Mesa A Annual Sand Sheet Vegetation Monitoring ......Report Reference:...

Report Reference: 14287‐17‐BISR‐1Rev0_171212

Mesa A Annual Sand Sheet Vegetation Monitoring September 2017

Prepared for Rio Tinto

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Author(s)

A. Gove

J. Kelcey

S. Stapleto

A. Gove

J. Kelcey

S. Stapleto

A. Gove

J. Kelcey

S. Stapleto

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D. O’Grady

J. Johnston

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M. Stalker

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Rio Tinto Mesa A Annual Sand Sheet Vegetation Monitoring, September 2017

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Abbreviations

Abbreviation Definition

Atp Acacia tumida var. pilbarensis

Biota Biota Environmental Sciences

CF chlorophyll florescence

Fm Maximum fluorescence

Fv Variable florescence

GDA94 Geocentric Datum of Australia 1994

GPS Global Positioning System

ICF Index of chlorophyll florescence

MGA94 Map Grid of Australia 1994

MS 756 Ministerial Statement 756

MSAVI Modified Soil Adjusted Vegetation Index

NDVI Normalised Difference Vegetation Index

NIR Near Infrared

PEC1 Priority Ecological Community 1

PEC2 Priority Ecological Community 2

SAVI Soil Adjusted Vegetation Index

Rio Tinto Rio Tinto Iron Ore

sp. Species (singular)

spp. Species (plural)

Tz Triodia schinzii


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ExecutiveSummary

Rio Tinto Iron Ore owns the Mesa A/Warramboo Iron Ore project, located approximately 43 km west of Pannawonica in the Pilbara region of Western Australia. The project was assessed and approved by the Environmental Protection Authority (Bulletin 1264) and approved under the Ministerial Statement 756 (MS 756) on 21 November 2007. Mining at Mesa A commenced in February 2010.

Condition 7 of MS 756 specifies the protection of the Sand Sheet Vegetation Community. The ‘Sand Sheet vegetation (Robe Valley)’ Priority 3 three Priority Ecological Community was identified on the south‐eastern edge of the Mesa A Iron Ore Project during a botanical survey conducted by Biota Environmental Sciences. The associated vegetation type for the Sand Sheet Vegetation Community is described as ‘Corymbia zygophylla scattered low trees over Acacia tumida var. pilbarensis, Grevillea eriostachya tall shrubland over Triodia schinzii hummock grassland’.

Astron was commissioned to undertake the tenth annual Sand Sheet Vegetation Community monitoring survey in September 2017. The monitoring is designed to assess the effects of mining operations from the Mesa A Iron Ore project on the Sand Sheet Vegetation Community from 2008 to 2017. Eight consecutive monitoring surveys had previously been conducted by Biota Environmental Sciences from 2009 to 2016.

Statistical analysis of on‐ground monitoring data indicated that vegetation cover in the Sand Sheet Vegetation Community has significantly declined over the period of monitoring (2008 to 2017), while the mean number of species has increased. There was no clear indication that declines in cover were associated with mining activities, which was primarily assessed in terms of proximity to the mine footprint. Variation in rainfall 12 months prior to each survey did not explain declines in vegetation cover. Remotely sensed imagery comparing 2012, 2016 and 2017 indicated that the Sand Sheet Community Vegetation condition was relatively stable over these monitoring periods. Mapped change in Normalised Difference Vegetation Index and Modified Soil Adjusted Vegetation Index indices over the same period indicated some areas of decline and other areas of improvement; these changes have occurred in areas not necessarily covered by a monitoring quadrat. The main area of interest was a decline along the north‐western edge of the Sand Sheet Vegetation Community in the period 2016 to 2017. NDVI and MSAVI values for each quadrat were associated with dust levels quantified on ground, but dust levels were not directly linked to plant condition measures quantified on ground. While areas of vegetation dieback were noted on ground, particularly Acacia tumida var. pilbarensis and Triodia schinzii, changes highlighted by remote sensing were mostly associated with on ground changes in ephemeral vegetation associated with surface water.

Vegetation condition did not generally differ between the Sand Sheet Vegetation Community and the reference areas, which were established in 2017. The exception was dust scores which tended to be rated ‘medium’ in the Sand Sheet Vegetation Community and ‘low‐none’ at the reference areas.

While rainfall may not be directly linked to declines in vegetation cover, changes in soil moisture and altered surface water flow may be a driver of vegetation condition and worthy of further investigation. While dust loads were not directly associated with declines in vegetation condition, proximity to the mine is an approximate measure of exposure to mine activities such as dust production. There was no clear evidence that dust loads were affecting plant health within the period of monitoring. Spatially refining sources of dust may be helpful in testing for impacts to the Sand Sheet Vegetation Community, particularly given that there has been no long‐term monitoring of an appropriate reference site.

Comparison of on ground and remote sensing methods indicated that the two methods were providing complimentary rather than mutually redundant information. On ground monitoring was


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useful for quantifying measures such as vegetation complexity and species diversity, and for characterising change which was first indicated by remote sensing. Remote sensing provided a landscape‐level perspective on vegetation change, including historical change not necessarily available from on ground data. Remote sensing detected vegetation change in small patches that often occurred between monitoring quadrats. On ground monitoring and ground‐truthing is then useful to confirm and diagnose these changes.


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TableofContents

1 Introduction ..................................................................................................................................... 1

1.1 Project Background ..................................................................................................... 1

1.2 Scope ........................................................................................................................... 2

2 Methods .......................................................................................................................................... 4

2.1 Monitoring Design ....................................................................................................... 4

2.2 Field Assessment ......................................................................................................... 4

2.2.1 Assessment of Quadrats ............................................................................................. 4

2.2.2 Targeted Conservation Significant Flora and Weeds Survey ...................................... 5

2.2.3 Dust Monitoring .......................................................................................................... 5

2.2.4 Quantitative Plant Health ........................................................................................... 5

2.2.5 Vegetation Descriptions and Mapping of Reference Areas ........................................ 5

2.2.6 Vegetation Condition .................................................................................................. 6

2.3 Data Analysis ............................................................................................................... 8

2.3.1 On Ground Plant Condition ......................................................................................... 8

2.4 Remote Sensing ........................................................................................................... 9

2.4.1 On Ground and Remote Sensing Comparisons ......................................................... 13

2.5 Limitations ................................................................................................................. 13

3 Results and Discussion ................................................................................................................... 15

3.1 Climate and Survey Timing ........................................................................................ 15

3.2 Floristics .................................................................................................................... 16

3.2.1 Overview ................................................................................................................... 16

3.2.2 Conservation Significant Flora .................................................................................. 16

3.2.3 Introduced Flora (Weeds) ......................................................................................... 16

3.3 Species Richness ........................................................................................................ 17

3.4 Vegetation Cover ....................................................................................................... 19

3.5 Dust Cover ................................................................................................................. 21

3.6 Plant Physiological Health ......................................................................................... 25

3.7 Remote Sensing ......................................................................................................... 28

3.7.1 On Ground and Remote Sensing Comparisons ......................................................... 28

3.7.2 Remotely Sensed Quantification of Vegetation Change ........................................... 31


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3.8 Vegetation Mapping .................................................................................................. 38

3.9 Vegetation Condition ................................................................................................ 41

4 Conclusions .................................................................................................................................... 43

5 References ..................................................................................................................................... 44

ListofFigures

Figure 1: Survey area location................................................................................................................. 3

Figure 2: Visual comparison of the Sand Sheet Vegetation Community and reference areas during

2012, 2014, 2016 and 2017. ......................................................................................................... 10

Figure 3: Comparison of plant spectral absorption of red and its reflectance of NIR. A corresponding

vegetation index is illustrated that exploits this relationship to produce a highly correlated

index. ............................................................................................................................................. 11

Figure 4: Illustration of the different distributions detectable through the use of cluster analysis:

clustered, systematic and random. ............................................................................................... 13

Figure 5: Illustration of the demarcation of vegetation using a threshold binary layer, and a

corresponding percentage vegetation cover estimates using contiguous quadrats. ................... 13

Figure 6: Climate data based on 12 months preceding the survey in comparison to long term trends.

