes had the largest extent of stressed stands, with only 1.5% of the floodplain forests and woodlands containing good condition stands. In GunbowerKoondrook-Perricoota Forests, stands were predominantly (66%) in moderate condition. From Hattah Lakes to Chowilla Floodplain, stands were predominantly in poor to severe condition (71-83%). The extent of good condition stands was predicted


to have increased in the upper Murray (Barmah, Millewa, Gunbower, Koondrook and Perricoota Forests) between 2010 and 2012 while the extent of good condition stands has decreased in the lower Murray (Hattah Lakes to Chowilla Floodplain, Table 12).

The distribution of good condition stands tended to be around areas receiving recent inundation. At Barmah-Millewa Forest, areas of good condition stands were predicted to be widespread across the low lying wetlands and plains (Figure 5). Good condition stands were more restricted at Gunbower-Koondrook-Perricoota Forest, with patches concentrated around the lagoons of the northern end of Gunbower Island and the river channel (Figure 6). A past fire in Moira State Forest and the centre of Gunbower Island showed up as areas of poor condition forest (Figures 5 & 6). On the floodplains of Hattah Lakes, stands of good condition were predicted to be restricted to around the lakes and along both branches of Chalka Creek (Figure 7). Further downstream at Wallpolla, Mulcra and Lindsay Islands, and Chowilla floodplain good condition stands were restricted predominantly to the river channel (Figure 8). On the large floodplains of Chowilla and Hattah Lakes, there were extensive areas of stands in degraded condition away from the river (Figures 7 & 8).


Good

Gulpa Island SF Edwards River

(8.1-10.0)

Moderate

(6.1-8.0)

Poor

(4.1-6.0)

Degraded

(2.1-4.0)

Severe

(0.0-2.0)

Millewa SF Murray River

Past fire

Moira SF

Barmah Forest

10 km

Figure 5 Stand condition of forests and woodlands in 2012 predicted by the model from remotely-sensed data for Barmah-Millewa. See Table 3 for an explanation of the condition classes. Waterbodies are indicated in blue.


Good

Koondrook SF Murray River

Gunbower Island

(8.1-10.0)

Moderate

(6.1-8.0)

Poor

(4.1-6.0)

Degraded

(2.1-4.0)

Severe

(0.0-2.0)

Perricoota SF

Past fire

Gunbower Creek 10 km Figure 6 Stand condition of forests and woodlands in 2012 predicted by the model from remotely-sensed data for GunbowerKoondrook-Perricoota. See Table 3 for an explanation of the condition classes. Waterbodies are indicated in blue.


Chalka Creek North Good

(8.1-10.0)

Moderate

(6.1-8.0)

Poor

(4.1-6.0)

Degraded

(2.1-4.0)

Severe

(0.0-2.0)

Murray River

Hattah Lakes

5 km

Chalka Creek South

Figure 7 Stand condition of forests and woodlands in 2012 predicted by the model from remotely-sensed data for Hattah Lakes. See Table 3 for an explanation of the condition classes. Waterbodies are indicated in blue.


Good Chowilla Floodplain

(8.1-10.0)

Moderate

(6.1-8.0)

Poor

(4.1-6.0)

Degraded

(2.1-4.0)

Severe

(0.0-2.0)

Lindsay Island

Lindsay River

Murray River

Mulcra Island

Wallpolla Island

Wallpolla Creek 5 km

Figure 8 Stand condition of forests and woodlands in 2012 predicted by the model from remotely-sensed data for the Chowilla floodplain and Lindsay, Mulcra and Wallpolla Islands. See Table 3 for an explanation of the condition classes. Waterbodies are indicated in blue.


Table 8 Proportion of forested areas (%) in different condition classes on the Murray River floodplain in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’. The proportions predicted by the 2010 Stand Condition Model (informed by Landsat imagery) are provided for comparison. Proportion of forest area (%)

Condition class

good

(8.1-10.0)

moderate

(6.1-8.0)

poor

(4.1-6.0)

degraded (2.1-4.0) severely degraded (0.0-2.0)

Landsat 2010

Rapideye 2012

21.0

27.1

(17.3-25.3)

(19.2-32.5)

42.7

36.7

(40.4-44.0)

(35.9-40.4)

24.9

25.2

(21.3-28.4)

(22.1-27.7)

7.6

10.8

(6.1-9.2)

(9.4-11.7)

3.9

0.3

(3.2-4.7)

(0.1-1.0)


Table 9 Proportional area (%) in different condition classes within the forest types across the Murray River floodplain in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.

