TS-CRC Student project - Application of radar remote sensing in the Northern Territory of Australia

Northern Territory University, Darwin: Completed

Carl Menges

Summary | Aims | Biophysical parameters and land-cover classification | Optimal resolution | Differentiation between species and tree density | Synthetic Aperture Radar | Application | Publications and presentations | More information |

Summary

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 required.

Aims

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.

Biophysical parameters and land-cover classification

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.

Optimal resolution

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 area.

Differentiation between species and tree density

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.

Synthetic Aperture Radar

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 cost.

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 response.

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.

Application

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.

Publications and presentations

Journal Publications

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, Australia.

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 June.

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.

Contacts

Dr Carl Menges

Faculty of SITE, Bldg 18
DARWIN, NT