...................................................................................................................................................... 15

Figure 7: Number of native species recorded in 2017 at the reference and Sand Sheet Vegetation

Community quadrats. Numbers are displayed as boxplots: the dark horizontal line indicates the

median value, the box indicates the 25 and 75 percentiles and the ‘whiskers’ indicate the

maximum and minimum values. ................................................................................................... 18

Figure 8: Number of native species recorded at Sand Sheet Vegetation Community quadrats (MSS01‐

MSS12) from 2008 to 2017. .......................................................................................................... 18

Figure 9: Vegetation cover recorded in 2017 at reference and sand sheet sites. Numbers are

displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the

25 and 75 percentiles and the ‘whiskers’ indicate the maximum and minimum values. ............ 19

Figure 10: Vegetation cover (summed across species) recorded at Sand Sheet Vegetation Community

quadrats (MSS01‐MSS12) from 2008 to 2017. ............................................................................. 20

Figure 11: Quadrat‐level foliage projected cover in terms of distance from the disturbance footprint.

...................................................................................................................................................... 20

Figure 12: Average dust scores recorded in 2017 at reference and Sand Sheet Vegetation Community

quadrats. Data are displayed as boxplots: the dark horizontal line indicates the median value,

the box indicates the 25 and 75 percentiles and the individual points indicate extreme outliers.

...................................................................................................................................................... 21


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Figure 13: Mean dust scores recorded at Sand Sheet Vegetation Community quadrats in relation to

distance from the disturbance footprint, in 2017. ....................................................................... 22

Figure 14: Monthly dust levels (g/m2) recorded at monitoring gauge from 2010 to 2017. ................. 23

Figure 15: Rate of change in vegetation cover, in terms of mean observed dust scores at each of the

Sand Sheet Vegetation Community quadrats. .............................................................................. 24

Figure 16: Mean vegetation cover in relation to dust levels (g/m2) recorded at the Sand Sheet

Vegetation Community gauge (DDGMA10) in the previous 12 months. ..................................... 24

Figure 17: Leaf chlorophyll fluorescence across reference and Sand Sheet Vegetation Community

quadrats in 2017, all species combined. Values are displayed as boxplots: the dark horizontal

line indicates the median value, the box indicates the 25 and 75 percentiles, the ‘whiskers’

indicate the expected maximum and minimum values and the individual points indicate

extreme outliers. ........................................................................................................................... 25

Figure 18: Leaf chlorophyll fluorescence, by species, in 2017 across reference and Sand Sheet

Vegetation Community quadrats. Values are displayed as boxplots: the dark horizontal line

indicates the median value, the box indicates the 25 and 75 percentiles, the ‘whiskers’ indicate

the expected maximum and minimum values and the individual points indicate extreme

outliers. ......................................................................................................................................... 26

Figure 19: Leaf chlorophyll fluorescence values of individual species within each quadrat and their

associated dust score. ................................................................................................................... 27

Figure 20: Mean leaf chlorophyll fluorescence values within each quadrat in relation to the distance

from the mine footprint. ............................................................................................................... 27

Figure 21: Relationships between vegetation cover (%), species richness and annual rainfall. Annual

rainfall was calculated as the rainfall in the 12 months prior to survey. A.t.p. = Acacia tumida

var. pilbarensis, T.z.= Triodia schinzii. Dotted lines indicate the line of best fit based on linear

regression. None of these relationships were statistically significant. ......................................... 28

Figure 22: Relationship between on ground dust scores (mean score per quadrat) and NDVI in 2017.

...................................................................................................................................................... 29

Figure 23: Relationship between on ground dust scores (mean score per quadrat) and MSAVI in

2017. ............................................................................................................................................. 30

Figure 24: Visual comparison of the NDVI layers generated for 2012, 2014, 2016 and 2017. ............. 32

Figure 25: The distribution of NDVI values between the Sand Sheet Vegetation Community and the

reference areas for 2012, 2014, 2016 and 2017. .......................................................................... 33

Figure 26: Visual comparison of the MSAVI layers generated for 2016 and 2017. .............................. 33

Figure 27: The distribution of MSAVI values between the Sand Sheet Vegetation Community and the

reference areas, for each of the two years: 2016 and 2017. ........................................................ 34


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Figure 28: Comparison of the vegetation index change layers for the NDVI periods 2012‐2014, 2014‐

2016 and 2016‐2017, and the MSAVI period 2016‐2017. ............................................................ 35

Figure 29: The distribution of NDVI and MSAVI change values between the Sand Sheet Vegetation

Community and the reference areas, for each pair of comparison years. ................................... 36

Figure 30: Comparison of the vegetation index change clustering layers for the NDVI periods 2012‐

2017 and 2016‐2017, and the MSAVI period 2016‐2017 ............................................................. 37

ListofPlates

Plate 1: Vegetation representing CzAtGeTs – facing south‐east from GPS coordinates 384819mE and

7601974mN (MGA Zone 50). ........................................................................................................ 38

Plate 2: Vegetation representing CzChAtrAa(Aa)TspP – facing south‐east from GPS coordinates

385780mE and 7600321mN (MGA Zone 50). ............................................................................... 39

Plate 3: Vegetation representing ChAtr(Sao)TspPAhPm – facing south‐east from GPS coordinates

384428mE and 7600990mN (MGA Zone 50). ............................................................................... 39

Plate 4: Vegetation representing CzAtr(Atu)Ts – facing south‐east from GPS coordinates 385097mE

and 7601462mN (MGA Zone 50). ................................................................................................. 40

Plate 5: Senescence observed in the adult Acacia tumida var. pilbarensis. ......................................... 42

Plate 6: Dieback observed in the adult Triodia schinzii. ....................................................................... 42

Plate 7: Tussock grass dominated vegetation observed at Opp02. ...................................................... 42

ListofTables

Table 1: Conditions from Ministerial Statement 756 relevant to the protection of the Sand Sheet

Vegetation Community. .................................................................................................................. 1

Table 2: Species selected at each quadrat for measurement of leaf chlorophyll florescence. .............. 7

Table 3: Data capture characteristics of the four Mesa A multispectral images, including the

radiometric bands provided to Rio Tinto. ..................................................................................... 11

Table 4: The statistical ranges and their corresponding descriptions used to reclassify areas of

positive, negative and no change in a vegetation index change layer. ........................................ 12

Table 5: Taxa most frequently recorded. .............................................................................................. 16

Table 6: Introduced flora species (weeds) recorded during the monitoring survey between 2008 to

2017 Biota (2009, 2010, 2011, 2012, 2014a, 2014b, 2015, 2016). ............................................... 17

Table 7: Statistical relationships between remote sensing vegetation indices and on‐ground

measurements of vegetation state in 2017. Significant P values (P < 0.05) are in bold. .............. 29


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Table 8: Comparison of remote sensing observations (see Section 3.7.3 for more detail) and on

ground assessment of change to vegetation cover. ..................................................................... 30

Table 9: Vegetation types recorded for the monitoring survey. .......................................................... 38

ListofAppendices

Appendix A: Sand Sheet Vegetation Community and Reference Areas, and Location of Quadrats and Dust Monitoring Equipment.

Appendix B: Vegetation Classification and Condition Scale, and Dust Deposition Scores.

Appendix C: Quadrat Data and Photographs

Appendix D: Vascular Flora Species List from 2008 to 2017, and Site by Species Matrix for 2017

Appendix E: Introduced Flora Locations

Appendix F: Vegetation Mapping

Appendix G: Vegetation Condition Mapping and Dust Mapping for 2017

Appendix H: Observations of Vegetation Condition and Health

Appendix I: Remote Sensing Images and Analysis


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1 Introduction

1.1 ProjectBackground

Rio Tinto Iron Ore (Rio Tinto) owns the Mesa A/Warramboo Iron Ore project, located approximately 43 km west of Pannawonica in the Pilbara region of Western Australia (Figure 1). The project was assessed and approved by the Environmental Protection Authority (Bulletin 1264 (Environmental Protection Authority 2007)) and approved under the Ministerial Statement 756 (MS 756) on 21 November 2007. Mining at Mesa A commenced in February 2010.

