An automated image analysis approach for classification and mapping of woody vegetation from digital aerial photograph

This paper presents a recent study on woody vegetation delineation and mapping using digital aerial photograph and geographic information system (GIS) in Hunter Region, Australia. The aim of the study was to develop automated and repeatable digital image processing methods for woody vegetation classification and mapping using aerial photograph or high-resolution satellite images and GIS. Parallelepiped classification or density slice method was used to classify woody and non-woody vegetation, and ancillary GIS data were used as quality controls in the classification processing. Specific scripts were developed for automated image processing in a GIS environment. The classification accuracy was assessed against traditional aerial photograph interpretation using adequate random points. The automated process reached an overall classification accuracy of 94% and 97% after post-classification correction. The automated approach can be applied to any other type of high-resolution imagery such as SPOT 5, ALOS, IKONOS and QuickBird images.