STRESSED CONDITION Forest Type River red gum forest

River red gum woodland

RRG – black box woodland Black box woodland

RRG – box woodland

Box woodland

good

moderate

poor

degraded

severe

40.9

46.0

11.1

1.7

0.2

(26.8-50.9)

(38.5-56.5)

(8.9-14.5)

(1.6-1.8)

(0.0-0.5)

27.6

37.4

27.5

7.2

0.3

(23.2-30.6)

(36.7-40.2)

(22.7-31.5)

(6.4-7.6)

(0.1-1.0)

11.2

25.6

41.5

21.5

0.2

(10.1-11.7)

(21.4-33.7)

(36.5-43.6)

(18.1-23.9)

(0.0-0.9)

4.8

20.7

46.8

27.5

0.1

(3.9-5.3)

(17.7-27.7)

(43.6-46.8)

(23.4-31.0)

(0.0-0.8)

21.3

63.9

12.7

0.3

1.9

(9.3-28.1)

(61.0-70.1)

(9.0-18.2)

(0.1-0.6)

(1.8-1.9)

14.4

75.5

9.8

0.2

0.0

(5.9-21.0)

(72.3-77.9)

(6.6-15.8)

(0.1-0.4)

(0.0-0.0)


Table 10 Proportion of forested areas (%) in good condition within the forest types across the Murray River floodplain in 2010 and 2012, predicted by the associated Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes.

River red gum forest


RRG – black box woodland

Black box woodland


Box woodland

Landsat 2010

Rapideye 2012

25.4

40.9

(20.9-30.7)

(26.8-50.9)

25.6

27.6

(21.8-30.1)

(23.2-30.6)

22.7

11.2

(19.3-26.5)

(10.1-11.7)

11.4

4.8

(9.0-14.4)

(3.9-5.3)

7.5

21.3

(5.1-10.8)

(9.3-28.1)

9.1

14.4

(6.6-12.8)

(5.9-21.0)


Table 11 Proportion of forested areas (%) in different condition classes within the Icon Sites and their component forests in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.

STRESSED CONDITION good

moderate

poor

degraded

severe

Murray channel

31.1 (25.2-34.1)

27.2 (27.2-29.6)

29.8 (26.0-31.8)

11.5 (10.2-12.3)

0.4 (0.0-1.2)

Barmah – Millewa

42.0 (22.0-58.1)

49.5 (34.9-66.0)

7.6 (6.2-10.7)

0.8 (0.7-1.0)

0.1 (0.0-0.2)

Barmah

52.4 (28.8-69.5)

42.7 (26.5-64.0)

4.6 (3.7-6.7)

0.3 (0.3-0.4)

0.0 (0.0-0.1)

Millewa

33.0 (16.2-48.2)

55.3 (42.2-67.6)

10.2 (8.4-14.2)

1.3 (1.2-1.6)

0.4 (0.0-0.4)

22.2 (13.3-26.9)

65.9 (64.8-69.3)

11.6 (8.0-17.0)

0.3 (0.3-0.4)

0.1 (0.0-0.1)

Gunbower

34.4 (23.4-40.0)

57.0 (54.0-64.3)

8.1 (5.6-11.7)

0.5 (0.4-0.5)

0.1 (0.0-0.1)

Koondrook-Perricoota

14.4 (6.8-18.6)

71.5 (71.5-72.5)

13.8 (9.5-20.4)

0.2 (0.2-0.3)

0.1 (0.0-0.1)

1.5 (1.2-1.7)

15.6 (11.7-22.0)

52.1 (49.5-52.1)

30.8 (24.2-37.2)

0.1 (0.0-0.4)

13.3 (12.5-13.7)

16.3 (13.8-21.3)

30.7 (29.6-30.6)

39.6 (35.4-41.5)

0.2 (0.0-1.6)

13.6 (12.6-14.2)

17.7 (15.1-23.2)

30.0 (28.3-30.2)

38.5 (34.4-40.4)

0.2 (0.0-1.6)

9.0 (8.7-9.2)

11.3 (9.2-15.1)

31.7 (30.4-32.9)

47.9 (42.9-50.2)

0.1 (0.0-1.5)

G-K-P

Hattah Lakes

C-L-M-W Lindsay-Mulcra-Wallpolla

Chowilla


Table 12 Proportion of forested areas (%) in good condition within the Icon Sites and their component forests in 2010 and 2012, predicted by the associated Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes.

