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 pixeland 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 OPEN ACCESS Remote Sens. 2013, 5 378 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$338,000 to AU$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.