Mapping Land Use In A Large Agricultural Basin: A Comparison Between Classification Techniques
Free (open access)
D. Ierodiaconou, M. Leblanc, L. Laurenson, F. Stagnitti & V. Versace
In order to facilitate the better management of river basin resources, the Glenelg-Hopkins region in south-east Australia required an accurate and up to date land use map. Land use has a major impact on Australia’s natural resources including its soil, water, flora and fauna and plays a major role in determining basin health. Inappropriate land use and practices have contributed to extensive dryland salinity and water quality problems. Land use data is often required for environmental models and in most cases the reliability of model outputs is dependent on the spatial detail and accuracy of the land use mapping. This paper examines methods to obtain an up to date land use map and a detailed accuracy assessment using Landsat ETM+ data for a regional basin. A multi-source based approach allowed the collection of 4817 ground truth data points from the field investigation. This enabled researchers to (i) incorporate a full range of information into digital image analysis with significant improvements in accuracy and (ii) hold sufficient independent references for an accurate error assessment. Classification accuracy was significantly improved using a stratification design, in which the region is sub-divided into smaller homogenous areas as opposed to a full scene classification technique. The overall classification accuracy was 84% (KHAT= 0.833) for the stratified approach compared to 76% (KHAT= 0.743) for the full scene classification. Effective assessment, planning and management of basins are dependent on a sound knowledge of the distribution and variability of land use. Keywords: image classification, stratification, land use, remote sensing.
image classification, stratification, land use, remote sensing.