Mapping wetlands and potential wetland restoration areas in Black Hawk County, Iowa using object-oriented classification and a GIS-based model

Wetlands are transitional lands between terrestrial and aquatic systems that provide many benefits, including: floodwater retention, non-point pollution treatment, wildlife habitat, and soil-erosion control. Wetlands in Iowa have decreased over 95% in the last 200 years. Therefore, there is a need to map and monitor these resources, as well as to determine potential sites for wetland restoration. In Black Hawk County, wetland maps are outdated, and ground surveys have proved to be too time-consuming and expensive. Traditional pixel-based automated classifiers of remotely-sensed imagery have also proven to be inaccurate in classifying wetlands because of spectral confusion. This study tests multispectral data, hybrid data, hyperspectral data, a seasonal matrix, and a new object-oriented classifier. These are tested against traditional multispectral, pixelbased (ISODATA and Maximum-Likelihood) classifiers both to see if wetland classification accuracies from remotely-sensed imagery can be increased and to produce an updated wetlands map for Black Hawk County. A hyperspectral image of Eddyville, Iowa is tested to evaluate how well wetlands are classified when a hyperspectral image is used with an object-oriented classifier and a hyperspectral pixel-based (Spectral Angle Mapper or SAM) classifier. A GIS-based wetland restoration model is developed to identify potential wetland restoration sites in Black Hawk County. This study shows that the object-oriented classifier is more accurate in identifying wetlands and overall land-cover than pixel-based ones (ISODATA, Maximum-Likelihood, SAM) in both multispectral, hybrid-multispectral, and hyperspectral imagery. The summer/fall seasonal matrix produced unacceptable accuracies. Wetlands in Black Hawk County decreased by 1500 acres (plus or minus an error margin of 375 acres) from 1983 to 2003. The restoration model identified 2,971 acres in Black Hawk County as being highly suitable, 34,307 acres as being moderately suitable, and 121,271 acres as having low suitability for wetland restoration. The results are available at http://gisrl-9.geog.uni.edu/wetland. Limitations of the study include file size when using the object-oriented classifier, image availability for the seasonal matrix, and the number of variables employed in the GIS-based restoration model. The future direction of the study lies in obtaining hyperspectral data for Black Hawk County, more current Landsat multispectral imagery for the seasonal matrix, and testing of more non-parametric classifiers, such as the CART algorithm

A Multi-Agent System framework to support the decision-making in complex real-world domains

The aim of this work was to develop a framework capable of supporting the decision-making process in complex real-world domains, such as environmental, industrial or medical domains using a Multi-Agent approach with Rule-based Reasoning. The validation of the framework was done in the environmental domain, particularly in the area of river basins

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems