This paper describes an algorithm to describe the distribution of cloud forests in the tropics using Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery. As an important component to global biodiversity, the conservation of tropical cloud forests is a high priority. Unfortunately, it is difficult to map these forests from space because they tend to look similar to other tropical forests. Our approach uses high-resolution satellite imagery (MODIS) to determine the average cloudiness in a region, and to relate these mean cloudiness patterns to the underlying topography. In addition, we developed techniques to distinguish the cloud forests based not only on the total amount of cloudiness, but its seasonal and diurnal variability. Currently, the Central American region was most closely depicted by averaging monthly mean images into annual mean images for both morning (Terra satellite) and afternoon (Aqua satellite). The algorithm was created to narrow the favorable conditions for a cloud forest to exist. For this, each corresponding pixel for each month was averaged together to create a mean annual frequency cloudiness image for Terra and Aqua separately. The difference of maximum and minimum pixel cloud brightness was then calculated in order to locate the lowest variability locations necessary for persistent cloudiness throughout the year. This helps to eliminate any season dependence that may occur with frequent cloudiness locations. Lastly, maintaining the combined overlapped product of Terra and Aqua provided the lowest amount of diurnal variability essential for an ideal cloud forest to exist. This acts to prevent non-ideal daytime dependence. The results demonstrated the combined product of the thresholds of the Central American MODIS sector, and are placed among topography in order to analyze the detailed locations with the 250 meter spatial resolution. The cloud forest regions are along the northeastern mountain slopes of Central America, which are favorable locations due to the persistent trade winds.