Evaluating the impacts of longwall mine subsidence on vineyards in the Broke Region of New South Wales: The

challenges of analysing multi-scale field data

J. A. Thompson, Precision Agriculture Research Group, University of New England

P. S. Frazier, Precision Agriculture Research Group, University of New England

D. W. Lamb, Precision Agriculture Research Group, University of New England

Summary

This paper explores the impacts of longwall mine subsidence on vineyards in the Broke region

of the Upper Hunter Valley. The Broke region is a recognized viticultural subregion of the

Upper Hunter known for its white wine varietals of Chardonnay and Semillon and for its red

wine varietals of Cabernet Sauvignon, Merlot and Shiraz. Bulga Coal Management was

granted, the right to extend its extraction activities to seams beneath several vineyards in the

Region. The first vineyards were undermined in 2005, and by end of the mining lease, 9

vineyards totalling some 119 ha of vines will eventually be undermined several times. In 2003,

a long-term project was established to monitor the impacts of longwall mining on these

vineyards. Data were collected at different scales and at different times of year. As such, the

dataset is both multi-scaled and multi-temporal with at least 2 years of pre-mining records and

2 years post-mining records. In this paper we present the data collection strategy and proposed

analysis methods. The challenges of dealing with a highly variable, multi-temporal and multi-

scale data set are highlighted with particular attention given to the delineation of likely impact

zones resulting from subsidence.

1. Introduction

Situated near Singleton in the Upper Hunter

Valley and the villages of Broke and Bulga

is the Broke-Fordwich viticultural region.

This region was amongst the first areas to be

formally recognized as a separate

viticultural sub-region of the Hunter Valley.

In wine circles, it is known for its cv.

Chardonnay and cv. Semillon white

varietals and for its cv. Cabernet Sauvignon,

cv. Merlot and cv. Shiraz red varietals. In

recent years, several of its vineyards have

been singled out and recognized for their

award winning wines (Broke Fordwich

Tourism Association 2007).

In addition to being a unique sub-region of

the Hunter, the Broke-Fordwich region is

also unique in that viticultural activities

occur in close proximity to longwall coal

mining. Some nine vineyards in the region

are situated over active longwall mining

activities. In 2003, subterranean mining at

the Beltana Number 1 mine commenced,

and in 2005 the first vineyards were

undermined.

A long-term monitoring project designed to

monitor the impacts of longwall mine

subsidence (LWMS) on viticultural

production in the undermined vineyards also

commences in 2003. Monitoring data have

been collected at various scales and at

different times of the year, the data forming

a multi-scaled, multi-temporal data set. In

this paper we present: the design and

techniques associated with collecting the

monitoring data collection: results from the

exploratory phase of data analysis; and a

discussion of analysis procedures at the

vineyard block and whole vineyard scales.

2. Study Area

The study area comprised seven vineyards

located approximately 18 km from

Singleton between the villages of Broke and

Bulga in the Broke-Fordwich region (Lat

32° 37’ S, Long 151° 12’ E) of the upper

Hunter (Figure 1). Semillon and

Chardonnay are the most common types of

grape vines found within the study area

vineyards. Additional varieties include

Verdelho (white varietal) as well as Merlot,

Shiraz and Pinot (red varietals).

Figure 1. Study area map, highlighting vineyards,

relevant longwalls, and subsidence monitoring

transects.

Long-term meteorological data indicate that

average rainfall for the Broke-Fordwich

region is 722 mm per year (BoM 2007).

Highly variable, summer dominant rainfall

is characteristic of the region as are high

maximum temperatures regularly exceeding

40° (C) heat stress is a common problem for

vines in this region (Dry and Smart 2004)

and irrigation is generally required.

Topographically, this region has been

categorised as undulating to hilly (Kovac

and Lawrie 1991). Elevation in the study

area ranges from a low of 70 m (asl – above

seal level) in the southwest to a high of 172

m (asl) in the northeast, with the slope of

most vineyard blocks being less than 6 %.

The majority of blocks have a south-

westerly to westerly to orientation.

There are three major soil types within the

study area, alluvial soils, yellow podzols,

and chocolate soils with the latter two

characterizing much of the area planted out

to vines. Kovak and Lowrie (1991) have

classified the podzolic soils as belonging to

the Branxton soil landscape, which is

comprised of Permian shale, sandstone,

mudstone, siltstone, tuff and coal seams,

with Yellow Podzolic soils on midslopes

and Red Podzolic soils on crests and upper

slopes. The chocolate soils were identified

as belonging to the Saxonvale soil

landscape, comprised of tertiary dolerite

with some basalt, Chocolate Soils on the

slopes with brown Soloths on some upper

slopes (Kovac and Lawrie 1991).

