The field strength prediction chart is a set of statistical curves obtained through the analysis of huge amount of field strength measurement data of the specific radio band in some area. It reflects the natural or artificial effects such as geography, atmospheric condition and buildings, etc. that affect the radio wave propagation. One advantage is that we can predict the rough relationship between the field strength and distance easily. As a result, we don¡¦t have to perform simulation field measurement in every radio planning. With prediction chart and field strength interference /protection ratio standard, we can suggest a minimum distance separation criterion between co-channel and adjacent channel broadcasting stations. It also provides a reference to authority to examine the broadcasting service application. The FCC develops the F(50,50) charts and minimum separation between radio stations base on data collected in the U.S.. Presently, the regulations concerning the broadcasting applications in Taiwan still follow the FCC¡¦s suggestion. In general, the field strength distribution is affected by two main factors: geography and atmospheric condition, which can be different from those in the U.S.. With the acquisition of digital terrain data of Taiwan, the terrain profile for a given path can be generated. In this thesis, we¡¦ll use Deygout model and the database of existed broadcasting stations to generate field strength distribution database for each station and analyze the database to develop the prediction chart that is suitable for the propagation environment in Taiwan. When combine with the field strength interference /protection ratio standard, we¡¦ll provide a minimum distance separation criterion of co-channel and adjacent channel in the FM band broadcasting stations. Our study can help the authority to achieve the most effective spectrum management in FM band.

Limited knowledge exists of the terrain variables that have an influence on gopher tortoise (Gopherus polyphemus) burrow locations. Previous studies suggest that terrain features may play a role in preference of burrow location. LiDAR- (Light Detection and Ranging) derived terrain features can be evaluated through GIS (Geographic Information System) analysis at a fine spatial scale. LiDAR data acquired at 0.5 meter post spacing over three locations on Camp Shelby Joint Forces Training Center, MS were used to develop DTMs (Digital Terrain Models) for use in burrow site characterization. Terrain variables (e.g. elevation, slope, aspect) were developed from the LiDAR DTM in ArcGIS. Burrows and randomly allocated non-burrow points were used in logistic regression analysis to model the relationship between burrow occurrence and terrain features. Four models correctly classified more than 83% of the burrow locations. The R2 were 34.83%, 49.31%, 28.09%, and 31.51%.