Lidar -- Light Detection and Ranging -- is a remote sensing technology that utilizes a device similar to a rangefinder to determine a distance to a target. A laser pulse is shot at an object and the time it takes for the pulse to return in measured. The distance to the object is easily calculated using the speed property of light. For lidar, this laser is moved (primarily in a rotational movement usually accompanied by a translational movement) and records the distances to objects several thousands of times per second. From this, a 3 dimensional structure can be procured in the form of a point cloud. A point cloud is a collection of 3 dimensional points with at least an x, a y and a z attribute. These 3 attributes represent the position of a single point in 3 dimensional space. Other attributes can be associated with the points that include properties such as the intensity of the return pulse, the color of the target or even the time the point was recorded. Another very useful, post processed attribute is point classification where a point is associated with the type of object the point represents (i.e. ground.).

Lidar has gained popularity and advancements in the technology has made its collection easier and cheaper creating larger and denser datasets. The need to handle this data in a more efficiently manner has become a necessity; The processing, visualizing or even simply loading lidar can be computationally intensive due to its very large size. Standard remote sensing and geographical information systems (GIS) software (ENVI, ArcGIS, etc.) was not originally built for optimized point cloud processing and its implementation is an afterthought and therefore inefficient. Newer, more optimized software for point cloud processing (QTModeler, TopoDOT, etc.) usually lack more advanced processing tools, requires higher end computers and are very costly. Existing open source lidar approaches the loading and processing of lidar in an iterative fashion that requires implementing batch coding and processing time that could take months for a standard lidar dataset. This project attempts to build a software with the best approach for creating, importing and exporting, manipulating and processing lidar, especially in the environmental field. Development of this software is described in 3 sections - (1) explanation of the search methods for efficiently extracting the “area of interest” (AOI) data from disk (file space), (2) using file space (for storage), budgeting memory space (for efficient processing) and moving between the two, and (3) method development for creating lidar products (usually raster based) used in environmental modeling and analysis (i.e.: hydrology feature extraction, geomorphological studies, ecology modeling, etc.).