Eventually, an estimated 119 ha of vines in

9 vineyards will potentially be subjected to

LWMS (Smart 2003) associated with the

mining of four, progressively deeper coal

seams of the Hunter Coalfield (Waddington

Kay & Associates 2003; O'Brien 2004). The

relevant seams form part of the gently

dipping strata of the larger Singleton Super

Group and are part of the Wittingham Coal

Measures of the Jerry Plains Subgroup

(Stevenson et al. 1998; Waddington Kay &

Associates 2003). Most of the rock units are

of Upper Permian Age, with seams

interbedded with sandstone, conglomerate,

siltstone, shale and tuff (Waddington Kay &

Associates 2003). Within the study area, the

average depth of cover ranges from 211 –

225 m, with an average seam thickness of

2.8 m (Waddington Kay & Associates

2003).

Prior to commencement of undermining in

2003, subsidence patterns were modelled

using the incremental profile method of

Waddington and Kay (1995). Projections

estimated that subsidence in the study area

would not exceed 2000 mm and that the

angle of draw would range between 20 – 22°

(Waddington Kay & Associates 2003). Post-

commencement subsidence monitoring

suggests that there is a reasonable

correlation between predicted and observed

subsidence under vineyards, with observed

values being less than the maximum

predicted subsidence in all cases (MSEC

2007). With the exceptions of longwalls 6

and 7, the actual draw angles were within

the predicted range. For longwalls 6 and 7,

the angles of draw were 26.5° and 28°

respectively (MSEC 2007).

3. Methods

To monitor the impacts of LWMS on

viticultural production a multi-scale, mutli-

temporal monitoring regime was developed

and implemented. A technical reference

group, consisting of mining company

representatives, academics, government

regulators and vignerons, designed the

survey regime (O'Brien 2004). As a result,

the study methodology encapsulates a

combination of industry standard viticultural

metrics, rigorous scientific methods, and

emerging research technologies. A sliding

window approach to data collection was

adopted, whereby data would be collected

for at least 2 years before and 2 years after

blocks were to be undermined. Hence, in

2003, two years before the first vineyards

blocks were undermined, data collection

commenced.

3.1. Vineyard Block Scale

Data were collected at the scale of

individual vineyard blocks. Monitoring at

this scale, hitherto referred to as panel

sampling, consisted of in-situ sampling of a

‘representative’ vine from every 2 - 3 panels

in ten adjacent rows for descriptors of grape

productivity including yield, 50 berry-

weight (50BW), number of bunches per vine

(2006 onwards), total soluble solids (°Brix),

titrateable acidity (TA) and pH. Descriptors

of vine biomass were also measured at

pruning time, including trunk diameter

(2005 onwards), mass of pruned foliage,

number of canes removed and number of

buds retained.

Initially, two vineyard blocks were selected

for monitoring; one comprised of cv.

Chardonnay (Vineyard A) and the other of

cv. Shiraz (Vineyard B). The cv.

Chardonnay block is located on a gently

sloped hillside (5% gradient) with a

vertically-shoot-positioned (VSP) trellising

system, while the cv. Shiraz is located on a

relatively flat block (1% gradient) with a

single-wire trellising system (Frazier et al.

2005). From each block, vines from ten

consecutive rows were selected and spatially

located using a differential global

positioning system (dGPS – Pro-XL,

Trimble, Sunnyvale, California USA). Vines

were selected based upon their relative

location to expected subsidence contours,

with care taken to ensure that areas between

the minium subsidence of the chain-pillars

and maximum subsidence associated with

the centre of longwall panels were well

represented. From 2006, panel data were

collected from three additional vineyard

sites (all belonging to Vineyard G), in

accordance with the sliding window

methodology described above.

3.2. Vineyard Scale

Continuous data were also collected at the

scale of entire vineyards. On-the-go yield

maps were acquired using a Braud self-

propelled grape harvester equipped with an

onboard FarmscanTM

yield monitor

(Farmscan, Perth, Western Australia,

Australia) and following the protocol of

Bramley and Williams (2001). In spite of

the fact that vine yield is seasonally

dependent, Bramley and Hamilton (2004)

have suggested that yield patterns in

vineyards may be temporally stable, in that

independent of the quality of the season,

some areas within a vineyard tend to be

better (or worse) producers in relation to

other areas.

Electromagnetic (EM38) soil surveys were

also conducted during both vine dormancy

(June) and post-harvest (February – March).