Condition 7 of MS 756 specifies the protection of the Sand Sheet Vegetation Community (Table 1). The ‘Sand Sheet vegetation (Robe Valley)’ Priority 3 Priority Ecological Community (herein referred as the Sand Sheet Vegetation Community) was identified the south‐eastern edge of the Mesa A Iron Ore Project during a botanical survey conducted by Biota Environmental Sciences (Biota) (2005, 2006). The Sand Sheet Vegetation Community has been mapped in two areas (Appendix A, Figure A.1): Priority Ecological Community 1 (PEC1), which covers an area of 146.3 ha, and Priority Ecological Community 2 (PEC2), which covers an area of 6.8 ha and is located 1.4 km to the south‐west of PEC1. The associated vegetation type for the Sand Sheet Vegetation Community is described as ‘Corymbia zygophylla scattered low trees over Acacia tumida var. pilbarensis, Grevillea eriostachya tall shrubland over Triodia schinzii hummock grassland’.

Table 1: Conditions from Ministerial Statement 756 relevant to the protection of the Sand Sheet Vegetation Community.

Condition

7 – Protection of the Sand Sheet Vegetation Community

7‐1 Prior to ground‐disturbing activity and until such time as the CEO determines, the proponent shall ensure that the Sand Sheet Vegetation Community (…) is not significantly adversely affected through either direct or indirect impacts from the implementation of the proposal.

7‐2

The proponent shall carry out a suitable program of environmental monitoring to ensure that the Sand Sheet Vegetation Community is not adversely affected by either direct or indirect impacts of the proposal.

The monitoring program shall commence prior to ground‐disturbing activity and continue until such time as the CEO determines that monitoring may be discontinued.

7‐3 In the event that monitoring referred to in condition 7‐2 detects direct or indirect impacts on the Sand Sheet Vegetation Community resulting from the proposal, the proponent shall take prompt remedial action and shall advice the CEO of the action taken as soon as practicable.

The Mesa A Mine Construction/Operation Sand Sheet Vegetation Management Plan (Rio Tinto Iron Ore 2014) was developed to address Condition 7 of MS 756. In 2008, Biota was commissioned to undertake a flora and vegetation survey of the Sand Sheet Vegetation Community. The quadrat data from this flora and vegetation survey was used as baseline data for the subsequent monitoring surveys to detect whether direct or indirect impacts from Mesa A mining operations were having an adverse impact on the Sand Sheet Vegetation Community (Biota Environmental Sciences 2009). Eight consecutive monitoring surveys have since been conducted by Biota (2009, 2010, 2011, 2012, 2014a, 2014b, 2015, 2016) from 2009 to 2016.


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1.2 Scope

Astron was commissioned to undertake the tenth annual Sand Sheet Vegetation Community monitoring survey in September 2017. The monitoring is designed to assess the effects of mining operations from the Mesa A Iron Ore project on the Sand Sheet Vegetation Community from 2008 through to 2017.

The 2017 monitoring survey included:

reassessment of 12 permanent quadrats that have been established in the Sand Sheet Vegetation Community

documenting the suite of flora species occurring in the Sand Sheet Vegetation Community

surveying the Sand Sheet Vegetation Community for the extent of conservation significant flora and weeds present

collecting qualitative evidence of potential dust impacts to the Sand Sheet Vegetation Community

documenting observations on the overall condition and health of the Sand Sheet Vegetation Community

establishment of new quadrats within the identified reference areas located to the south of the Sand Sheet Vegetation Community

baseline vegetation mapping and vegetation condition mapping of reference areas

comparison of the Sand Sheet Vegetation Community to reference areas.

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Figure 1: Survey Area LocationRio TintoMesa A Annual Sand Sheet Vegetation Monitoring

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2 Methods

2.1 MonitoringDesign

Twelve quadrats are established in the Sand Sheet Vegetation Community (MSS01‐MSS12) (Figure A.1, Appendix A). Ten quadrats (MSS01‐MSS10) were initially installed during the 2008 flora and vegetation survey within PEC1. Quadrat MSS11 was installed in PEC1 during the 2010 survey in vegetation that had been exposed to sediment‐laden water from a nearby fixed plant, and Quadrat MSS12 was installed during the 2012 monitoring survey within PEC2. Six new quadrats (MSS13‐MSS18) were installed during the 2017 monitoring survey in four reference areas identified by Rio Tinto.

Each quadrat covers an area of 2,500 m2 (50 m x 50 m, or equivalent) and is permanently marked with a fence dropper at each of the four corners. During each monitoring survey, measuring tapes are run between each of the corner posts to clearly define the boundary of the quadrat. Coordinates were taken at each four corners and measured using a handheld Global Positioning System (GPS) (Map Grid of Australia 1994 (MGA94), Geocentric Datum of Australia 1994 (GDA94)).

2.2 FieldAssessment

The monitoring survey was conducted from 12 to 18 September 2017 by Astron Environmental Scientists Samantha Stapleton and Lucy Dadour.

2.2.1 AssessmentofQuadrats

The following data was collected at each quadrat:

Vegetation description: vegetation was described according to level 5 of the National Vegetation Information System (Department of the Environment and Energy 2017) and classified according to the Aplin (1979) modification of the vegetation classification system of Specht (1970) (Table B.1, Appendix B).

Vegetation condition: assessed according to the vegetation condition classification adapted from Trudgen (1988) (Table B.2, Appendix B). Evidence of any obvious disturbance factors was also recorded.

Vegetation cover: total cover of vegetation (as projected foliar cover of green leaves) was estimated based on averaging of estimates from two observers.

Species present: all vascular plant species present. Species that could not be identified in the field were collected for later identification in the Astron Herbarium.

Foliar cover: percentage cover was estimated visually for each species.

Height: maximum height (cm) was estimated visually for each species.

Dust deposition: a broad scoring system which describes dust levels as ‘high’, ‘medium’ and ‘low’ (Table B.3, Appendix B) was assigned for vegetation within the quadrat. A finer scale assessment of dust impact using a 1 to 5 dust deposition scoring system (Table B.4, Appendix B) was also applied to each species in the quadrat.

Habitat – a broad description of the surrounding landscape based on landform, topography and soil.

Photographs – representative colour photographs were taken from the northwest corner looking diagonally into the quadrat towards the southeast corner.


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Data collected from each quadrat, along with representative photographs, are presented in Appendix C.

2.2.2 TargetedConservationSignificantFloraandWeedsSurvey

A targeted search for conservation significant flora and weeds was conducted in the Sand Sheet Vegetation Community and reference areas. The survey was targeted towards areas where these species have been found previously (and the immediate areas surrounding them).

Any conservation significant flora or weed species recorded were captured electronically using ArcPad (Esri) on a handheld device. The information recorded included population size and cover.

2.2.3 DustMonitoring

Dust deposition levels on vegetation were also mapped across the Sand Sheet Vegetation Community and reference areas using quadrat data. The broad scoring system which describes dust levels as ‘high’, ‘medium’ and ‘low’ (Table B.3, Appendix B) was used for mapping dust deposition to remain consistent with previous monitoring surveys. The broad dust score was also used to test whether dust deposition was a driver of plant physiological health (Section 2.2.4).

The finer scale assessment of dust impact (Table B.4, Appendix B) was added to the 2017 monitoring survey to test individual plant dust scores between the Sand Sheet Vegetation Community and reference areas, and in reference to proximity to the disturbance footprint. Individual plant dust scores were also used to determine if dust deposition influenced vegetation cover.

Dust load data from six dust gauges situated around the site, monitored since 2010, was provided by Rio Tinto.

2.2.4 QuantitativePlantHealth

At each quadrat, three shrub and/or tree species were selected for measurement of photosynthetic performance using a hand held chlorophyll fluorimeter (Pocket plant efficiency analyser), Hansatech Instruments, UK) to measure leaf chlorophyll florescence (CF). This measurement provides a quantitative indicator of plant physiological health(Maxwell and Johnson 2000).

Species selected for measurement of leaf CF was based on presence and dominance throughout the Sand Sheet Vegetation Community and reference areas, and on variations in leaf morphology. Three individuals from each of the three species were selected as replicate samples. Individuals were located either in or close to (within 10 m) the quadrat, and were marked and labelled with flagging tape. Species selected at each quadrat are presented in Table 2.

One leaf from each individual was measured with the hand held chlorophyll fluorimeter. Leaf CF was recorded in Fv/Fm, the ratio of variable florescence (Fv) to maximum fluorescence (Fm), which indicates the maximum quantum efficiency of Photosystem II. Measurements were taken according to standard protocols (Hansatech Instruments 2015).

