## 2 Understanding the Jargon: Equipment and Data Products
### Understanding ALS Data Products
The second difference is that LIDAR data is 3-D (although it can also be represented as 2-D), while single source imagery is 2-D (although stereoscopic imagery or structure-from-motion techniques can be used to produce 3-D datasets of a limited type of surfaces from 2-D images). So the differences discussed below are only related to what can be observed in Figure 7 and not the technical differences between ALS data and imagery. The first and obvious difference is that imagery registers spectral signatures and ALS data registers elevation signatures. However, some spectral signatures can be obtained from the LiDAR return intensities (Figure 7c), and some elevation information can be inferred from the spectral images based on shadows cast by objects present in the scene. Notice that the mound (identified with the red arrow) that is clearly visible in the shaded relief image is harder to identify by an untrained individual in satellite imagery even when it is mostly clear of vegetation. Another obvious but crucial difference is that the ALS data can be processed to remove the vegetation cover and partially reveal what lies underneath it; imagery cannot be processed to filter-out vegetation. However, perhaps the most important difference between both types of data sets has to do with the fact that the entire surface imaged by the satellite sensor is fully illuminated by the Sun (with the exception of the areas with shadows), meanwhile the shaded relief image, although it represents a complete and uniform surface model, comes from interpolating a limited number of irregularly spaced laser returns. This is perhaps one of the most important points this paper intends to highlight: a raster (image) derived from LiDAR observations may appear to represent the entire surface, but this representation is based on limited sampling of the real surface, and thus is not complete. Finally, it is important to consider that the rule that applies for airborne or satellite imagery with regard to the smallest object that can be identified in the image, defined as image resolution, does not apply to LiDAR rasters. This is because while the raster resolution or raster cell size defines the smallest interpolation element, the smallest object that can be identified will depend on the actual spacing between laser returns, the laser beam footprint size, and ultimately how much of the surface was illuminated. If an object is not illuminated by the LASER it will not be detected and will be completely missing from images derived from the LiDAR observations. It should be noted that ALS return intensity (which is the ALS product with more resemblance to imagery), as a relative measure of the amount of backscattered light can provide useful information in an archeological context in some circumstances. For instance it is useful in non-vegetated and sparsely vegetated areas, where differences in soil properties or vegetation can translate into differences in reflectance at the ALS wavelength. These differences will manifest as features in intensity images, as illustrated in Figure 8 which shows a DSM and an intensity image of the Chaco Canyon, NM site displaying some features visible in the DSM shaded relief image that are not visible in the intensity image, and _vice versa_. The elevation and intensity signatures can be fused into an intensity textured 3-D model as in part (c) of the figure, providing an enhanced visualization dataset. However, the usefulness of the intensity information is extremely limited in forested environments, where the first return intensity can be used to crudely distinguish between open and vegetated covered areas (which is also available from the ALS spatial information). Unfortunately, the intensities of sub canopy returns are extremely attenuated and their values depend on so many different factors that making use of them is extremely difficult. This is illustrated in Figure 9 that shows intensity images of both first returns (DSM) and from ground returns (DEM) of the highly forested environment in the Honduran Mosquitia.