Landsat 2010

Rapideye 2012

Murray channel

27.9 (23.6-32.7)

31.1 (25.2-34.1)

Barmah – Millewa

18.5 (13.7-23.7)

42.0 (22.0-58.1)

Barmah

24.6 (18.7-32.0)

52.4 (28.8-69.5)

Millewa

13.2 (9.4-18.4)

33.0 (16.2-48.2)

10.6 (8.5-13.1)

22.2 (13.3-26.9)

19.5 (15.9-23.9)

34.4 (23.4-40.0)

4.8 (3.7-6.3)

14.4 (6.8-18.6)

4.7 (3.7-6.1)

1.5 (1.2-1.7)

25.0 (21.9-28.4)

13.3 (12.5-13.7)

Lindsay-Mulcra-Wallpolla

26.0 (22.8-29.6)

13.6 (12.6-14.2)

Chowilla

16.9 (14.6-19.5)

9.0 (8.7-9.2)

G-K-P Gunbower Koondrook-Perricoota

Hattah Lakes

C-L-M-W


Discussion

There was not a consistent change in stand condition measured at the references sites across the Murray River floodplain between early 2010 and early 2012 (Figure 2). Few individual sites (11%) showed increases in stand condition after the two wet years of 2010 and 2011. This was an encouraging response in comparison to the overall decline in stand condition observed between 2009 and 2010 (Cunningham et al., 2011). The extent of good condition stands increased by 6% across the whole floodplain, which suggests the condition of forests and woodlands improved in the relatively small areas that were flooded. Equally important, the extent of poor to severely degraded stands remained at 36% between 2010 and 2012. The stabilisation of these stressed classes indicates the above average rainfall over this period was sufficient to mitigate further decreases in condition in unflooded areas.

A comparison of the ground surveys of stand condition at the reference sites in 2010 and 2012 showed there was not an overall increase in condition following the recent floods and above average rainfall (Figure 2d). The limited improvement in stand condition following the floods of 2010 and 2011 is understandable given the length of the proceeding drought and the lack of extensive flooding in the lower Murray River. There may be a delayed response of trees to the recent wet conditions, with crowns expanding over the coming years. Poorer condition stands will take many years of adequate water availability for extant trees to attain full crown extent and longer to increase the number of live trees. Several stands had substantial increases in crown extent in 2012, which is an encouraging response.

Stand condition of forests and woodlands across The Living Murray Icon Sites in 2012 was predicted successfully (R2 = 0.71) using a combination of quantitative ground surveys, Rapideye satellite imagery, satellite-derived structural data and a predictive clustering tree. Modelling stand condition from Rapideye imagery in 2012 and derived structural variables was more successful than using Landsat imagery in Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 40

2009 and 2010 (R2 = 0.68 and 0.59 respectively). In addition to the 2009 and 2010 Stand Condition Models being based on different imagery (Landsat), they were not modelled using predictive clustering trees and, of course, used data acquired in a different year. The 2009 and 2010 ground surveys will be remodelled using the Rapideye imagery and both modelling approaches to provide a direct comparison in a subsequent report. Here, it was found that the predictive clustering tree predicted stand condition better than the MLP neural network (Table 4). This is likely to be because a predictive clustering tree uses an ensemble of multiple models to obtain better predictive power whereas a MLP neural network finds a single model of best fit.

The predictive clustering tree under-predicted the condition of good condition stands and over-predicted the condition of degraded stands (Figure 3). This is a result of the majority of the reference sites (134 of the 175 reference sites) having a stand condition score in 2012 of between 4 and 8 (poor to moderate condition). Therefore, a model will tend to fit the data better at these intermediate values and poorly at extreme values. This issue was addressed by transforming the predictions of the chosen 2012 Stand Condition Model towards equality (the line where observations equal predictions). This problem will be addressed in the Stand Condition Tool by the same transformation approach based on the relationship between surveyed and predicted condition from the current year.