2.2.5 VegetationDescriptionsandMappingofReferenceAreas

Vegetation was described and mapped for the four reference areas identified by Rio Tinto according to level 5 of the National Vegetation Information System (Department of the Environment and Energy 2017) and classified according to the Aplin (1979) modification of the vegetation classification system of Specht (1970) (Table B.1, Appendix B). Vegetation types were described and mapped using data collected at quadrats.


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2.2.6 VegetationCondition

Vegetation condition (Trudgen 1988) was mapped within the Sand Sheet Vegetation Community and reference areas using quadrat data.

Any recent tree death or significant understorey dieback observed within the Sand Sheet Vegetation Community was recorded, as was any sign of disturbances such as grazing by introduced species, fire, weeds, ground disturbance, rubbish and vehicle tracks. Observations were focused around quadrats, but observations were also recorded on an opportunistic basis.

On ground verification of vegetation changes detected by remote sensing analysis, conducted by Astron (2017a), was also undertaken during the monitoring survey. A Normalised Difference Vegetation Index (NDVI) change layer between 2014 and 2016 generated by the remote sensing analysis (Astron Environmental Services 2017a) was used to direct observations to areas where there had been a cluster of significant decline or increase in index values. Locations outside of quadrats were recorded with a GPS, a photograph was taken and any notable observations on vegetation health and condition were recorded.


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Table 2: Species selected at each quadrat for measurement of leaf chlorophyll florescence.

Species

MSS01

MSS02

MSS03

MSS04

MSS05

MSS06

MSS07

MSS08

MSS09

MSS10

MSS11

MSS12

MSS13

MSS14

MSS15

MSS16

MSS17

MSS18

Acacia ancistrocarpa X X X X

Acacia inaequilatera X

Acacia trachycarpa X X X X X X X X X X X X X X X X

Acacia tumida var. pilbarensis X X X X X X X X X X X

Corymbia hamersleyana X X X X

Corymbia zygophylla X X X X X X X

Grevillea eriostachya X X X X X X X X X X

Grevillea wickhamii X


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2.3 DataAnalysis

2.3.1 OnGroundPlantCondition

As reference quadrats were only established in 2017 (the current survey), data were analysed using two different approaches. In the first approach mean values in the Sand Sheet Vegetation Community were compared with those found in the reference areas. As this was a post‐impact only analysis, a significant difference between the two ‘treatments’ would not necessarily indicate an impact, but may indicate intrinsic differences in the vegetation types present prior to potential impact. However, this comparison does provide baseline data and will be useful in detecting significant and contrasting change in the future.

The second approach was to test for significant change in the Sand Sheet Vegetation Community quadrats over the monitoring period (2008 to 2017). Again, significant change may not necessarily indicate an impact from mining operations, and may be due to broader environmental change. However, in order to address this issue we tested whether significant changes in any variable were associated with proximity to the mine footprint, and variables for which there were data (e.g. rainfall) were tested to determine whether any changes could be explained by broader environmental change.

Most comparisons were made using linear models. If variables were non‐normal, similar non‐parametric approaches were applied. In the case of analyses testing for change over time, ‘quadrat’ was included as a variable, so that a significant time‐by‐quadrat interaction would indicate that rate of change varied amongst quadrats. This contrast in rates of change could then be considered in terms of proximity to the mining footprint.

Primary variables tested were the species richness of each quadrat and the vegetation cover. Cover was recorded in the field as the percentage of the quadrat covered by each species. Prior to analyses, cover was summed across species. As species could overlay each other, sums of cover could exceed 100%. This measure of cover therefore integrates a habitat complexity component. In contrast, foliage projected cover of each quadrat was first quantified in 2017. This variable was compared amongst Sand Sheet Vegetation Community quadrats and reference area quadrats, and in terms of proximity to the disturbance footprint. Individual plant dust scores, introduced in 2017 were analysed in a similar manner.

Rio Tinto has been monitoring dust loads at six dust gauges within and adjacent Mesa A Iron Ore Project since 2010. This included a gauge in the mine pit, and in the Sand Sheet Vegetation Community (Figure A.1, Appendix A). These data were analysed for significant change in dust loads over time, and between sites, which were expected to differ in dust loads given their varied proximity to the mine.

Plant physiological health was quantified using leaf CF, introduced in 2017. Comparisons between Sand Sheet Vegetation Community quadrats and reference area quadrats were made using all plant species combined. However, as species were not evenly represented across each site type and plant physiology may vary amongst species, species were also analysed separately. Of the eight species monitored, this meant that two were unable to be compared across site types as they only occurred in the Sand Sheet Vegetation Community quadrats.

In order to understand the drivers of plant physiological health, several tests of correlation amongst monitored variables were performed. Mean dust scores of individual quadrats were tested for correlation with the observed rate of change in vegetation cover. Dust load recorded in the previous 12 months within the Sand Sheet Vegetation Community (quantified by dust gauge) was tested for


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correlation with average vegetation cover in the Sand Sheet Vegetation Community quadrats over the years of monitoring. Leaf CF of each species selected within each quadrat was tested for correlation with its dust score.

All analyses were carried out in R statistical software (R Development Core Team 2016).

2.4 RemoteSensing

Data used within this study consisted of four capture dates: three pre‐existing captures covering 2012, 2014 and 2016, and a new capture for 2017 (Figure 2). Figure 2 provides a comparative illustration between 2012, 2014, 2016 and 2017 monitoring and reference areas. Pre‐processed data for 2012 and 2014 were supplied by Rio Tinto, while processing for the 2016 data was completed previously by Astron (Astron Environmental Services 2017b). The supplied 2017 data was pre‐processed to calibrate the data to top‐of‐atmosphere correction reflectance. The 2017 data underwent additional processing by Astron applying a dark‐offset subtraction to further reduce atmospheric effects and calibrate the data to at‐surface reflectance measurements.


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Figure 2: Visual comparison of the Sand Sheet Vegetation Community and reference areas during 2012, 2014, 2016 and 2017.


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Table 3: Data capture characteristics of the four Mesa A multispectral images, including the radiometric bands provided to Rio Tinto.

Platform Capture Date Radiometric Bands Spatial Resolution (m) ‐ Panchromatic

Spatial Resolution (m) ‐ Multispectral

GeoEye‐1 10 April, 2012 3 (Red, Green, Blue) 0.5 2

WorldView‐2 30 May, 2014 3 (Red, Green, Blue) 0.5 2

GeoEye‐1 22 May, 2016 4 (Red, Green, Blue, NIR) 0.5 2

WorldView‐3 15 Sept, 2017 4 (Red, Green, Blue, NIR) 0.5 2

Vegetation metrics were calculated through the use of remote sensing vegetation indices. Multispectral vegetation indices are developed based upon the interaction of light with the biophysical properties of vegetation. The most recognised of these biophysical relationships is that between red and the near infrared (NIR) spectral wavelengths. Chlorophyll strongly absorbs red wavelengths as part of the photosynthetic process, while the structure of healthy, turgid plant tissue strongly reflects NIR (Figure 3). Vegetation indices exploit this relationship of absorption and reflection to generate an index strongly correlated with plant abundance and condition.

Figure 3: Comparison of plant spectral absorption of red and its reflectance of NIR. A corresponding vegetation index is illustrated that exploits this relationship to produce a highly correlated index.

The most commonly adopted index is the normalised difference vegetation index (NDVI, Equation 1). The NDVI was first conceptualised in response to changing solar zeniths over broad geographic areas. Early vegetation indices were highly sensitive to the shift in solar zenith as data collection moved from lower to higher latitudes. The normalisation effect of the NDVI helps compensate for this effect, producing consistent measurements across latitudes.

Equation 1: Calculation of the NDVI from NIR and red spectral channels.

However, the NDVI itself is sensitive to changing environmental factors including atmospheric and soil compositions. More advanced vegetation indices have been developed from the NDVI to counter these effects. The soil‐adjusted vegetation index (SAVI) expanded the NDVI to incorporate a pre‐determined fixed parameter that addresses variability in soil composition and saturation (Equation 2) (Huete 1988). While capable of compensating for background soil effects, the main limitation with the SAVI was its dependence upon a single soil adjustment parameter. Difficulty


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arose in the selection of this parameter, particularly in scenarios where a single value was unrepresentative of the variety of soil conditions across a landscape. The SAVI was further refined into the modified soil‐adjusted vegetation index (MSAVI) (Qi et al. 1994). The MSAVI incorporated an element of self‐adjustment, providing a vegetation index with the dynamic capacity for adjusting to changing environmental conditions. This effect not only improved the response of vegetation index measurements across changing soil conditions, but also improved the measure under other variable environmental conditions such as shadowing.

