Christian Witte

Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery

The objectives of this research were to (a) develop a geo-object-based classification system for accurately mapping riparian land-cover classes for two QuickBird images, and (b) compare change maps derived from geo-object-based and per-pixel inputs used in three change detection techniques. The change detection techniques included post-classification comparison, image differencing, and the tasseled cap transformation. 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. Concurrent in-situ land-cover identification and lidar data were used for calibration and validation. The geo-object-based classification results showed that the use of class-related features and membership functions could be standardized for classifying the two QuickBird images. The geo-object-based inputs provided more accurate change detection results than those derived from the pixel-based inputs, as the geo-object-based approach reduced mis-registration and shadowing effects and allowed inclusion of context relationships.

Assessment and monitoring of foliage projected cover and canopy height across native vegetation in Queensland, Australia, using laser profiler data

The aim of this project was to demonstrate the potential of laser profiling for monitoring forest structure. Data were captured from flights at 30, 60, and 100 m above the canopy over three study sites in south-east Queensland at regular intervals over a 2-year period. Field measurements of foliage projected cover (FPC) and tree height were found to be highly correlated with laser derived estimates (R2 from 0.91 to 0.95). Monitoring of changes in FPC and tree height, as a result of logging or growth, was also successful. Tree heights, in particular, were measured accurately over time (residual standard error (RSE) of 0.45 m). The large RSE of the FPC model (from 5.7% to 7.3% FPC) means that subtle changes, such as seasonal variation, may be difficult to monitor. Flying height was found to be a significant explanatory variable in estimating field FPC. A transect of greater than 1000 km was also flown in a single helicopter pass to assess the technology over a range of forest types. Field measurements of FPC were collected for 21 sites along this transect. Strong relationships were observed between laser and field FPC, but these varied with forest type.

Mapping banana plantations from object-oriented classification of SPOT-5 imagery.

The objectives of this research were to develop and evaluate on approach for object-oriented mopping of banana plantations from SPOT-5 imagery, and to compare these results to banana plantations manually delineated from high spatial resolution airborne imagery. Cultivated areas were first identified through large spatial scale mapping using spectral and elevation data. Within the cultivated areas, separation of banana plantations and other land-cover classes increased when including image co-occurrence texture measures and context relationships in addition to spectral information. The results showed that a pixel size of <= 2.5 in was required to accurately identify the row structure within banana plantations, which enabled object-based separation from other crops based on texture information. The users and producers accuracies for mapping banana plantations increased from 73 percent and 77 percent, respectively, to 94 percent and 93 Percent after post-classification visual editing. The results indicate that the data and processing techniques used offer a reliable approach for mapping banana plants and other plantation crops.

Investigating the Potential for Mapping Fallow Management Practises Using MODIS Image Data

The objective of this study was to investigate the potential for mapping fallow land management practices on local farm scale in Southern Queensland, Australia, using high temporal frequency satellite remote sensing over a period of six years. The Moderate Resolution Imaging Spectroradiometer (MODIS) was chosen as it provides a temporal resolution fine enough to detect ground cover change within cropping cycles (fallow periods). Previous studies have successfully employed MODIS data detecting cropping patterns in Kansas, North America and Northern Kazakhstan.