Tropical savannas are of great importance to tropical regions
and are of global significance due to their impact on the global
carbon balance. The use of Synthetic Aperture Radar (SAR) can add
to the benefit of established remote sensing techniques by allowing
wet season monitoring and by providing additional information
related to the vegetation structure. This research project has
provided a methodology for the utilization of SAR data for land
cover mapping. The major component of this methodology lies in a
procedure that allows correction of SAR data for variability caused
by the imaging geometry. It has been shown that detailed land cover
information can be extracted from data corrected in this manner
using standard image processing algorithms. This methodological
framework should prove useful in widening the user base of SAR data
due to the reduction in specialised software and expertise
The aim of this research project was to develop suitable image
processing techniques for multi-frequency polarimetric AirSAR data
to characterise the vegetation communities of the tropical savannas
in northern Australia. Major steps have been taken that overcome
some of the obstacles inherent to such data products. Currently,
analysis of such data is severely restricted due mainly to the
phenomena of speckle and variation of backscatter signatures which
are dependent on the varying incidence angles.
This research project focused on the use of AirSAR data for
estimating biophysical parameters and achieving a satisfactory
land-cover classification of vegetation communities in the study
area developing methodologies appropriate to this data product.
Firstly, the problem of speckle was addressed by investigating the
optimum spatial resolution for discriminating the land cover and
thus establishing an appropriate filter to use for the suppression
of speckle. Secondly, a new method to correct for the effect of
incidence angle variation was developed. These two steps were
instrumental to assess the correlation between bio-physical
parameters of woody vegetation and the AirSAR data, and for the
development of a classification methodology that can be implemented
using standard image processing software.
The investigation of the optimal resolution for the delineation
of vegetation communities in the study area was found to be between
20 and 27metres. The resultant averaging process to reduce the data
product to this resolution has been shown to have the additional
benefit of substantially reducing the effect of speckle. A new
method developed for this project allowed the reduction of the
effect of variation in incidence angle. The correction method
developed applies a histogram equalization to the lines of constant
incidence angle and was shown to be effective through an evaluation
against an existing land cover classification of the study
No empirical evidence was found for a relationship between above
ground woody biomass and AirSAR backscatter. However, the
land-cover classification of the AirSAR data was successfully
implemented using standard image processing techniques.
Differentiation between species and tree density was possible where
there is a significant difference in the vegetation structure. The
classification accuracy is very similar to that achieved by a land
cover classification using Landsat TM data.
Floodplain vegetation and the pine plantation, were two cover
types for which the Landsat data registered a low classification
accuracy due to the overlap with grassland and woodland communities
respectively. A synergism of optical and AirSAR data has the
potential to produce a more detailed and more accurate
classification than either data set on its own.
The use of Synthetic Aperture Radar (SAR) to map the forests and
woodlands and to monitor the long-term developments in the tropical
savannas of northern Australia is desirable because large area
coverage can be achieved at regular intervals at relatively low
SAR is superior to optical remotely sensed data because of its
ability to penetrate cloud cover, thus making resource monitoring
during the wet season possible. Furthermore, SAR is an additional
source of information on vegetation as the recorded data is
determined by the structure of the vegetation and underlying ground
surface rather than the chemical properties determining the optical
The potential of SAR, however, is dependent on the unravelling
of the relationship that exists between radar backscatter
characteristics, surface conditions and physical characteristics of
forest and woodlands in this particular environment.
During this study, the optimum resolution for studying
vegetation communities was determined with issues of scale
clarified. A new algorithm was developed that allowed the
correction of the SAR data for the effect of changes in local
incidence angle. In consequence, promising results in ground cover
discrimination were achieved.
Menges, C. H., Hill, G. J. E., and Ahmad, W.
(in press), 'Use of Airborne Video Data for the Characterisation of
Tropical Savannas in Northern Australia: The Optimal Spatial
Resolution for Remote Sensing Applications', International Journal
of Remote Sensing.
Menges, C.H ., van Zyl, J. J., Ahmad, W., and
Hill, G.J.E., (in press), 'A Procedure for the Correction of the
Effect of Variation in Local Incidence Angle on AIRSAR Data',
International Journal of Remote Sensing. Menges, C., Hill, G. J.
E., Ahmad, W., and van Zyl, J. J., (in press), 'Incidence Angle
Correction of AirSAR Data to Facilitate Land Cover Classification',
Photogrammetric Engineering & Remote Sensing.
Phinn, S.R., Menges, C., Hill, G.J.E. and
Stanford, M. (in press), 'Optimising remotely sensed solutions for
monitoring, modelling and managing coastal environments', Remote
Sensing of Environment.
Conference and Workshop Proceedings
Bartolo, R., Menges, C.H., (2000), 'Wetland
mapping in northern Australia using SAR', Proceedings of the Sixth
International Conference Remote Sensing for Marine and Coastal
Environments, Charleston, South Carolina, USA, 1-3 May.
Menges, C.H ., Bell, D., Van Zyl, J. J.,
Ahmad, W., and Hill, G.J.E., (in press), 'Image classification of
AirSAR data to delineate vegetation communities in the tropical
savannas of northern Australia', Proceedings of the 10th
Australasian Remote Sensing Photogrammetry Conference, Adelaide,
Menges, C.H ., Van Zyl, J. J., Ahmad, W., and
Hill, G.J.E., (1999), Classification of vegetation communities in
the tropical savannas of northern Australia using AirSAR data.
Pacific Rim AIRSAR Significant Results and Planning Workshop, Maui,
Hawaii, 24-26 August.
Menges, C.H ., O'Grady, A., and Ahmad, W.,
(1999), Relationships between biophysical parameters and
polarimetric AirSAR data in the tropical savannas of northern
Australia. Pacific Rim AIRSAR Significant Results and Planning
Workshop, Maui, Hawaii, 24-26 August.
Menges, C., Ahmad, W., Hill, G. J. E., and van
Zyl, J. J., (1999), 'Evaluation of AirSAR data for the
Classification of Vegetation Communities in the Tropical Savannas
of Northern Australia', in NARGIS, Darwin, Australia, 28-30th
Menges C. H., Crerar, J., Hill G. J. E., and
Ahmad W., (1998). 'A Method for Estimating the Effect of Variation
in Local Incidence Angle on AIRSAR Data'. Proceedings of the 9th
Australasian Remote Sensing Photogrammetry Conference, Sydney
Australia, 20-24 July, CD-ROM.
Ahmad W., vanZyl, J.J., Menges, C., O'Grady,
A., and Hill, G.J.E., (1998), Preliminary results of supervised and
unsupervised AIRSAR data classification techniques in the tropical
svannas of northern Australia, PACRIM Airborne Synthetic Aperture
Radar (AIRSAR) Workshop, Sydney, 26-28 July.
Phinn, S.R. , Menges , C. , Hill, G.J.E. and
Stanford, M. (1998). 'Optimising remotely sensed solutions for
monitoring, modelling and managing coastal environments',
Proceedings of the 5th International Conference on Remote Sensing
for Marine and Coastal Environments, San
Diego, October 5-7, pages, I:190-197.