The 2012 Stand Condition model was informed mainly by historical Landsat-derived NDVI, Rapideye reflectance in the red and red edge spectra, tree density and the probability of river red gum forest (Table 7). Photosynthetically-active leaves are known to strongly absorb the red spectrum and reflect the near infrared spectrum. The index NDVI uses this relationship to distinguish vegetation. Therefore, it is not surprising that the 2012 Stand Condition model relies on NDVI, the red spectrum and the red edge (near infrared) spectrum to predict stand condition (the amount of canopy at a stand). The Rapideye spectra would provide a current estimate of leaf Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 41

area and the historical Landsat-based NDVI provides a past estimate of leaf area, which would help calibrate the estimation of current leaf area. It is possible that this combination of variables (leaf area estimated during a drought and after a flood) overcome the potential problem of overestimating stand condition when the understorey remains green in the summer during a wet year.

A strong relationship between stand condition and tree density was not surprising because tree density is directly related to recent mortality and also helps to calibrate the model solution across structural types (i.e. forest and woodland). The probability of river red gum forest is important in distinguishing between river red gumdominated and black box-dominated forest types, and consequently mediating the condition response within the disparate forest types.

The independent survey of river red gum and black box stands at Gunbower Island and Chowilla Floodplain in early 2012 was predicted well (R2 = 0.83, Figure 4) by the 2012 Stand Condition Model. Similarly, a separate survey of condition at Barmah Forest in 2010 was predicted well (R2 = 0.88, Cunningham et al., 2011) by the 2010 Stand Condition Model. This provides us with a high level of confidence in the Stand Condition Models that will be used to build the Stand Condition Tool to provide accurate predictions of stand condition across the Murray River floodplain.

The 2012 Stand Condition Model predicted extensive areas of stressed stands across The Living Murray Icon Sites, with 73% of the area covered by river red gum, black box and box communities being in a stressed condition in 2012 (Table 8). The amount of stressed stands is less than predicted for 2010 (Cunningham et al., 2011), suggesting that the recent flooding and above average rainfall has increased the extent of good condition stands across the floodplain. With further flooding in these areas over the coming years, it would be possible to maintain this increased area of good condition stands. The extent of poor to severely degraded sites remained stable at 36% between 2010 and 2012 suggesting that the above average rainfall over this Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 42

period was sufficient to mitigate further decreases in stand condition in areas that were not flooded. However, if drought conditions return in the coming years, it is likely that the extent of stressed condition stands will continued to expand without additional water.

River red gum forests were predicted to contain the largest extent (41%) of good condition stands among the forest types found across The Living Murray Icon Sites (Table 9). The better overall condition of river red gum forests than other forest types is a result of their dominance in areas of high flooding frequency (fringing rivers, creeks and wetlands) and higher rainfall regions. During the prolonged dry period between 1997 and 2009 (‘The Big Dry’), many of the riparian river red gum communities would have had access to comparatively more water than other parts of the floodplain, due to either their association with permanent waterways or through additional environmental watering. Again due to the position of river red gum forests in the landscape, they would have received the recent floods earlier and probably experienced longer duration floods than the other forest types. Pure black box woodlands were predicted to be in the worst condition in 2012, with 74% of their range containing poor to severely degraded stands. It is likely that the majority of these black box woodlands were not flooded between 2010 and 2012. Although these communities are adapted to longer intervals between floods (10-50 yr century-1) than river red gum communities (at least 45 yr century-1 Roberts, 2004), current intervals between floods appear to be exceeding their tolerances.

Stand condition was predicted to differ widely among and within the Icon Sites in 2012 (Table 10). Barmah-Millewa Forest had the smallest extent of stressed stands (58%) while Hattah Lakes had the largest extent (98.5%). This reflects the downstream decline in the stand condition along the Murray River, which has been observed previously (Margules & Partners, 1990; Cunningham et al., 2009b). Stressed stands were pervasive (> 85%) across the floodplains of Hattah Lakes, LindsayMulcra-Wallpolla Islands and Chowilla floodplain, where the majority of trees are Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 43

growing away from permanent water and floods are now rare events. The New South Wales portions of Barmah-Millewa Forest and Gunbower-KoondrookPerricoota Forests had a larger extent of stressed stands than the Victorian side of the floodplain. This is probably due to the higher elevation of the floodplain on the New South Wales side resulting in reduced flooding frequencies and durations, particularly in recent decades but may also reflect differences in land management.

