Extracting Terrain Categories from Multi-Source Satellite Imagery

Abstract

Geoscience Australia has ben conducting a series of national risk assessments for a range of natural hazards such as severe winds. The impact of severe wind varies considerably between equivalent structures located at different sites due to local roughness of the upwind terrain, shielding provided by upwind structures and topographic factors. Terrain surface roughness information is a critical spatial input to generate wind multipliers. It is generally the first spatial field to be evaluated, as it is utilised in both the generation of the terrain and topographic wind multiplier. Landsat imagery was employed to generate a terrain surface roughness product for six major metropolitan areas across Australia. It was necessary to investigate the applicability of multi-sensor approaches to generate a regional or national terrain surface roughness map based on the Australian-New Zealand wind loading standard. This paper presents a methodology to derive terrain surface roughness from various multi-source satellite images. MODIS, Landsat and IKONOS imagery were acquired during 12 September – 26 November 2002 covering a significant portion of New South Wales, Australia. An object-based image segmentation and classification technique was tested for seven bands of MODIS, six bands of Landsat Thematic Mapper, and four bands of IKONOS. Eleven terrain categories were identified using this technique which achieved classification accuracies of 79 percent and 93 percent over metropolitan Sydney and rural or urban areas respectively. It was revealed that the object-based image classification enhances the quality of the terrain product compared to traditional spectral-based maximum likelihood classification methods. To further improve the derivation of terrain roughness classification results, an integrated textural-spectral analysis merged Synthetic Aperture Radar and optical datasets. A comparison with results derived from textural-spectral classification showed considerable improvement over the results from earlier classification techniques.