This study aimed to detect and understand remotely sensed urban wetland dynamics as a sensitive indicator of the combined effects of human disturbances and climate impacts in the course of global change. To address this objective, the study developed technical approaches to detect and interpret wetland changes across spatial scales in complex urban landscapes. Using a series of Satellite Pour l'Observation de la Terre (SPOT) images covering 1992-2010, the study was conducted in the Kansas City metropolitan area of the USA, which has experienced significant urban sprawl in recent decades. As a fine-tuning of the traditional supervised image classification, a knowledge-based classification algorithm was developed to identify fine-scale, hidden wetlands that cannot be appropriately detected based on their spectral differentiability. The analyses of wetland change were implemented at the metropolitan, watershed, and sub-watershed scales as well as being based on the size of surface water bodies in order to reveal real pictures of urban wetland change trends in relation to major driving factors. The results of the study indicated that the knowledge-based classification approach improved the detection capability and accuracy of urban wetlands by finetuning the traditional classification results. The cross-scale analysis of detected land covers revealed that wetland dynamics varied in trend and magnitude from metropolitan, watersheds, to sub-watershed scales. The study found that increased precipitation swelled wetlands, which inflated the findings of remotely sensed wetland cover and related trend interpretation. During an 18 year study period, human development activities in the study area resulted in a large increase in impervious surfaces, which was mainly at the expense of farmland/grassland areas and some small wetlands in all urban watersheds. In contrast, increased precipitation in the region swelled large wetlands in particular. This mixed picture of urban wetland dynamics, associated with the analysis of underlying driving factors, provides a new baseline for relevant urban planning, management, and research in a global change perspective.

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Tracer analysis is commonly used to evaluate the hydraulics of environmental and chemical engineering systems. The traditional tracer analysis is conducted through physical experiments that are usually complex, costly, time-intensive, and may be impractical. Because of the continued advancement of computing technology, computational fluid dynamics (CFD) has demonstrated its applicability in simulating tracer transport. CFD can provide advantages, including no interruptions of existing water treatment process, no impacts of background concentration, and a relatively low cost. However, no reports have quantitatively studied the cost that CFD can save on tracer analysis. This study first proved the accuracy of CFD tracer analysis for an existing ozone disinfection tank and then compared the economic expenses and environmental impacts of CFD tracer analysis with those of a field tracer analysis. It was found that CFD-based tracer analysis has accuracy on par with the physical-based study but at relatively low economic cost and environmental impacts. K E Y W O R D S computational fluid dynamics, cost, environmental impacts, tracer study

It has been a challenge to accurately detect urban wetlands with remotely sensed data by means of pixel-based image classification. This technical difficulty results mainly from inadequate spatial resolutions of satellite imagery, spectral similarities between urban wetlands and adjacent land covers, and spatial complexity of wetlands in human transformed, heterogeneous urban landscapes. To address this issue, an image classification approach has been developed to improve the mapping accuracy of urban wetlands by integrating the pixel-based classification with a knowledge-based algorithm. The algorithm includes a set of decision rules of identifying wetland cover in relation to their elevation, spatial adjacencies, habitat conditions, hydro-geomorphological characteristics, and relevant geo-statistics. ERDAS Imagine software was used to develop the knowledge base and implement the classification. The study area is the metropolitan region of Kansas City, USA. SPOT satellite images of 1992, 2008, and 2010 were classified into four classes -wetland, farmland, built-up land, and forestland. The results suggest that the knowledge-based image classification approach can enhance urban wetland detection capabilities and classification accuracies with remotely sensed satellite imagery.