Land use and land cover change analyses are important tools for planning and development decisions. Tropical deforestation has both local and global implications. One main reason for deforestation is the conversion of forest to agricultural land. This study explores influences and potential causes for agricultural expansion and deforestation within the Toledo District in southern Belize, Central America. Many factors play into the deforestation and degradation of tropical forests in this district, including social, cultural, political and economic issues, all of which need serious consideration if planners and politicians are to combat the problem. Understanding the reasons for deforestation goes hand in hand with knowing where the deforestation is occurring. Knowing where and why will aid in knowing how to focus policies to prevent or control the deforestation. Conversely, looking at historical deforestation trends can aid in discerning what socio-cultural, economic, and/or political influences may have occurred at the time changes in trends occurred. One way to determine where it occurs is through the use of remotely sensed data. Remote sensing provides a viable source of data from which LULC changes can be gathered efficiently and inexpensively in order to track these changes. Using Landsat satellite images from 1994 and 1999 to perform an analysis of the land cover change in the Toledo District, this study expands on a previous study of the same area by Emch, Quinn, Peterson, and Alexander (2005). This study explores the question, "Can an unsupervised classification of the Toledo District, which is less time consuming, requires less intensive data collection, and thus is less costly, produce statistically significant data?" If this can be done using unsupervised classification, it will provide an efficient tool for planners and policy makers to focus efforts to understand where and why deforestation is occurring and thus focus policies to control and/or prevent deforestation, whether that be through the creation of new policies and development plans, implementing policies that have worked in the past, or detecting unforeseen or unwanted outcomes and changing policies to change the course of current trends. This study used the same 1999 Landsat satellite image also used in the Emch, et al. (2005) study, which served as a control for the current study. The 1999 image results from the Emch, et al. study with the results found in the current study. The images used in the current study were analyzed using unsupervised classification, whereas the images used in the Emch, et al. study used supervised classification. It was difficult to discern if an area was "agriculture" or "cleared" or "deforested/regrowth". There are great differences between the 1999 image data results from the current study and those found by Emch, et al. The most drastic difference is seen in the difference between forest data, which differed by 59 percent. While the results of this analysis are determined to be insignificant, the implications relating to the method of performing this analysis will impact future studies.