Lara A. Arroyo

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.

Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia

This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies.

Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management.

Natura 2000 is a European network of protected areas established under the Habitats Directive (92/43/EEC). According to the Habitats Directive, habitat maps must be periodically updated, which requires the development of cost- and time-efficient mapping practices. In this study, we propose a methodology for habitat mapping using very high spatial resolution (QuickBird) images with Object-Based Image Analysis (OBIA). We classified five segmentation levels: level 5 incorporated the prior knowledge of the study area into the analysis; level 4 and 1 were used to identify arable areas and land covers, respectively. The information contained in levels 1, 4, and 5 was then combined to classify plant species in level 2. Finally, habitats were classified in level 3 using level 2 class-related features. The habitat map obtained had an overall accuracy of 86.3 percent. Classification accuracies were higher for tree-and pasture-dominated habitats than for shrub-dominated habitats.

Cost-Effectiveness of Seven Approaches to Map Vegetation Communities — A Case Study from Northern Australia’s Tropical Savannas

Vegetation communities are traditionally mapped from aerial photography interpretation. Other semi-automated methods include pixel- and object-based image analysis. While these methods have been used for decades, there is a lack of comparative research. We evaluated the cost-effectiveness of seven approaches to map vegetation communities in a northern Australia’s tropical savanna environment. The seven approaches included: (1). aerial photography interpretation, (2). pixel-based image-only classification (Maximum Likelihood Classifier), (3). pixel-based integrated classification (Maximum Likelihood Classifier), (4). object-based image-only classification (nearest neighbor classifier), (5). object-based integrated classification (nearest neighbor classifier), (6). object-based image-only classification (step-wise ruleset), and (7). object-based integrated classification (step-wise ruleset). Approach 1 was applied to 1:50,000 aerial photography and approaches 2–7 were applied to SPOT5 and Landsat5 TM multispectral data. The integrated approaches (3, 5 and 7) included ancillary data (a digital elevation model, slope model, normalized difference vegetation index and hydrology information). The cost-effectiveness was assessed taking into consideration the accuracy and costs associated with each classification approach and image dataset. Accuracy was assessed in terms of overall accuracy and the costs were evaluated using four main components: field data acquisition and preparation, image data acquisition and preparation, image classification and accuracy assessment. Overall accuracy ranged from 28%, for the image-only pixel-based approach, to 67% for the aerial photography interpretation, while total costs ranged from AU

Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas.

338,000 to AU

Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

388,180 (Australian dollars), for the pixel-based image-only classification and aerial photography interpretation respectively. The most labor-intensive component was field data acquisition and preparation, followed by image data acquisition and preparation, classification and accuracy assessment.