Charles Darwin University
Objectives | Research
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Prime site for fire: understorey grass in the
savannas of Yinberrie Hills, 50 km north-west of Katherine in the
Photo: Zhang Yue
Many savanna birds dependent on grass seeds have declined in
distribution and abundance. These declines often appear to have
preceded extensive disturbance such as land clearing, suggesting
that changes in the condition of grasslands began with extensive
pastoralism. Possible sources of change include the direct impacts
of grazing, as well as alteration of fire regimes to favour
Other work supported by the TS-CRC indicates that the early wet
season is a period of resource shortage, when many graminivorous
birds are dependent on a small suite of perennial grasses that are
patchily distributed in the landscape. Important species include
Chrysopogon fallax and Alloteropsis semialata .
Within seasons, disturbances such as fire affect both the size of
seed crops and the timing of their production, and over the
longer-term influence the distribution and abundance of the
This project was designed to describe the configuration and
quality of perennial grass patches needed to sustain regional
graminivore populations, to identify the landscape and management
determinants of that patterning, and to develop a model for
grassland management to maintain graminivore populations. It
included sites with varying degrees of grazing pressure.
Specific objectives were to:
- Employ existing data to characterise grasslands used by
graminivores in terms of resource density, patch configuration, and
position in the landscape.
- Develop a GIS-based model of the correlates of distribution and
abundance of favourable grassland patches using available themes
including vegetation/land system/land units, soils, geology, DEM,
and other (Auslig) topographic data.
- Relate ground-based measures of grassland pattern to
remotely-sensed images of landscape pattern. Integrate
classifications with GIS model.
- Assemble fire history of study sites used for characterisation
of favourable patches, using methodologies developed by the NT
Bushfires Council Fire Ecology group.
- Predict location of favourable grassland patterning in the
landscape with and without reference to fire history.
- Conduct ground surveys of vegetation and grassland patterning
to verify model predictions and explore implications of different
- Conduct ground surveys of graminivorous birds using the site
during the wet season.
- Relate results of avifaunal surveys to fire history and
landscape context. Identify additional landscape elements with
which different taxa are strongly associated.
- Develop GIS-based models to identify additional sites at which
particular taxa are expected to occur. Verify against existing
records of avifaunal distribution and design and perform surveys to
- Develop models for management of graminivore habitat through
the appropriate use of fire at a landscape scale.
- Integrate outputs with models of landscape function developed
in pastoral landscapes.
An operational method to map bush fire history with these
Landsat TM data was developed. This interpretation approach is
practicable for the use of large numbers of images by which a
multi-yearly fire history wildfire mapping and spatial analysis can
With the visible red, NIR and MIR bands, an unsupervised digital
image classification was carried out to delineate the burnt
patches. These patches were labelled by using field knowledge as
well as by on-screen assessment of the raw data and signature files
for previously confirmed fire scars.
Spectral overlap between fire scars, water bodies, shadows and
miscellaneous geological features was observed and was eliminated
by using of a binary spatial mask and the Digital Elevation Model
The validity of the fire mapping was assessed with the help of
field data as well as using a high spatial resolution IKONOS image
acquired at the time of the TM data recording in 2000. Preliminary
analyses showed accuracy rates of fire mapping in the image of 2000
were 91 per cent and 94 per cent, according to field data and
IKONOS data, thus the validation of the method was proved to be
Thirty-two Landsat TM images were offered by the Parks &
Wildlife Commission of the NT through the TS-CRC in August 1999 and
a number of rectification procedures for the images were carried
out. This data set crosses 11 years but unfortunately in some years
there are just one scene available so that this mapping result
still need to be enhanced by acquiring required images in certain
Vegetation mapping was also carried out using a new approach.
One Landsat TM image acquired in March 1997 was used to implement
vegetation interpretation with red, NIF and MIF bands. To simplify
the complexity of tropical savanna landscape characteristics in the
study area—such as very small areas of vegetation or
geological distribution that differs from the types around
them—a statistical filter was used to merge those pixel
assemblages with the pixels around them.
A supervised classification with maximum likelihood algorithm
was carried out using training data that included ground data
collected within the study area, hard-copy vegetation maps and
digitised vegetation maps carried out by the NT Department of
Lands, Planning & Environment (NTDLPE).
Seven classes of vegetation types were mapped with the
resolution of Landsat data, but this should be significant enough
for a fire history analysis to quantify fire-sensitive vegetation
cover both temporally and spatially.
The digital vegetation data of study area in the format of
ArcView shape file was provided by NTDLPE, which divides the
vegetation of the study area into 17 types of communities. Except
for training signatures for supervised classification, the spatial
modelling method included in ArcView Spatial Analyst can be
implemented directly to delineate the fire influences on the
different covers of vegetation types.
To provide a preliminarily analysis of the spatial changes to
pattern in this landscape during the fire season, binary grid fire
maps were generated in a GIS and these were used to compute spatial
pattern indices: number of patches, mean patch size, mean shape
index, and mean patch fractal dimension. The fluctuations of these
indices illustrate the changes in spatial patterning of vegetation
in a savanna landscape that is significantly affected by dry-season
The implementation of tropical wildfire mapping, vegetation
mapping and spatial pattern analysis will be used as the basic data
stream for habitat analysis and modelling of graminivores.