The apparent soil conductivity (ECa)

measured by the EM38 (Geonics, Ontario,

Canada) survey equipment have been

related to soil physical properties, and have

been used by Carpenter (1997) to map

longwall mining induces subsidence

fractures and Johnson et al. (2001) have

suggested ECa is well suited for the

temporal monitoring of soil ecological

trends. As vineyard trellising is known to

distort ECa measurements, all EM38 data

were acquired using the protocol of Lamb et

al. (2005).

3.3. Regional Scale

Regional scale data were collected in the

form of satellite images. With an individual

image encapsulating 64 km2, Digital

Globe’s Quickbird satellite platform was

selected as it could capture the entire Broke-

Fordwich region in one pass. Quickbird

imagery was also selected, as it currently

has the highest spatial resolution

commercially available from a space-based,

optical sensor platform. The panchromatic

and multispectral bands of Digital Globe’s

Basic Imagery Product were radiometrically

corrected and were supplied with sensor

attitude, ephemeris, and camera information,

making them suitable for advanced

photogrammetric processing (Digital Globe

2006). The work of Hall et al. (2003)

established a link between photosynthically

active biomass (PAB) and vine canopy

reflectance. With this relationship and with

a pixel resolution ranging from 2.44 – 2.88

m and 0.61 – 0.71 m, these multi-spectral

and panchromatic images should be capable

of both identifying vines and monitoring

variability in vine vigour across the study

region.

4. Data Exploration

The connection between longwall mining

and the above ground vineyards is complex

and poses numerous analytical challenges in

terms of the field data collected.

It is reasonable to expect that the finite

spatial extent of longwall mining would

impart some form of spatial limitation on

the above ground effects in terms of

viticultural production, as subsidence and

ensuing ground ‘strain’ are not uniformly

distributed across undermined areas. Since

grape vines are deep rooting plants, it is

reasonable to expect some form of

differences in the growing environment

before and after mining, with the transition

between the relatively undisturbed areas of

the chain-pillars and the areas of a

maximum subsidence of the longwall

potentially being the most heavily impacted.

This is due to the fact that this region

encompasses both the maximum and

minimum induced strain, which could

impact the roots either by compacting or

sheering them.

Because actual subsidence values were not

uniformly available across the vineyards

(note the considerable distance between

subsidence monitoring transects and

vineyards in Figure 1), a series of

subsidence zones were identified within

undermined vineyards. The subsidence

monitoring data of Mine Subsidence

Engineering Consultants (2007) were used

to construct zones of minimal subsidence

associated with chain-pillars (CP), zones of

maximal subsidence associated with the

longwall (LW), and a zone corresponding to

the transition (TR) between them. These

zones were identified longwall by longwall

and were designed to encapsulate both the

maximum and minimum mine-induced

strain. The survey points corresponding with

maximum and minimum strain were

identified and the survey point 20 m either

side of these points were selected as the

starting or ending point for the zone (Figure

2).

Figure 2. Longwall 4 observed subsidence and

strain; CP = chain pillar, TR = transition zone, LW =

longwall. Data courtesy of MSEC (2007).

A spatial template encapsulating three key

zones; chain pillar (CP), transition zone

(TR) and longwall (LW) was constructed

and subsequently used to partition data, both

continuous and panel-sampled, to allow

subsequent interrogation using both ArcGIS

9.2 (ESRI 2007) and the statistical package

R 2.4.1 (The R Project for Statistical

Computing 2006).

4.1. Vineyard Block Scale

A summary of the correlation between all

vine productivity descriptors measured in

the panel sampling (not partitioned into

mining zones) is given in Table 1. With a

cut-off of 0.5, it is evident that the following

relationships exist across all years:

pH r>0.5 Pruning Weight,

50BW r>0.6 Yield,

Brix r< 0.5 Yield,

TA r< 0.5 pH.

Viticulturally, these correlations are

sensible. For instance, with the pH/Pruning

Weight relationship the more mass removed

during pruning directly translates to more

incident sunlight on the grapes in the

growing season meaning they ripen faster

thereby reducing overall pH. With the

50BW/Yield relationship when berries are

larger and weigh more yields are also likely

to be higher. Similarly, the Brix/Yield

correlation evident in the study vineyards

confirms the viticultural link between higher

yields and lower Brix; in high yield vines,

finite quantities of assimilates (e.g. sugars)

are distributed amongst higher grape mass

(e.g. more berries, bigger berries) resulting

in lower amounts of dissolved solids overall.