Equation 2: Calculation of the SAVI and the MSAVI from NIR and red spectral channels.

1 L L

2 1 2 1 8

2

Vegetation indices were generated for the three datasets. The pre‐processed 2012 and 2014 datasets were supplied with a NDVI layer. However, the absence of an NIR band in the supplied 2012 and 2014 datasets excluded the calculation of additional vegetation indices (i.e. MSAVI). NDVI and MSAVI layers were generated for both the 2016 and the 2017 datasets. Per‐pixel subtraction was used to quantify change between raster layers. The subtraction of an earlier NDVI measurement from a later measurement provides a quantification of the magnitude and direction of change over that time period.

Changes in NDVI were quantified using per‐pixel raster subtraction between the periods: 2012 to 2014, 2014 to 2016, and 2016 to 2017. Changes in MSAVI were quantified over one period: 2016 to 2017. Changes in vegetation condition where explored for the pre‐existing twelve quadrats established in the Sand Sheet Vegetation Community (MSS01‐MSS12) and for the newly established six reference quadrats (MSS13‐MSS18).

In addition to the quadrats, a predefined project area was supplied by Rio Tinto within which broader changes in NDVI and MSAVI were explored. For this phase of the analysis, the change layers were subsequently reclassified into a relative measure of change. This reclassification was based upon the relative degree of deviation, as defined by units of standard deviation from the mean change (Table 4). The reclassified data were then utilised within a cluster analysis.

Table 4: The statistical ranges and their corresponding descriptions used to reclassify areas of positive, negative and no change in a vegetation index change layer.

Statistical range Description

< ‐2 standard deviations below the mean Substantial negative change

‐2 to ‐1 standard deviations below the mean Moderate negative change

‐1 to + 1 standard deviations around the mean No change

+1 to +2 standard deviations above the mean Moderate positive change

> +2 standard deviations above the mean Substantial positive change

Cluster analyses assess the spatial distribution of phenomena, identifying and statistically assessing spatial patterns in the underlying data. A cluster analysis reveals if the distribution of phenomena is clustered together, distributed systematically or randomly distributed (i.e. no spatial pattern exists). A cluster analysis, utilising the Getis‐Ord statistic (Wulder and Boots 1998), was performed upon the spatial distribution of the reclassified vegetation change. The Getis‐Ord statistic was then assessed to determine if patterns of vegetation change exhibit a clustering behaviour that was significantly different from a spatially random process.


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Figure 4: Illustration of the different distributions detectable through the use of cluster analysis: clustered, systematic and random.

Vegetation cover was estimated by the application of a threshold to a vegetation index. This threshold demarcates the index into a binary state that identifies vegetated and non‐vegetated areas. Vegetation cover statistics across predefined areas can be estimated by calculating and comparing the proportional area of vegetated to non‐vegetated pixels (Figure 5).

Figure 5: Illustration of the demarcation of vegetation using a threshold binary layer, and a corresponding percentage vegetation cover estimates using contiguous quadrats.

2.4.1 OnGroundandRemoteSensingComparisons

Mean NDVI and MSAVI values derived from remote sensing and attributed to quadrats in 2017 were compared to values recorded on ground using linear models. The on ground variables tested were: vegetation cover (sum of each species), plant health (Fv/Fm), number of species and dust scores. Changes to NDVI and MSAVI scores (2012 to 2017 and 2016 to 2017 respectively) were compared to the distance to the mine footprint.

2.5 Limitations

MSS01 had a smaller area than other quadrats (60 m x 30 m vs. 50 m x 50 m). This affected very few analyses as variables (% cover, dust score) are area‐independent, as are any assessments of relative change over time. The only area‐dependent variable is species richness, in which the relationship between species richness and area is not linear. Adjusting for this contrast in area requires a complex rarefaction process using a variety of different sized quadrats. Therefore, consideration of MSS01 in terms of relative species richness should be approached with caution. However, none of the analyses indicated that the reduced area of MSS01 influenced a statistically significant result.


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Seasonal conditions were considered average, with lower than average rainfall preceding the survey. As such there was generally lower occurrence of some taxa compared to the previous year which recorded optimal survey timing for the collection of annual and ephemeral flora, and cryptic perennial species (Biota Environmental Sciences 2016). The 2015, 2013, 2011 and 2009 monitoring surveys also recorded optimal conditions, while 2014, 2012, 2010 and 2008 monitoring surveys recorded dry conditions.

Mapping of vegetation, vegetation condition and dust deposition on vegetation was primarily based on data collected from quadrats. As such, vegetation mapping and descriptions were undertaken at a relatively broad‐scale.

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3.2 Floristics

3.2.1 Overview

A total of 105 confirmed plant species from 29 families and 60 genera were recorded from the eighteen quadrats in the 2017 monitoring survey. The dominant plant families was were Fabaceae and Malvaceae, which recorded 18 species each, while Acacia was the most frequently recorded genus (Table 5). A species list from 2008 to 2017 (Table D.1) and a site by species matrix for 2017 (Table D.2) are presented in Appendix D.

Table 5: Taxa most frequently recorded.

Family Number of taxa

Fabaceae 18

Malvaceae 18

Poaceae 14

Genus Number of taxa

Acacia 9

Ptilotus 8

Sida 5

Solanum 5

3.2.2 ConservationSignificantFlora

No Threatened or Priority flora were recorded during the 2017 monitoring survey, nor were any identified during previous monitoring surveys by Biota (2009, 2010, 2011, 2012, 2014a, 2014b, 2015, 2016).

3.2.3 IntroducedFlora(Weeds)

Only one weed species, *Cenchrus ciliaris (Buffel Grass), was recorded during the 2017 monitoring survey. *Cenchrus ciliaris was recorded in four of the eighteen quadrats (MSS04, MSS09, MSS11 and MSS17) and 23 opportunistic observations were recorded. *Cenchrus ciliaris has previously been recorded in quadrats MSS04 and MSS09, but it has not been previously recorded in MSS11. Locations and maps of the 2017 weed occurrences are presented in Appendix E.

In addition to *Cenchrus ciliaris, five weed species have been recorded during the monitoring surveys between 2008 to 2016 (Table 6). None of the weed species recorded are listed as a declared pest for the Shire of Ashburton nor are they a Weed of National Significance (Department of Primary Industries and Regional Development 2017; Australian Weeds Committee 2012).


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Table 6: Introduced flora species (weeds) recorded during the monitoring survey between 2008 to 2017 Biota (2009, 2010, 2011, 2012, 2014a, 2014b, 2015, 2016).

Species

(common name) 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

*Flaveria trinervia

(Speedy Weed) X X

*Malvastrum americanum

(Spiked Malvastrum) X

*Cenchrus ciliaris

(Buffel Grass) X X X X X X X X X X

*Cenchrus setiger

(Birdwood Grass) X X X X

*Digitaria ciliaris

(Summer Grass) X X

*Setaria verticillata

(Whorled Pigeon Grass) X X

3.3 SpeciesRichness

Analyses were performed separately for all native species combined, and perennial native species only. Results were indistinguishable from each other, and therefore reporting focusses on the analysis for all native species.

Number of native species per quadrat did not differ significantly between reference areas and Sand Sheet Vegetation Community quadrats (F1,16 = 0.26, P = 0.62; Figure 7). While the twelve quadrats within the Sand Sheet Vegetation Community increased significantly in species richness from 2008 to 2017, the rate of increase was dependent upon the particular quadrat (year‐by‐site interaction, F11, 90 = 2.65, P = 0.006; Figure 8). Although MSS05 exhibited the lowest slope of all sites, the interaction term was still significant once this quadrat was removed from the analysis, indicating that the contrast in response of quadrat was more widespread than a single outlying quadrat (F10, 82 = 2.38, P = 0.016).