Good condition stands have largely contracted to areas fringing the river channel, and creeks and wetlands that regularly contain water. Many of these patches of good condition stands correspond to areas that have received regular environmental watering, including the plains of western Barmah forest, the lagoons of northern Gunbower Forest, the Hattah Lakes, and other anabranch creeks across the Lower Murray River floodplain (Figures 5 to 8). It is also in these low lying areas of Barmah, Millewa, Gunbower, Koondrook and Perricoota Forests that good condition stands have expanded from following the recent floods.

Climate change is predicted to further reduce water availability by raising temperatures (1-1.5oC and consequently increasing evaporative demand) and decreasing annual rainfall (2-5%) in southeastern Australia by 2030 (CSIRO & Australian Bureau of Meteorology, 2007). Therefore, the extent of good condition stands may retract in the near future as flooding frequencies and rainfall are further reduced by climate change. The recent flooding was associated with a substantial increase in the area (25% to 41%) of river red gum forest in good condition. This suggests that forest dieback across the Murray River floodplain may be resolved by returning regular water to these systems. The continuing decrease in condition of black box woodlands over the same period suggests that regular flooding has to be returned to these communities soon if they are to be maintained on the floodplain. If floods remain rare events, the condition of forests and woodlands along the Murray River floodplain is likely to decrease further in the coming years.


Conclusions A model was successfully built (R2 = 0.71) that predicted stand condition of forests and woodlands across The Living Murray Icon Sites in 2012. This demonstrates that the stand condition modelling approach works equally successfully after floods as it did during the drought. The 2012 Stand Condition Model was very successful (R2 = 0.83) in predicting the condition of stands from the independent validation survey. These results provide us with confidence that the Stand Condition Tool will be able to predict stand condition under a range of environmental conditions and outside the 175 sites used to build it.

The 2012 Stand Condition Model, using Rapideye imagery and derived structural variables as inputs, predicted in 2012 that: •

73% of the area covered by river red gum, black box and box communities in The Living Murray Icon Sites was in a stressed condition (moderate to severely degraded condition). This is a minor decrease from the prediction of 79% from the 2010 Stand Condition Model.

•

41% of river red gum forests were in good condition.

•

River red gum – black box woodlands and black box woodlands were predominantly (63% and 74% respectively) in poor to severely degraded condition.

•

Barmah-Millewa Forest had the largest extent of good condition stands (42%).

•

Chowilla Floodplain and Hattah Lakes had the highest extents (80% and 83% respectively) of poor to severely degraded stands.

•

Between early 2010 and early 2012, the extent of good condition stands increased in the forests and woodlands of upper Murray River while they decreased in the lower Murray River.

The predictions of the 2012 Stand Condition Model suggest that: •

flooding and above average rainfall during 2010 and 2011 lead to a small improvement in stand condition of forests and woodlands. Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 45

•

above average rainfall mitigated further increases in the extent of poor to severely degraded stands.

•

forests and woodlands in good condition remain restricted to the limited areas that received adequate water due to either river regulation and managed or unmanaged floods.

Acknowledgements This project was funded by the Murray-Darling Basin Authority as part of The Living Murray program. We appreciate all the continuing support and discussions from the Environmental monitoring Team at the MDBA (Greg Raisin, Stuart Little, David Hohnberg and Anne Stensletten). We gratefully appreciate the efforts of the people in the field during the long and often hot days of the survey. Those who come to mind, in order of their affiliations, are Kate Bennetts (Fire, Flood and Flora), Scott MacDonald, Brianna Hay (Monash University) Mark Henderson, Bernard McCarthy, David Wood (Murray-Darling Freshwater Research Centre), Danielle McAllister (Office of Environment and Heritage, NSW) and Erin Lenon (South Australian Murray-Darling Basin Natural Resources Management Board). Without the quality data you collected, we would not have an accurate Stand Condition Model. We also appreciate the continued support of the wider group involved with the project, including Linda Broekman, Denise Lalor (Forests NSW), Keith Ward (GoulburnBroken CMA), Peter Kelly, Louise Searle (Mallee CMA), Kathryn Stanislawski, Melanie Tranter (North Central CMA), Mark Schultz (South Australian MurrayDarling Basin Natural Resources Management Board), Paul O'Connor and Julian Raudino (Victorian Department of Sustainability and Environment ). AAM Group, particularly Ken Gillan, for supplying the Rapideye mosaic across the Murray River floodplain for 2012. Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 46

