The presence and spread of invasive tree species have caused great ecological and economic damages. Previous studies usually ignored the role of socioeconomic factors and seldom treated presence and abundance as different phenomena. Using Classification and Regression Trees (CART) analysis, important driving factors affecting the presence and abundance of invasive tree species in Mississippi were identified. Then these selected important factors were spatially analyzed using a spatial lag model at the plot and county levels. The empirical results from the spatial lag model showed that: 1) presence was associated with elevation, ownership, population density and per capita annual income; 2) abundance was related to stand age, elevation, growing stock and per capita annual income. These findings suggested that socioeconomic factors besides ecological factors played a significant role and factors affecting the presence and abundance were different. Thus, management prescriptions to monitor and control invasions should depend on difference factors.

In this research, the fire effects on structural and compositional change, and advance regeneration of oak forests in the Ozarks of Missouri were investigated by combining the statistic methods of MANONA, survival analysis, CART analysis, and logistic analysis. Results indicated that fire treatments significantly reduced the midsotry and understory basal area and stem density. However, fire effects on overstory tree survival differentiated among size classes. A new morphological variable, ratio of the total height to the square of basal diameter, was found to be statistically significantly related to the tree mortality rate for most of the species. The developed logistic regression models for selected species using the morphological variable well simulated the impact of initial stem size of advance regeneration on mortality for most of the species. The resultant logistic regression models could be a potential tool to compare and quantify species response to fires on a comparable basis.

Video image detection system (VDS) equipment provides real-time traffic data for monitored highways directly to the traffic management center (TMC) of the Georgia Department of Transportation. However, at any given time, approximately 30 to 35% of the 1,600 camera stations (STNs) fail to work properly. The main reasons for malfunctions in the VDS system include long term road construction activity and operational limitations. Thus, providing alternative data sources for offline VDS stations and developing tools that can help detect problems with VDS stations can facilitate the successful operation of the TMC. To estimate the travel speed of non-working STNs, this research examined global positioning system (GPS) data from vehicles using the ATMS-monitored freeway system as a potential alternative measure to VDS. The goal of this study is to compare VDS speed data for the estimation of the travel speed on freeways with GPS-equipped vehicle trip data, and to assess the differences between these measurements as a potential function of traffic and roadway conditions, environmental, conditions, and driver/vehicle characteristics. The difference between GPS and VDS speeds is affected by various factors such as congestion level (expressed as level of service), onroad truck percentage, facility design (number of lanes and freeway sub-type), posted speed limit, weather, daylight, and time of day. The relationship between monitored speed difference and congestion level was particularly large and was observed to interact with most other factors. Classification and regression tree (CART) analysis results indicated that driver age was the most relevant variable in explaining variation for the southbound of freeway dataset and freeway sub-type, speed limit, driver age, and number of lane were the most influential variables for the northbound of freeway dataset. The combination of several variables had significant contribution in the reduction of the deviation for both the northbound and the southbound dataset. Although this study identifies potential relationships between speed difference and various factors, the results of the CART analysis should be considered with the driver sample size to yield statistically significant results. Expanded sampling with larger number of drivers would enrich this study results.