In hyperspectral imaging, shadowy areas present a major problem as targets in shadow show decreased or no spectral signatures. One way to mitigate this problem is by the fusion of hyperspectral data with LiDAR data; since LiDAR data presents excellent information by providing elevation information, which can then be used to identify the regions of shadow. Although there is a lot of work to detect the shadowy areas, many are restricted to distinct platforms like ARGCIS, ENVI etc. The purpose of this study is to (i) detect the shadow areas and to (ii) give a shadowiness scale in LiDAR data with Matlab in an efficient way. For this work, we designed our Line of Sight (LoS) algorithm that is optimized to run in a Matlab interface. The LoS algorithm uses the sun angles (altitude and azimuth) and elevation of the earth; and marks the pixel as “in shadow” if there lies an object of higher elevation between a given pixel and the sun. This is computed for all pixels in the scene and a shadow map is generated. Further, if a pixel is marked as a shadow area, the algorithm assigns a different darkness level which is inversely proportional to the distance between the current pixel and the object that causes the shadow. With this shadow scale, it is both visually and computationally possible to distinguish the soft shadows from the dark shadows; an important information for hyperspectral imagery. The algorithm has been tested on the SHARE 2012 Avon AM dataset. We also show the effect of the shadowiness scale on the spectral signatures.