The detection of impervious surfaces is an important issue in the study of urban and rural environments. Imperviousness refers to water’s inability to pass through a surface. Although impervious surfaces represent a small percentage of the Earth’s surface, knowledge of their locations is relevant to planning and managing human activities. Impervious structures are primarily manmade (e.g., roads and rooftops). Impervious surfaces are an environmental concern because many processes that modify the normal function of land, air, and water resources are initiated during their construction. This paper presents a novel method of identifying impervious surfaces using satellite images and light detection and ranging (LIDAR) data. The inputs for the procedure are SPOT images formed by four spectral bands (corresponding to red, green, near-infrared and mid-infrared wavelengths), a digital terrain model, and an .las file. The proposed method computes five decision indexes from the input data to classify the studied area into two categories: impervious (subdivided into buildings and roads) and non-impervious surfaces. The impervious class is divided into two subclasses because the elements forming this category (mainly roads and rooftops) have different spectral and height properties, and it is difficult to combine these elements into one group. The classification is conducted using a decision tree procedure. For every decision index, a threshold is set for which every surface is considered impervious or non-impervious. The proposed method has been applied to four different regions located in the north, center, and south of Spain, providing satisfactory results for every dataset.