The forest products industry is a major component of the economic base for many states in the southeastern United States. Forest inventories that are precise within a 50- to 80-mile mill working circle and the availability of decision support tools for locating mills are of primary importance in attracting and sustaining the industry. This research focuses on the current status of the State of Mississippi's efforts to provide forest inventory information to attract forest industry and balance potentially increased utilization due to new markets with resource sustainability. A pilot study is described that integrates a decision support system (DSS) in a 40-mile radius working circle with a geo-spatially based county-level forest inventory and a transportation network to determine the feasibility and optimal location of a case study Oriented Strand Board (OSB) mill. A linear programming (LP) model was constructed to minimize the cost of procuring and transporting wood to the case study OSB mill site. Net revenue (NR) was calculated to assess financial feasibility of placing the mill at the selected location.

Methods are needed to predict the spatial distribution pattern(s) of forest harvests to improve large-scale timber inventories through the incorporation of spatial distribution information. In this study, land cover type change detection methods were carried out using information from 1972 thru 2005 east-central Mississippi Landsat TM data to detect the spatial distribution of clearcut forest harvests. A random pattern was observed for clearcut harvests and this and other statistical data gathered in the study will be integrated into an existing forest inventory and transportation network database. The resulting harvested and forested area predictions generated by the database will assist with determining the sustainability and availability of forest resources for existing and future forest product mills and the creation of transportation networks needed to supply raw materials and distribute end products. Other implications include assessment of the quality and spatial components of wildlife habitat and a tool for forest certification programs.