Finally, the negative pH/TA relationship is

also sensible; TA measures the total amount

of dissociated or undissociated acids (H+

ions) in solution while pH measures only the

total dissociated acids (free H+ ions) in

solutions.

It should be noted that in previous years,

some of these relationships were stronger

and others also existed. It is an open-ended

question as to why some of these

relationships have deteriorated, though it is

likely that the drought and poor seasons

have been a significant factor as climate is

the most significant driver of viticultural

productivity.

When bi-variate plots of the panel data were

generated, the impacts of seasons on the

data became apparent (Figure 3). With data

coded by season, the presences of seasonal

clusters were immediately apparent.

A careful examination of the bi-variate plots

suggested that the data may contain

significant structure and the seasonal

structure of the data were noted above. It

also appears that differences in either the

variety or management of the panel blocks

may be significant as well. Further structure

is suggested through considering the timing

of mining. If time before mining is defined

as t0, 2004 and 2005 belong to the epoch of

t0 and all time after mining belongs to t1.

The apparent differences evident in the

comparison of t0 vs. t1 may also explain

some of the variance within the data.

However, caution needs to be exercised, as

it cannot be immediately concluded that

mining may be the most significant factor

influencing this t0 vs. t1 difference. This is in

part because the bi-variate plots do not

contain obvious differences between the

zones at the boundary of t0 vs. t1 as one

might expect if mining were the direct

cause. There is further evidence to suggest

that other factors may be at work here;

though not as complete as the panel data,

other data (not presented here) associated

with blocks that were not undermined in

2006 also exhibit a similar divergence at t1.

Though not conclusive, the data exploration

of the panel data suggests that the impacts of

LWMS are not immediately apparent and

readily observable. Thus, there is inherent

utility in the following multi-variate

relationships:

Eq 1. Seasons + Variety + Time

As Figure 4 suggests, the relative

pattern of EM38 variability appears

to be reasonably consistent from year

to year. Like the panel data, no

obvious patterns associated with

LWMS in t1 are immediately

obvious. Boxplots of the EM38 data

for the Vineyard A and Vineyard B

blocks (coincident with the panel

sampling data of Section 4.1 and

segmented according to the mining

template discussed earlier) are

presented in Figure 5. Boxplots

(a.k.a. box and whisker plots) are

useful for examining the range and

variability of data at a glance. The

box depicts the range between the

first and third quartiles, known as the

Inter-Quartile Range (IQR), and the

solid black line within the box

Table 1. Panel sampling data correlation matrix.

Canes Buds

Pruning

Weight Yield 50BW Brix pH TA

Canes 1.000 .

Buds .335 1.000

Weight .090 -.223 1.000

Yield .116 -.042 .101 1.000

50BW -.028 -.120 .255 .601 1.000

Brix .045 .164 .170 -.520 -.281 1.000

pH -.187 -.312 .508 -.418 -.187 .394 1.000

TA .077 -.186 -.310 .402 .081 -.374 -.513 1.000

4.2. Vineyard Scale

Like the panel data, early exploration of the

EM38 data demonstrated the need for a

rigorous statical analytical processing in

order to measure the impacts of LWMS on

viticultural productivity. As EM38 data

were captured as a continuous set of points,

they can be readily interpolated, for example

using an inverse-distance-weighting (IDW).

Figure 4 (Colour Plates) presents an

interpolated representation of the data

acquired during the 2004 and 2006 pruning

surveys using the IDW algorithm available

in ArcGIS 9.2 (ESRI 2007).

depicts the median point. The ‘whiskers’

depicted with the dashed line generally

represent the minimum and maximum

values of the data set. However, in the case

where extreme values are present in the data

(generally considered outliers), these points

are depicted with circles outside the

‘whiskers’. Such values are either 1.5 times

greater or smaller than the IQR.

As with the panel data, the EM38 surveys

suggest there is substantial inter-block

variability within the data. The average ECa

for Vineyard A was 32.3 mS/m with a

standard deviation of 15.2 mS/m and the

average ECa for Vineyard B was 44.7 mS/m

with a standard deviation of 20.1 mS/m. It

should be noted that unlike the panel data, t0

does not neatly encapsulate both 2004 and

2005; portions of both blocks were

undermined prior to the 2005 EM survey.

While there is an obvious difference in t0

and t1 ECa for Vineyard A, such a

difference is not evident in the Vineyard B

data. What this gap represents is unclear and

there are at least two possible alternatives:

1) it is the manifestation of LWMS; 2) the

amount of water retained within the soil

profile was substantially different between

the blocks at the time the survey was

undertaken in 2004, as there is generally a

positive correlated between soil moisture

and ECa.