The contrast in slopes amongst quadrats could not be explained by their proximity to the disturbance footprint (year‐by‐distance interaction term, F1,110 = 0.18, P = 0.67). Sites which were closer to the disturbance footprint did not necessarily have a lower increase in species richness over the period of monitoring.


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Figure 7: Number of native species recorded in 2017 at the reference and Sand Sheet Vegetation Community quadrats. Numbers are displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the 25 and 75 percentiles and the ‘whiskers’ indicate the maximum and minimum values.

Figure 8: Number of native species recorded at Sand Sheet Vegetation Community quadrats (MSS01‐MSS12) from 2008 to 2017.


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3.4 VegetationCover

Vegetation cover in 2017 did not differ significantly between the reference and Sand Sheet Vegetation Community quadrats (F1,16 = 0.23, P = 0.64; Figure 9). Within the Sand Sheet Vegetation Community, vegetation cover declined significantly and this rate of decline differed amongst quadrats (year‐by‐site interaction term, F11, 90 = 3.32, P < 0.001, Figure 10). Rate of decline in cover was not related to the proximity to the disturbance footprint (year‐by‐distance interaction term, F1, 110 = 2.09, P= 0.15). Similar declines in cover, varying across quadrats, was seen when both Triodia schinzii and Acacia tumida var. pilbarensis were analysed separately (results not shown). At the quadrat level, projected foliage cover (recorded only in 2017) did not differ with distance from the disturbance footprint (F1,10 = 0.004, P = 0.95; Figure 11).

Rate of cover decline across quadrats was not associated with their proximity to the disturbance footprint (see above) or CF recorded in 2017 (Pearson’s correlation = ‐ 0.43, P = 0.17).

Figure 9: Vegetation cover recorded in 2017 at reference and sand sheet sites. Numbers are displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the 25 and 75 percentiles and the ‘whiskers’ indicate the maximum and minimum values.


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Figure 10: Vegetation cover (summed across species) recorded at Sand Sheet Vegetation Community quadrats (MSS01‐MSS12) from 2008 to 2017.

Figure 11: Quadrat‐level foliage projected cover in terms of distance from the disturbance footprint.


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3.5 DustCover

Dust scores of individual plants were introduced to the monitoring program in 2017 and therefore cannot be tested for change over time. Dust scores on individual plants can only be tested between the Sand Sheet Vegetation Community and reference areas, and in reference to proximity to the disturbance footprint.

Dust scores differed between reference and Sand Sheet Vegetation Community quadrats, with plants in the reference area being consistently scored as ‘negligible’ (’1’), while average scores at the Sand Sheet Vegetation Community quadrats were consistently ‘low’ (‘2’) to ‘medium’ (’3’) (Wilcoxon’s rank score test, W = 0, P < 0.001; Figure 12), with the exception of quadrat MSS12 which consistently scored as ‘negligible’ across species.

Within the Sand Sheet Vegetation Community quadrats, there was no relationship between the average dust score and proximity to the disturbance footprint (Figure 13). The highest dust score was recorded at MSS11 (2.5 score, 23 m from disturbance), while the lowest was recorded at MSS12 (1.0 score, 0 m from disturbance). MSS12 is located in a separate patch from the other sites near the waste dump, at which monitored dust levels were relatively low. Therefore the distance from disturbance for MSS12 may not serve as an ideal proxy for the level of dust exposure.

Figure 12: Average dust scores recorded in 2017 at reference and Sand Sheet Vegetation Community quadrats. Data are displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the 25 and 75 percentiles and the individual points indicate extreme outliers.

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3.6 PlantPhysiologicalHealth

When species identity was not considered, there was no significant difference between the Index of Chlorophyll Fluorescence (ICF) for plants at reference areas and the Sand Sheet Vegetation Community (F1, 148 = 1.64, P = 0.20; Figure 17). However, Sand Sheet Vegetation Community plants recorded the lowest values (0.25 for Grevillea eriostachya at MSS07 and 0.47 for Acacia ancistrocarpa at MSS12). These values are considered less than healthy (Ritchie 2006). Contrasts between the two site types differed greatly when plant species was considered. Reference area values were significantly higher than Sand Sheet Vegetation Community values for Acacia ancistrocarpa (W = 34.5, P = 0.01; Figure 18), while reference area values were significantly lower for Acacia trachycarpa (W = 125.5, P = 0.002; Figure 18) and Acacia tumida var. pilbarensis (W = 50.5, P = 0.02; Figure 18). There was no significant relationship between observed dust on plants (dust scores) and CF (F2, 50 = 0.79, P = 0.46; Figure 19). There was insufficient variation in dust scores within plant species to analyse relationships for individual species. CF values within a quadrat were not related to their proximity to the mine footprint and quadrats closest to the mine exhibited both the highest and lowest CF values (Figure 20, F1,10 = 0.24, P = 0.63) (Figure 20).

Figure 17: Leaf chlorophyll fluorescence across reference and Sand Sheet Vegetation Community quadrats in 2017, all species combined. Values are displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the 25 and 75 percentiles, the ‘whiskers’ indicate the expected maximum and minimum values and the individual points indicate extreme outliers.


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Figure 18: Leaf chlorophyll fluorescence, by species, in 2017 across reference and Sand Sheet Vegetation Community quadrats. Values are displayed as boxplots: the dark horizontal line indicates the median value, the box indicates the 25 and 75 percentiles, the ‘whiskers’ indicate the expected maximum and minimum values and the individual points indicate extreme outliers.


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Figure 19: Leaf chlorophyll fluorescence values of individual species within each quadrat and their associated dust score.

Figure 20: Mean leaf chlorophyll fluorescence values within each quadrat in relation to the distance from the mine footprint.


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While there was a slight increase in cover with increased rainfall, this relationship was not significant for all species combined (F1, 4 = 3.32, P = 0.14; Figure 21), Acacia tumida var. pilbarensis (F1, 4= 3.32, P = 0.14), or Triodia schinzii (F1, 4 = 3.32, P = 0.14). Species richness was not associated with rainfall (F1, 4 = 3.32, P = 0.14; Figure 21). This lack of significance could be partially due to low statistical power, with rainfall data only available for six years. However, similar rainfall in 2012 and 2016 (253 and 256 mm respectively) was associated with quite different vegetation cover values (76.0 and 61.8% respectively) indicating that rainfall in the preceding 12 months does not satisfactorily explain the variation seen in vegetation cover.

Figure 21: Relationships between vegetation cover (%), species richness and annual rainfall. Annual rainfall was calculated as the rainfall in the 12 months prior to survey. A.t.p. = Acacia tumida var. pilbarensis, T.z. = Triodia schinzii. Dotted lines indicate the line of best fit based on linear regression. None of these relationships were statistically significant.

3.7 RemoteSensing

3.7.1 OnGroundandRemoteSensingComparisons

Assessments of vegetation state across the 18 sites based on remote sensing (NDVI and MSAVI) generally did not correlate with values derived from the available on ground measurements (Table 7). The exceptions were dust scores, which were negatively correlated with NDVI and MSAVI values (Table 7). Temporal changes to NDVI and MSAVI within quadrats was not associated with proximity to the mine (Table 7). There was no consistent association between changes in remotely sensed vegetation indices and changes in cover recorded on the ground (Table 8). Spatial declines in NDVI or MSAVI (Figure 22 and Figure 23) were not strictly associated with areas mapped with the highest dust loads (’medium’).


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Table 7: Statistical relationships between remote sensing vegetation indices and on‐ground measurements of vegetation state in 2017. Significant P values (P < 0.05) are in bold.

On ground variable Remote sensing index

F‐value P‐value R2

Cover Cover, NDVI 0.60 0.45 0.04

Cover, MSAVI 0.72 0.41 0.04

Health (Fv/Fm) NDVI 4.46 0.05* 0.22

MSAVI 3.52 0.08 0.18

Species NDVI 0.92 0.35 0.05

MSAVI 0.14 0.72 0.01

Dust scores NDVI 6.67 0.02 0.25

MSAVI 15.48 <0.01 0.46

Distance to mine footprint

Change in NDVI, 2012‐2017

0.52 0.49 0.05

Change in MSAVI, 2016‐2017

0.10 0.76 0.01

* Negative correlation. Low Fv/Fm was associated with higher NDVI.

Figure 22: Relationship between on ground dust scores (mean score per quadrat) and NDVI in 2017.