References CSIRO & Australian Bureau of Meteorology (2007) Climate change in Australia. CSIRO, Melbourne. Cunningham, S.C., Read, J., Baker, P.J. & Mac Nally, R. (2007) Quantitative assessment of stand condition and its relationship to physiological stress in stands of Eucalyptus camaldulensis (Myrtaceae) in southeastern Australia. Australian Journal of Botany, 55, 692699. Cunningham, S.C., Mac Nally, R., Griffioen, P. & White, M. (2009a) Mapping the Condition of River Red Gum (Eucalyptus camaldulensis Dehnh.) and Black Box (Eucalyptus largiflorens F.Muell.) Stands in The Living Murray Icon Sites. A Milestone Report to the Murray-Darling Basin Authority as part of Contract MD1114. Murray-Darling Basin Authority, Canberra. Cunningham, S.C., Griffioen, P., White, M. & Mac Nally, R. (2011) Mapping the Condition of River Red Gum (Eucalyptus camaldulensis Dehnh.) and Black Box (Eucalyptus largiflorens F.Muell.) Stands in The Living Murray Icon Sites. Stand Condition Report 2010. Murray-Darling Basin Authority, Canberra. Cunningham, S.C., Mac Nally, R., Read, J., Baker, P.J., White, M., Thomson, J.R. & Griffioen, P. (2009b) A robust technique for mapping vegetation condition across a major river system. Ecosystems, 12, 207-219. Friedman, J.H. (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232. Kocev, D., Vens, C., Struyf, J. & Džeroski, S. (2007) Ensembles of multi-objective decision trees. Machine Learning: ECML 2007. Proceedings of the 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 (ed. by J. Kok, J. Koronacki, R. De Mántaras, S. Matwin, D. Mladenić and A. Skowron), pp. 624-631. Springer, Berlin. Margules & Partners (1990) Riparian Vegetation of the River Murray. Report prepared by Margules and Partners Pty. Ltd., P. & J. Smith Ecological Consultants and Department of Conservation Forests and Lands. Murray-Darling Basin Commission, Canberra. MDBC (2002) The Living Murray: a Discussion Paper on Restoring the Health of the River Murray. In, p. 94. Murray-Darling Basin Commission, Canberra. MDBC (2005a) The Living Murray Foundation Report on the significant ecological assets targeted in the First Step Decision. Murray-Darling Basin Commission, Canberra. MDBC (2005b) Survey of River Red Gum and Black Box Health Along the River Murray in New South Wales, Victoria and South Australia - 2004. In, p. 24. Murray-Darling Basin Commission, Canberra. Mapping stand condition of The Living Murray Icon Sites in 2012 with Rapideye 47

Özesmi, S.L., Tan, C.O. & Özesmi, U. (2006) Methodological issues in building, training and testing artificial neural networks in ecological applications. Ecological Modelling, 195, 8393. Roberts, J. (2004) Floodplain Forests and Woodlands in the southern Murray-Darling Basin. In. Report JR 03/2004, Canberra. Rokach, L. & Maimon, O. (2008) Data mining with decision trees: theory and applications. . World Scientific Publishing, Singapore. Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986) Learning representations by backpropagating errors. Nature, 323, 533–536. Spielgelhalter, D., Thomas, A. & Best, N. (2003) WinBUGS version 1.4. Bayesian inference using Gibbs sampling. MRC Biostatistics Unit, Institute for Public Health, Cambridge, United Kingdom. ter Steege, H. (1996) WINPHOT 5.0: a programme to analyze vegetation indices, light and light quality from hemispherical photographs. In. Tropenbos Guyana Programme, Report 95-2., Tropenbos, Guyana.


Appendix – Proportional area in different condition classes within each forest types for The Living Murray Icon Sites and their subregions

Table A1 River Murray Channel - Proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




good

moderate

poor

degraded

severe

52.3

32.0

12.6

2.8

0.3

(40.4-58.3)

(28.8-41.1)

(10.3-15.1)

(2.6-2.8)

(0.0-0.7)

35.3

28.4

27.9

7.9

0.5

(31.1-37.6)

(28.4-31.4)

(23.7-30.8)

(7.3-8.1)

(0.0-1.4)

16.5

31.4

41.0

11.0

0.2

(14.9-17.3)

(26.7-40.4)

(33.1-45.2)

(9.2-12.4)

(0.0-0.7)

5.4

21.0

50.5

22.9

0.2

(4.6-5.9)

(17.5-29.1)

(45.5-51.1)

(19.4-25.9)

(0.0-0.9)


Table A2 Barmah-Millewa Forest Icon Site - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.



