Knowledge acquisition is concerned with finding and structuring knowledge in such a way that it can be used in a variety of intelligent decision-making tools. Knowledge of a domain can be encoded as taxonomy i.e., a hierarchically organized set of categories. The relationships within the hierarchy can be of different kinds, depending on the application, and a typical taxonomy includes several different kinds of relations. Thus taxonomies play an important role in analyzing and modeling knowledge. The focus of this study is to derive knowledge from a standard taxonomic structure in the remote sensing domain. The various methodological channels adopted by the remote sensing data analysts to produce different information products normally go through some definite processes, which can be examined along with their context (spectral, spatial, temporal) by the taxonomical approach. This allows users to assess the applicability of a methodology for a particular area of interest and also has the advantage in aiding the upper-level decision-makers in understanding why different approaches might provide different outputs to the same source data. Some of the previous work done by a number of multi disciplinary researchers in analyzing remote sensing data has been used in this study to examine the structure of their methodologies from a taxonomical perspective. The analysis of the developed taxonomies clearly indicates a definite structure to the underlying analysis procedures and has potential for the development of systems to automate them.

Although remote sensing has been used in predicting, monitoring, and assessing hazards and disasters for over 50 years, its use in the transportation domain is still in its infancy. This study was conducted to identify the research needs involving the use of remote sensing for such applications within the transportation domain. The first step taken was to determine the current state of remote sensing applications in the transportation domain associated with the prediction, monitor, and assessment of hazards and disasters. The second step was to identify the impacts that such events may cause and the information needed to prevent or reduce their impacts. With the knowledge of the required information, remote sensing requirements and technology limitations were defined. Then according to the knowledge of the current state of research and the limitations of remote systems, future research needs were identified. Finally, the Analytic Hierarchy Process (AHP) was used to rank these research needs.

Invasive weeds are becoming a greater problem throughout the southeastern United States, which calls for drastic means of location, classification, and management in order to halt these undesirable invasions. Three experiments were initiated in 2005 and 2006, two of which were to examine the effects of additives, NIS, Dyne-a-Pak, AMS, and Valor used in cogongrass [Imperata cylindrica L. Beauve] control and the other to evaluate the effectiveness of a ropewick applicator applied to established cogongrass in juvenile longleaf pine stands. Through these experiments, it was found that Dyne-a-Pak can provide greater enhancement in cogongrass control compared to non ionic surfactant, and that imazapyr can provide greater control than glyphosate when applied to cogongrass. The ropewick applicator was found to be an effective application technique for selectively controlling cogongrass. A fourth experiment was initiated in 2005 to evaluate the application of remote sensing techniques in classifying cogongrass from other vegetations along Mississippi highway rights-of-ways using high spatial resolution multispectral aerial imagery. Results from this study indicate that supervised and unsupervised classification techniques can successfully identify cogongrass along highway rights-of-ways.

Tropical cyclones in the Atlantic basin often undergo a process called extratropical transition (ET) and transform from warm-core to cold-core systems while retaining strong winds, heavy rainfall, and large ocean waves. Infrared satellite imagery from channels 2 and 4 of the Geostationary Operational Environmental Satellite (GOES) were used to examine key structural changes, synoptic interactions, and loss of deep centered convection in order to determine onset and completion of ET. The primary indicator for ET onset in 75% of cases was found to be a persistent increase in storm asymmetry along with the appearance of warm frontogenesis in its northern region. Cold frontogenesis in the southern portion of the storm was the secondary indicator for declaring onset of ET. Completion of ET was marked by the loss of centered deep convection for all cases. The average ET transition time was 18 hours for 60% of the cases.

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