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Figure 23: Relationship between on ground dust scores (mean score per quadrat) and MSAVI in 2017.

Table 8: Comparison of remote sensing observations (see Section 3.7.3 for more detail) and on ground assessment of change to vegetation cover.

Quadrat

Change in on ground cover 2012‐2017 (%/year)

Change in NDVI 2012‐2017

Change in on ground cover 2016‐2017 (%/year)

Change in NDVI 2016‐2017

Change in MSAVI 2016‐2017

MSS01 ‐2.3 Slight positive ‐10.4 Decline Decline

MSS02 2.1 Slight positive ‐6.5 Decline Decline

MSS03 ‐4.1 Slight positive 2.3 Slight positive Slight positive

MSS04 ‐7.4 Positive ‐10.2 Slight positive Slight positive

MSS05 ‐3.7 Positive ‐15.5 Slight decline Slight decline

MSS06 ‐6.9 Positive ‐10.8 Neutral Neutral

MSS07 ‐8.9 Decline in portion of quadrat

‐2.9 Slight decline Slight decline

MSS08 ‐5.0 Positive ‐3.0 Decline Decline

MSS09 ‐3.0 Positive ‐4.1 Positive Positive

MSS10 ‐8.6 Slight positive 1.3 Positive Positive

MSS11 ‐5.6 Slight positive ‐6.5 Positive Positive

MSS12 3.7 Positive ‐22.0 Decline Decline


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3.7.2 RemotelySensedQuantificationofVegetationChange

NDVI layers were generated for the 2012, 2014, 2016 and 2017 datasets (Appendix I, Figure I.5, Figure I.6, Figure I.7, Figure I.8). A visual comparison between the 2012, 2014, 2016 and 2017 NDVI layers is presented in Figure 24. The distribution of the four NDVI layers, with regard only to the Sand Sheet Vegetation Community and reference areas, is illustrated in a histogram (Error! Reference source not found.). Notable is the relative stability in NDVI measurements of the monitoring area, with a subtle progressive shift to higher index responses over the monitoring period. Of interest is the markedly sharp spike in low values within the reference area for the 2012 period. Closer examination of this area shows fire scarring within the southernmost reference area (Appendix I, Figure I.1), resulting in the blackening of vegetation and exposure of soil. This effect is particularly noticeable along the drainage lines. The reestablishment of vegetation by 2014 and 2016 (Appendix I, Figure I.2, Figure I.3), corresponds with higher, more stable NDVI distributions (Figure 24).


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Figure 24: Visual comparison of the NDVI layers generated for 2012, 2014, 2016 and 2017.


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Figure 25: The distribution of NDVI values between the Sand Sheet Vegetation Community and the reference areas for 2012, 2014, 2016 and 2017.

MSAVI layers were generated for both the 2016 and 2017 datasets (Appendix I, Figure I.9, Figure I.10). A visual comparison between the 2016 and 2017 MSAVI layers is presented in Figure 26. The distribution of the two MSAVI layers, with regard to the Sand Sheet Vegetation Community and reference areas, is illustrated in a histogram (Figure 27). Notable is the high level of stability exhibited, both temporally and spatially.

Figure 26: Visual comparison of the MSAVI layers generated for 2016 and 2017.


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Figure 27: The distribution of MSAVI values between the Sand Sheet Vegetation Community and the reference areas, for each of the two years: 2016 and 2017.

Change was calculated for the NDVI between 2012 to 2014, 2014 to 2016 and 2016 to 2017 (Appendix I, Figure I.11, Figure I.12, and Figure I.13). In addition, MSAVI change was calculated between 2016 and 2017 (Appendix I, Figure I.14). A visual comparison of the change in MSAVI (2016 to 2017), as well as NDVI change (2012 to 2014, 2014 to 2016 and 2016 to 2017), is presented in Figure 28. With the exception of the 2012 to 2014 period, measures of NDVI change all peaked within vegetation gain for all periods and sites (Error! Reference source not found.). The MSAVI within the Sand Sheet Vegetation Community peaked marginally within decline across the 2016 to 2017 timeframe. Conversely, the reference area peaked marginally within the gain for the same period.


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Figure 28: Comparison of the vegetation index change layers for the NDVI periods 2012‐2014, 2014‐2016 and 2016‐2017, and the MSAVI period 2016‐2017.


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Figure 29: The distribution of NDVI and MSAVI change values between the Sand Sheet Vegetation Community and the reference areas, for each pair of comparison years.

A cluster analysis was performed upon the NDVI 2012 to 2017 change layer (Appendix I, Figure I.12), as well as 2016 to 2017 (Appendix I, Figure I.13). Cluster analysis was also performed upon the MSAVI 2016 to 2017 change (Appendix I, Figure I.14). The results of the cluster analysis are presented in Figure 30. Notable is the strong cluster of reduction in vegetation recorded in the NDVI 2012 to 2017 layer, occurring along the drainage line. This reduction is notably absent in the clustering analysis of 2016 to 2017 change (both NDVI and MSAVI). Clusters of reduction within 2016 to 2017 are recorded within PEC2, along the western edge of PEC1 and east of the outcropping. Notable clustering of gains within the NDVI 2012 to 2017 layer occurs within the isolated patch (MSS12) and along the northern edge of the Sand Sheet Vegetation Community. Within the 2016 to 2017 clustering, gains are focused along the northern and eastern edge.


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Figure 30: Comparison of the vegetation index change clustering layers for the NDVI periods 2012‐2017 and 2016‐2017, and the MSAVI period 2016‐2017


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3.8 VegetationMapping

Biota (2005, 2006) described one vegetation type within the Sand Sheet Vegetation Community, and three vegetation types were described within the reference areas (Table 9). Vegetation mapping is provided in Figure F.1 (Appendix F).

Table 9: Vegetation types recorded for the monitoring survey.

Vegetation types and description Sites Vegetation condition

Total area (ha) Representative photograph

CzAtGeTs

Corymbia zygophylla scattered low trees over Acacia tumida var. pilbarensis, Grevillea eriostachya tall shrubland over Triodia schinzii hummock grassland

MSS01, MSS02, MSS03, MSS04, MSS05, MSS06, MSS07, MSS08, MSS09, MSS10, MSS11, MSS12

Excellent ‐ Poor

153

Plate 1: Vegetation representing CzAtGeTs – facing south‐east from GPS coordinates 384819mE and 7601974mN (MGA Zone 50).


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CzChAtrAa(Aa)TspP

Corymbia zygophylla, Corymbia hamersleyana low open woodland over Acacia trachycarpa, Acacia ancistrocarpa (Acacia arida) tall open shrubland over Triodia sp. Peedamulla (A.A. Mitchell PRP 1636) hummock grassland.

MSS13, MSS14, MSS16, MSS17

Excellent – Very good

52

Plate 2: Vegetation representing CzChAtrAa(Aa)TspP – facing south‐east from GPS coordinates 385780mE and 7600321mN (MGA Zone 50).

ChAtr(Sao)TspPAhPm

Corymbia hamersleyana low open woodland over Acacia trachycarpa (Senna artemisioides subsp. oligophylla) tall open shrubland over Triodia sp. Peedamulla (A.A. Mitchell PRP 1636) open hummock grassland with Aristida holathera var. holathera, Paraneurachne muelleri scattered tussock grasses.

MSS15 Very good 9

Plate 3: Vegetation representing ChAtr(Sao)TspPAhPm – facing south‐east from GPS coordinates 384428mE and 7600990mN (MGA Zone 50).


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CzAtr(Atu)Ts

Corymbia zygophylla scattered low trees over Acacia trachycarpa (Acacia tumida var. pilbarensis) tall open scrub over Triodia schinzii open hummock grassland.

MSS18 Excellent 13

Plate 4: Vegetation representing CzAtr(Atu)Ts – facing south‐east from GPS coordinates 385097mE and 7601462mN (MGA Zone 50).


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3.9 VegetationCondition

Vegetation in the Sand Sheet Vegetation Community ranged from ‘excellent’ to ‘poor’ (Trudgen 1988). Vegetation within two of the reference areas (MSS15 and MSS 17) were mapped as being in ‘very good’ condition, while the other two reference areas were mapped as being in ‘excellent’ condition (Trudgen 1988) (Appendix G. Figure G.1).

