Validation of Data Density and Data Void of Aerial LiDAR Data

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Airborne LiDAR technology has become an important tool for generating topographic products. The focus of this paper is on two important parameters that will play a significant role in any LiDAR mission: 1.) Point density and 2.) Point spacing. The desired data accuracy, data volume, schedule, and budgetary constraints are vital factors for deciding the point density and point spacing of the acquisition mission. There are general guidelines available for LiDAR data density (point density) and allowable data void for validation; but, they are more specific to a particular project. The validation of data density and data void will enable the data to be collected and processed accurately, be usable, and in conformance with the deliverables specified in the project’s scope of work.

LiDAR missions are planned to meet individual project specifications based on intended data use. Among the collection parameters, point spacing and point density plays an important role in meeting the project specification.

Point spacing
Point spacing refers to one dimensional measurement or a point-to-point distance. One must recognize that point distributions are not regularly or evenly spaced. As a rule of thumb, the more points that hit the ground, the better we define the targets. The point spacing varies depending on the application and type of deliverables that are produced. The point spacing question may also be important in determining the necessity for the delineation of linear features, such as breaklines, as a supplemental deliverable.

Point Density
Point density is related to point spacing and logically the closer a group of points are to one another, the higher the point density and vice versa. The point density is normally calculated from the actual data using the "box counting" method. In "box counting" an area of a rectangle is associated with the total number of LiDAR points inside the rectangle. Point density is a function of flying altitude, pulse rate, scan rate, and scan angle. With today’s state-of-the-art systems, it is possible to achieve densities of 10 20 points per square meter.

Data Void
A data void can occur in LiDAR data collection due to various reasons including water absorption. Data voids may be natural (e.g., water bodies or fresh asphalt that absorbs the laser energy), unintentional (e.g., high winds or navigation errors that cause gaps between flight lines), or intentional (e.g., from post-processing for deliberate removal of manmade structures and/ or dense vegetation not penetrated by the LIDAR). The spatial distribution of geometrically usable points is expected to be uniform and free from clustering. As stated earlier, LiDAR instruments do not produce regularly gridded points, the LiDAR collection should be close to a regular lattice of points, rather than a collection of widely spaced high-density profiles of the terrain. The uniformity of the point density throughout the dataset is important.

Based on FEMA’s Procedure Memorandum No 61, the guidelines for LiDAR nominal pulse spacing for deciles level 1, 2, 3 are given in Table 1. In a similar manner, USGS has specified certain guidelines for validating the data void in aerial LiDAR missions. Realizing the importance of the data density and data void, software has been developed to validate both parameters.

Validation of Data Density

The program developed for calculating the data density also validates the data completeness, and conducts a check of the tiles created after preprocessing. This process involves geocoding and geometric correction. This program uses the project coverage information, tile grid, and the .las files covering the project area as inputs.

The following entities will be validated by the program:
Size of the files are the same across all the .las files (xxxxm xxxxm)
Validation of file format and projection
LAS version (1.x
Proper vertical and horizontal units
Check for bounds of each file
Class statistics including overlap class

The program performs computation of statistics over all the classes per file, followed by an analysis of the results to identify anomalies, especially in the elevation fields and LAS class fields. The program creates a shape file which is the same as the tile grid used originally in the program and Excel report containing information on the LiDAR point density.

Validation of Data Void
In general, in order to ensure uniform densities throughout the data set, the following steps will be performed:
1. A regular grid with cell size equal to the K times design point spacing will be laid over the data where K is a constant.
2. At least a certain percentage of the cells in the grid shall contain at least the specified number of LiDAR points.

For example, if the LiDAR data is collected with 1m NPS, a regular grid 2m 2m will be overlaid on the LiDAR data and 90% of the grids should have at least 1 LiDAR point.

The program developed for validating the data void will create a virtual grid of appropriate grid size and check for the number of points (all returns/first return) in each grid. For example, if the requirements of the project are the following:
1. The point density should be more than 10 points per square meter.
2. 75% of the 1m 1m grid should have more than 10 points.

In this case, a grid of 1m 1m has been created and it is expected that each grid should have a minimum of 10 points per square meter. For a given .LAS file the total number of 1m 1m grid is computed at the beginning. During the program, each grid will be tested for the number of points meeting the specific criteria (namely 10 points). At the end, the program calculates the following percentage:

Percentage of grids that meets the spec = number of grids meeting the percentage criteria/total number of grids in the LAS files

If the percentage is above the specified percentage, then the particular LAS file has met the data void test. Otherwise, the particular LAS file has failed the data void test.

The block diagram for validating the data void and point density is shown in Figure 1 and the sample of the validation of data density and data void results are shown in Table 2.

Dr. Srinivasan "Srini" Dharmapuri has over 26 years of extensive, wide-ranging experience within the Geospatial industry; most notably with LiDAR, Photogrammetry, and GIS. He has worked in both the private and public sectors, as well as internationally. Currently he is working as a LiDAR Scientist with Michael Baker Jr., Inc.

FEMA’s Memorandum for Regional Risk Analysis Branch Chiefs, Procedure Memorandum No. 61: Standards for LiDAR and Other High Quality Digital Topography, Effective Date September 27, 2010.

USGS–LiDAR Base Specifications Version 1.0–Techniques and Methods 11-B4.

American Society for Photogrammetry and Remote Sensing (ASPRS), ASPRS Guidelines, Vertical Accuracy Reporting for LiDAR Data, vers. 1.0, May 24, 2004.

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