2. METHODS
2.1 Focal Species
Buffel grass (Cenchrus ciliaris) is a C4 African perennial
tussock grass hailed for its resistance to drought and
heavy grazing in sub-tropical semi-arid rangelands
throughout Australia and the Americas. However, it can
rapidly invade non-target environments, increasing the
frequency and intensity of wildfire threatening
biodiversity conservation as well as residential areas.
Efforts are now being made to prevent further spread of
this highly invasive and versatile species. Buffel grass
has highly varied morphological and physiological
characteristics. It spreads by seed both sexually and
asexually and vegetatively via rhizomes and stolons. The
result of this is that Buffel grass has a range of forms and
can be observed growing in dense monotypic stands as
well as small clumps and lone tussocks throughout the
landscape. Individual tussocks can live up to 20 years,
reaching heights of between 20 — 150cm and produce
inflorescence ranging in colour from beige to dark
purple. Older plants tend to have a less vibrantly green
leaves and typically hold dead leaf at the base of the
tussock (Figure 1).
Figure 1: LEFT Juvenile plant growing in creek line;
RIGHT mature plant growing at roadside
(Photographs by Victoria Marshall, northern South
Australia)
2.2 Study area
Our study site is a 10*10 km area located one kilometre
west of Alice Springs, in central Australia (Figure 2).
Selected to represent the great diversity of landscapes
present in central Australia, the scene covers 3-4 peaks of
the MacDonnell Ranges, the townships of Larapinta,
pastoral leases, dry creeks and wildlife protected areas.
Vegetation types which dominate the scene include
Witchetty Bush or Mulga woodlands, Ironwood Acacia
woodlands and Spinifex grasslands. This sub-tropical
arid region typically receives sporadic summer rains
which can support dense infestations of Buffel grass. The
grass was sown in and around Alice Springs Airport
(Figure 2) in the early 1970's to prevent dust storms, and
has spread out into the neighbouring regions. Locals have
a strong understanding of its presence in the highly
varied landscape. In this areas there are known dense
infestations along watercourse, associated alluvial soils
and roadsides; sparse infestation on foot hills, becoming
sparser as it moves further up the hill face; fire affected
regions where Buffel grass is emerging first on ash beds
as well as protected sites where the grass is actively
controlled.
a “Ground validation sites
Prodiond by Victarta Marstail. The Univeisity of Adelaide
Figure 2: Study site 1km west of Alice Springs in central
Australia. Worldview-2 imagery displayed in true colour
covering the total extent of the study site. Ground
validation sites are marked (black squares).
2.3 Image acquisition and pre-processing
The imagery was acquired on the 22 January 2011 in
the middle of the wet season following approximately
80mm of rain over the preceding month. The region had
also been exposed to record high volumes of rain
between August and November 2010 due to tropical
cyclones and floods in Northern Australia. Thus, high
densities of all ephemeral plants were expected. The
cloud-free imagery was captured at 1:30 in the afternoon
at an off-nadir angle of 13 degrees. We corrected for
atmospheric effects using Fast- — Line-of-sight
Atmospheric Analysis of Spectral Hypercubes
(FLAASH) in ENVI 4.8. A Mid-Latitude Summer
Atmospheric Model, Rural Aerosol Model and an Initial
Visibility of 40km were applied. The Zenith Angle was
calculated by taking the mean-off-nadir angle from 180.
2.4 Image segmentation: Extracting vegetation
Normalised Difference Vegetation Index (NDVI) was
applied to the image based on Worldview-2 Red and
NIR-2 bands. We adopted this traditional approach to
vegetation extraction in response to the high levels of
photosynthetically active vegetation present in the
region. Based on NDVI values, a generous mask (NDVI
values: 0.3- 2.5) was applied to extract vegetation
components of land cover from the image.
2.5 Principle Component Analysis
To identify the key factors contributing to variation in the
image a principal component analysis (PCA) was
applied. This analysis linearly transforms correlated
bands into uncorrelated components that represent
variation in the data, reducing the redundancy in the data,
while retaining all 8 bands. The result in this case is 8
principal components, where the first three components
represent approximately 9596 of the variation in the
image (Figure 3). Based on eigenvectors we can see that
principal component (PC) one, represents overall albedo,
PC-2 captures green vegetation and PC-3 captures areas
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