For the majority of the Sand Sheet Vegetation Community and reference areas, there were no obvious signs of damage caused by human activity. Isolated and small populations of *Cenchrus ciliaris (Buffel Grass) and/or higher levels of dust deposition on vegetation (Appendix G. Figure G.2) was recorded in vegetation mapped as ‘very good’ within the Sand Sheet Vegetation Community. An area of vegetation in the eastern side of the Sand Sheet Vegetation Community was rated as being in ‘good’ condition due to the higher density of *Cenchrus ciliaris (Buffel Grass) both currently and historically. Rubbish was only recorded at quadrat MSS11, which was within close proximity mining operations (less than 50 m). An area north of MSS01 was mapped as ‘poor’ due to historic clearing.

Extensive but old ground disturbance from exploration activity was present within one of the reference areas mapped as ‘very good’, and old vehicle tracks and two *Cenchrus ciliaris records were noted in another reference area that was rated as being in ‘very good’ condition. No evidence of cattle activity, or any other introduced herbivores, was recorded in the Sand Sheet Vegetation Community or reference areas.

Senescence was observed in several species during the field survey and it was particularly prevalent in Acacia tumida var. pilbarensis and Triodia schinzii. Triodia spp. dieback was first recorded by Biota in 2014 (Biota Environmental Sciences 2014b), while widespread senescence of both Triodia spp. and Acacia spp. was noted in 2015 (Biota Environmental Sciences 2015). Biota (2016) proposed four possible factors that could be attributed to the cause of widespread senescence; dust, altered surface water flow, fire history and climate conditions. But as the cause is unknown, plant senescence was not included as part of the Trudgen (1988) vegetation condition classification for the 2017 monitoring survey.

Senescence in adult Acacia tumida var. pilbarensis appeared to be widespread throughout the Sand Sheet Vegetation Community (Plate 5); however, recruitment within Acacia tumida var. pilbarensis was also prevalent with seedlings and saplings observed to be widespread throughout the Sand Sheet Vegetation Community. This extensive senescence was not observed in Acacia tumida var. pilbarensis, or other any other Acacia species, within the reference areas.

Patches of Triodia schinzii dieback was observed throughout the Sand Sheet Vegetation Community (Plate 6), though not as prevalent as senescence in the adult Acacia tumida var. pilbarensis. Triodia spp. dieback was noted in two quadrats within the reference areas, but it did not appear to be widespread through any of the other reference areas.

General observations on vegetation condition and health within the Sand Sheet Vegetation Community focused on Acacia tumida var. pilbarensis and Triodia schinzii. These observations are presented in Table H.1 (Appendix H). Quadrat ground‐truthing observation locations are presented with the NDVI change 2014 to 2016 clustering layer (Astron Environmental Services 2017a) in Figure H.1 (Appendix H). Dead Acacia tumida var. pilbarensis was often associated with NDVI increase from 2014 to 2016. Areas in which NDVI changed the most from 2014 to 2016 generally corresponded with on ground observations of areas associated with water pooling after rainfall. In these areas tussock grass species, such as Aristida holathera var. holathera and Paraneurachne muelleri, were dominant, rather than Triodia schinzii. The changes to NDVI values may have recorded the changes in biomass in these tussock grasses as they fluctuate dependent upon conditions.


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Plate 5: Senescence observed in the adult Acacia tumida var. pilbarensis.

Plate 6: Dieback observed in the adult Triodia schinzii.

Plate 7: Tussock grass dominated vegetation observed at Opp02.


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4 Conclusions

Astron was commissioned to undertake the tenth annual Sand Sheet Vegetation Community monitoring survey in September 2017 and to assess the effects of mining operations from the Mesa A Iron Ore project on the Sand Sheet Vegetation Community from 2008 to 2017. Additionally, remote sensing was employed to quantify changes in vegetation condition over a broader spatial extent.

On ground monitoring indicated that vegetation cover in the Sand Sheet Vegetation Community has significantly declined over the period of monitoring (2008 to 2017), while the mean number of species has increased. As there was no long term monitoring of an appropriate reference area, proximity to the mine footprint was used as a proxy for potential level of impact amongst Sand Sheet Vegetation Community quadrats. Declines in cover amongst the monitored quadrats were not associated with proximity to the mine footprint. Rainfall in the previous 12 months was tested as a possible explanation for the observed cover decline; however, there was no correlation between rainfall and vegetation cover.

Remotely sensed imagery comparing 2012, 2014, 2016 and 2017 indicated that the Sand Sheet Vegetation Community vegetation condition was relatively stable between 2012 and 2017. Mapped change in NDVI and MSAVI indices over the same period indicated some areas of decline and other areas of improvement, with the main area of decline along the northwestern edge of the Sand Sheet Vegetation Community in the period 2016 to 2017.

Remotely sensed vegetation condition was compared to values recorded on ground in 2017. NDVI and MSAVI values for each quadrat were correlated with dust levels quantified on ground, but dust levels were not directly linked to plant condition measures quantified on ground. While areas of vegetation dieback were noted on ground, particularly in Acacia tumida var. pilbarensis and Triodia schinzii, ground‐truthing revealed that changes highlighted by remote sensing were mostly associated with on ground changes in ephemeral vegetation associated with surface water.

Vegetation condition did not generally differ between the Sand Sheet Vegetation Community and the reference areas, which were established in 2017. The exception was dust scores which tended to be ‘medium’ in the Sand Sheet Vegetation Community and ‘low ‐ none’ at the reference areas.

There was no clear evidence that dust loads were affecting plant health within the period of monitoring. Declines in vegetation may be due to changes in soil moisture or altered surface water flow. However, refining sources of dust to more specific locations than the mine edge in general may assist in understanding potential impacts in terms of known vegetation declines.

Comparison of on ground and remote sensing methods indicated that the two methods were providing complimentary rather than mutually redundant information. On ground monitoring quantifies values such as vegetation complexity and species diversity, and is instrumental in characterising change which is first detected by remote sensing. Remote sensing provides a landscape‐level perspective on vegetation change, including historical change not necessarily available from on ground data. While remote sensing detected vegetation change in small patches, often occurring between quadrats, on ground monitoring and ground‐truthing is then useful to confirm and diagnose these changes.


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5 References

Aplin, TEH 1979, 'The flora', in B. J. O'Brien (ed), Environment and Science, University of Western Australia Press, Perth.

Astron Environmental Services 2017a, Mesa A Sand Sheet Vegetation Monitoring ‐ Remote Sensing Analysis, January 2017, unpublished report for Rio Tinto Iron Ore, Perth.

Astron Environmental Services 2017b, Mesa A Sand Sheet Vegetation Processing and Extraction of Vegetation Index Data, unpublished report to Rio Tinto, Perth.

Australian Weeds Committee 2012, Weeds of National Significance 2012, Department of Agriculture, Fisheries and Forestry, Canberra.

Biota Environmental Sciences 2005, Vegetation and Flora Survey of Mesa A and Mesa G, Near Pannawonica, unpublished report for Robe River Iron Associates.

Biota Environmental Sciences 2006, A Vegetation and Flora Survey of the Proposed Mesa A Transport Corridor, Warramboo Deposit and Yarraloola Borefield, unpublished report to Robe River Iron Associates.

Biota Environmental Sciences 2009, Mesa A Sand Sheet Environmental Monitoring Report: Baseline Survey to 2009, unpublished report for Rio Tinto Iron Ore, Perth.




Biota Environmental Sciences 2014a, Mesa A Sand Sheet Environmental Monitoring Report: Baseline Survey to 2013, unpublished report for Rio Tinto Iron Ore, Perth.

Biota Environmental Sciences 2014b, Mesa A Sand Sheet Environmental Monitoring Report: Baseline Survey to 2014, unpublished report for Rio Tinto Iron Ore, Perth.


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Hansatech Instruments 2015, Chlorophyll fluorescence product support, viewed 30 January 2017, <http://hansatech‐instruments.com/products/introduction‐to‐chlorophyll‐fluorescence/>.


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Ritchie, GA 2006, 'Chlorophyll Fluorescence: What is it and what do the numbers mean?', in L. E. Riley, R. K. Dumrosese and T. D. Landis (eds), National Proceedings: Forest and Conservation Nursery Associations ‐ 2005. , Vol. USDA Forest Service Proceedings RMRS‐P‐43.

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