Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
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This paper presents a pilot study on riparian vegetation delineation and mapping using remote sensing and geographic information systems (GIS) in the Hunter Region, Australia. The aim of the study was to develop appropriate and repeatable assessment and mapping techniques to quantify the extent of riparian vegetation within the region. Ortho-rectified digital aerial photographs, SPOT-4 multispectral (XS) and Landsat-7 ETM+ images were tested to delineate the riparian vegetation and to develop a quantifiable and repeatable method of mapping the extent of that vegetation. Image processing techniques such as parallelepiped classification, tasselled cap transformation, and vegetation index clustering were used in an attempt to delineate riparian vegetation from remotely sensed images. Specific GIS analysis techniques were used for riparian zone buffering and segmentation, vegetation cover estimation, and mapping. Specific GIS scripts were developed for those processes so that they were automatic, fast, and repeatable. Various vegetation indices (VI) were evaluated and compared for their ability to discriminate riparian vegetation and its extent. The riparian vegetation was assessed and mapped at designed segmental interval (e.g. 1 km) with polygon and lineal representations. The classification accuracy was assessed against field observation and air photo interpretation (API). The overall accuracy of the photo-based classification was about 81%, SPOT-4 63%, and Landsat-7 ETM+ 53%. Statistic analysis shows that there is little agreement between photo-based classification and that from satellite imagery.