Figure 5. EM38 pruning data for Vineyard A and

Vineyard B panel sampling blocks for all years.

Interestingly, although maps of Kovac and

Lawrie (1991) suggest the soil types are

likely to be the same, the ECa values of the

EM38 surveys suggest that they may in fact

be different. Considering that the Vineyard

B block on the border between two soil

types and considering that Kovac and

Lawrie (1991) were categorising soils at a

landscape level, their map could be expected

to be inaccurate at this local scale; in

particular at this locality.

In any case, as with the panel data, further

statistical analysis is required to determine if

any of the observed patterns are statically

significant and to assess whether of not

LWMS has a lasting effect on ECa.

4.3. Regional Scale

At the regional scale, a series of Normalized

Difference Vegetation Index (NDVI) images

were generated from the Quickbird satellite

data. In remote sensing, NDVI is a useful

index for indicating the relative vigour and

health of vegetation. Within viticulture, Hall

et al. (2003) established a correlation

between PAB and NDVI canopy

reflectance. Within NDVI images, areas

with healthy and vigorous vegetation appear

in the warmer colours of the image (reds and

yellows), with maximal values appearing as

deep red. Areas with poor or no vegetation

appear in the cooler colours (blue and green)

with minimal values appearing as deep blue

(Figure 6).

As is evident in the relative increase in

cooler colours across most vineyards in

2006, there appears to be a general decrease

in the presence of PAB in the vineyards. As

these images were acquired during harvest,

this is generally the time of maximum

canopy reflectance associated with PAB.

Correspondingly, in normal years the

January images should all be similar and

resemble that of the 2004 image, although it

should be noted that both the time of harvest

in relation to image capture and the

temperature during harvest can negatively

impact upon canopy reflectance.. Though

not presented, data for 2007 are similar to

those of 2006. The fact that all subsequent

January images reflect a relative decline in

healthy vineyard vegetation across all

vineyard blocks regardless of the presence

of LWMS perhaps suggests that prolonged

drought is beginning to have a significant

effect on viticultural production in the

Broke-Fordwich Region.

Figure 7 contains a boxplot comparison of

NVDI values associated with the panel

sampling blocks of the Vineyard A and

Vineyard B vineyards. Whereas the

Vineyard B block has exhibited a gradual

decrease in PAB since 2007, data from

Vineyard A suggest a stepped decline

occurring in 2007. This is perhaps indicative

of general difference in vineyard

management. It is worth commenting on the

significant number of circles evident in both

boxplots in Figure 7. While these are

normally indicative of outliers, in this case it

suggests that the data are not normally

distributed requiring a transformation prior

to analysis.

Unlike the EM38 data, which appears to

exhibit more natural variability in ECa

across the various zones, NDVI appears to

be relatively uniform across zones. This

relative uniformity of the NDVI values

across zones in spite of zonal soil variations

suggests that soil properties themselves may

not be the primary drivers of canopy growth

within these vineyards. In the context of

Hall et al’s (2003) work, this may imply

only a secondary link between soil

properties and viticultural properties such as

yield and Brix.

5. Conclusion

This paper presented the methodologies for

monitoring the impacts of LWMS on

viticultural production in the Broke-

Fordwich wine-producing region of the

Hunter. Further, it explored some of the

analytical complexities associated with the

resulting data.

Although no patterns associated with

LWMS are immediately apparent in the

Figure 7. NDVI harvest data for Vineyard A and

Vineyard B panel sampling blocks for all years

where image capture was possible.

data, structure does appear within elements

of the data sets. This structure highlights the

need for a rigorous statistical and remote

sensing analysis as to whether or not LWMS

has a statistically significant and measurable

impact. As such, caution is required when

considering the information presented here;

longwall mining in the Broke-Fordwich

region is on-going, and the data exploration

presented was based primarily on early data

gathered for longwalls 4 and 5, with

particular attention given to Vineyards A

and B and do not include harvest 2007.

6. Acknowledgements

Funding for this project and post-graduate

scholarship were provided by Beltana

Highwall Mining Pty Ltd. The authors wish

to thank Don Kay of Mine Subsidence

Engineering Consultants, Umwelt

Environmental Consults and the NSW

Department of Environment and

Conservation for providing access to data.

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Figure 3. Bi-variate scatter plots of the Vineyard A and Vineyard B panel sampling data across all years.

Figure 4. Interpolated EM38 soil surveys, collected

during pruning 2004 and 2006. Figure 6. NDVI images generated from satellite

imagery captured at harvest 2004 and 2006.