RRG –box woodland

Box woodland

good

moderate

poor

degraded

severe

45.6

47.1

6.5

0.8

0.1

(23.1-63.2)

(30.6-66.6)

(5.6-9.0)

(0.7-1.0)

(0.0-0.2)

26.1

62.0

11.5

0.3

0.0

(16.4-34.9)

(57.1-66.5)

(7.8-16.7)

(0.2-0.5)

(0.0-0.0)

28.8

65.0

6.1

0.1

0.0

(11.3-45.0)

(50.6-78.2)

(4.3-10.2)

(0.1-0.2)

(0.0-0.0)

20.5

70.5

8.9

0.2

0.0

(7.3-35.3)

(58.2-74.5)

(6.4-17.9)

(0.1-0.3)

(0.0-0.0)


Table A3 Barmah Forest – proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.



good

moderate

poor

degraded

severe

55.3

41.0

3.5

0.1

0.0

(28.8-74.0)

(22.8-65.8)

(3.0-5.1)

(0.1-0.2)

(0.0-0.0)

38.1

53.7

7.9

0.3

0.0

(26.6-48.0)

(46.8-60.6)

(5.0-12.3)

(0.2-0.4)

(0.0-0.0)


Table A4 Millewa Forest - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




Box woodland

good

moderate

poor

degraded

severe

37.1

52.3

9.1

1.3

0.1

(18.2-53.7)

(37.3-67.3)

(7.8-12.5)

(1.2-1.7)

(0.0-0.4)

13.6

70.7

15.3

0.4

0.0

(5.6-21.2)

(67.7-72.5)

(10.9-21.3)

(0.2-0.6)

(0.0-0.0)

28.8

65.0

6.1

0.1

0.0

(11.3-45.0)

(50.6-78.2)

(4.3-10.2)

(0.1-0.2)

(0.0-0.0)

20.5

70.4

8.9

0.2

0.0

(7.3-35.3)

(58.2-74.5)

(6.4-17.9)

(0.1-0.3)

(0.0-0.0)


Table A5 Gunbower-Koondrook-Perricoota Icon Site - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.





Box woodland

good

moderate

poor

degraded

severe

25.0

65.0

9.8

0.2

0.0

(14.4-30.7)

(62.1-7-.5)

(7.0-14.8)

(0.1-0.2)

(0.0-0.0)

12.3

66.5

20.8

0.4

0.0

(8.9-14.5)

(60.4-71.7)

(13.6-30.1)

(0.2-0.5)

(0.0-0.0)

77.1

2.1

14.6

6.3

0.0

(60.4-77.1)

(2.1-18.8)

(10.4-18.8)

(2.1-10.4)

(0.0-0.0)

13,3

77.1

9.0

0.5

0.0

(7.2-16.5)

(60.4-77.1)

(5.5-12.9)

(0.5-0.6)

(0.0-0.0)

16.9

66.6

16.1

0.4

0.0

(7.2-20.2)

(66.6-69.6)

(11.4-22.5)

(0.2-0.7)

(0.0-0.1)

11.6

79.2

9.1

0.1

0.0

(5.1-14.4)

(79.2-81.0)

(5.7-13.6)

(0.0-0.2)

(0.0-0.0)


Table A6 Gunbower Forest - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.



