Abstract

Laser intensity reflection strength from ground-based lidar instruments are affected by several natural factors. External factors like humidity, barometric pressure, and airborne dust, can change the reflection intensity of lidar even over relatively short distances (less than 1 km). Instead of applying variable corrections for each external factor, we used statistical methods to correct the laser intensities. Three discrete attributes were applied to separate and isolate point intensity populations. These are distance, angle of incidence and surface roughness. Histograms and correction tables were used to reduce the effect these variables had on resultant laser intensity values. A more consistent intensity distribution was achieved that better related to the rock properties being mapped. This method was used on over one hundred individual scans totaling more than 300 million individual laser observation points. Working statistical methods were coded in software to read and process lidar scans in a native binary format. This reduced the number of processing steps, kept the data in its original format, and minimized disc space allocation. Using this application along with monochromatic lidar can be used to detect any attribute receptive to the laser frequency used by the lidar instrument.