Object-oriented Change Detection of Riparian Environments from High Spatial Resolution Multi-spectral Images

The objectives of this research were to: (1) develop rule sets in Definiens Developer 7® for mapping and monitoring riparian zone land-cover classes within two QuickBird images; and (2) compare the results of four object-oriented and pixel-based change detection approaches. Two QuickBird images, atmospherically corrected to at-surface reflectance, were captured in May and August 2007 for a savanna woodlands area along Mimosa Creek in Central Queensland, Australia. In-situ vegetation structural measurements and LiDAR data, obtained on 28 May 5 June and 15 July 2007 respectively, were used for calibration and validation. A sequential segmentation routine was applied to enable segmentation of large image datasets. An Isodata unsupervised classification was used for pixel-based classification and rule sets were developed for object-oriented classification of the following land-cover classes: streambed; riparian vegetation; bare ground; rangelands; and woodlands. Four object-oriented and pixel-based change detection routines were applied to the image data: post-classification comparison; image differencing; image regression; and the tasselled cap transformation. The object-oriented classification results showed that objectand class-related features and membership functions could be standardized in the rule sets for classifying the two QuickBird images. Results from the different change detection approaches indicated that post-classification comparison and image differencing produced more accurate results, especially when used together. All four change detection approaches were suited to object-oriented analysis. Advantages of the object-oriented change detection routines included: (1) no need for post-change detection filtering and smoothing; (2) less impact of slight geometric offsets between image datasets; and (3) the ability to include context relationships to improve change detection results.