Black box woodland

good

moderate

poor

degraded

severe

37.5

55.6

6.8

0.2

0.0

(24.6-43.8)

(51.2-64.4)

(4.9-10.7)

(0.1-0.3)

(0.0-0.0)

34.4

51.0

13.6

0.9

0.1

(27.2-38.9)

(50.9-53.7)

(9.6-17.9)

(0.6-1.1)

(0.0-0.1)

13.4

76.8

9.2

0.6

0.0

(7.2-16.5)

(77.3-79.0)

(5.7-13.1)

(0.5-0.7)

(0.0-0.0)


Table A7 Koondrook-Perricoota Forests - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




Box woodland

good

moderate

poor

degraded

severe

17.4

70.9

11.5

0.2

0.0

(8.0-22.7)

(68.9-74.3)

(8.3-17.3)

(0.1-0.2)

(0.0-0.1)

3.7

72.6

23.5

0.1

0.0

(1.8-5.1)

(63.2-79.8)

(15.1-34.7)

(0.1-0.3)

(0.0-0.0)

16.9

66.6

16.1

0.4

0.0

(7.2-20.2)

(66.6-69.6)

(11.4-22.5)

(0.2-0.7)

(0.0-0.1)

11.6

79.2

9.1

0.1

0.0

(5.1-14.4)

(79.2-81.0)

(5.7-13.6)

(0.0-0.2)

(0.0-0.0)


Table A8 Hattah Lakes Icon Site - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




good

moderate

poor

degraded

severe

19.4

33.3

39.7

7.4

0.1

(17.6-20.1)

(26.7-42.0)

(31.9-47.1)

(6.1-8.1)

(0.0-0.6)

1.9

24.7

57.1

16.4

0.0

(1.4-2.1)

(17.9-34.6)

(50.6-59.8)

(12.6-20.8)

(0.0-0.1)

0.8

19.6

49.4

30.0

0.1

(0.6-1.0)

(15.2-26.5)

(48.3-49.4)

(23.9-35.2)

(0.0-0.7)

0.7

10.8

52.7

35.8

0.0

(0.5-0.8)

(7.9-15.9)

(47.6-55.3)

(27.9-43.7)

(0.0-0.4)


Table A9 Chowilla Floodplain and Lindsay–Mulcra–Wallpolla Islands Icon Site proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




good

moderate

poor

degraded

severe

49.5

21.3

20.7

8.3

0.2

(48.4-50.1)

(19.3-24.8)

(17.4-23.3)

(7.7-8.3)

(0.0-1.0)

23.5

25.8

34.1

16.4

0.1

(22.2-24.4)

(22.4-32.0)

(29.4-36.9)

(14.3-17.6)

(0.0-1.1)

5.0

13.5

35.9

45.5

0.2

(4.5-5.2)

(10.8-19.3)

(34.6-35.9)

(39.8-48.1)

(0.0-1.9)

3.6

12.4

30.5

53.3

0.2

(3.1-3.8)

(10.0-17.5)

(29.2-30.9)

(47.8-56.2)

(0.0-1.5)


Table A10 Lindsay–Mulcra–Wallpolla Islands - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




good

moderate

poor

degraded

severe

57.1

20.6

18.1

4.0

0.2

(55.7-57.8)

(19.1-24.2)

(14.4-20.6)

(3.5-4.0)

(0.0-0.8)

23.8

28,8

33.7

13.6

0.1

(22.1-24.8)

(25.2-35.8)

(27.9-37.0)

(11.5-15.0)

(0.0-0.8)

3.6

10.8

35.9

49.5

0.2

(3.1-3.9)

(8.6-17.0)

(33.3-36.8)

(42.3-53.4)

(0.0-1.6)

4.0

15.2

30.8

49.9

0.2

(3.3-4.3)

(12.4-21.0)

(30.2-30.8)

(44.7-52.8)

(0.0-1.3)


Table A11 Chowilla Floodplain - proportional area (%) in different condition classes within the forest types in 2012, predicted by the 2012 Stand Condition Model. Values are means with 97.5% and 2.5% credible intervals given in brackets. See Table 3 for explanation of the condition classes. Note that all classes below good condition are considered to be in ‘stressed condition’.




good

moderate

poor

degraded

severe

40.5

21.6

26.3

11.5

0.1

(39.8-41.0)

(18.9-25.4)

(23.0-29.1)

(10.6-11.5)

(0.0-0.9)

17.7

18.9

37.9

25.4

0.0

(17.1-18.0)

(15.6-23.8)

(35.8-39.7)

(22.4-26.4)

(0.0-1.2)

6.6

17.1

36.5

39.8

0.1

(6.2-6.8)

(13.9-22.6)

(34.9-36.8)

(35.7-41.0)

(0.0-2.1)

1.3

5.1

29.8

63.8

0.1

(1.2-1.3)

(3.9-8.1)

(26.0-33.9)

(56.7-67.6)

(0.0-1.3)


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