Light detection and ranging, or LIDAR, is a type of remote sensing technology that is similar to radar. It is used in a variety of geographic and environmental applications to model and analyze the physical world.
Often mounted to an airplane or motor vehicle, the sensing unit uses radio waves and the measured time delay between pulse transmission and reflected pulse receipt to determine the distance from an object. There are two styles in which this data can be received by the unit. Some units use what is called “discrete-return,” which only records data at predetermined precise locations (space or time). Some refer to this style as “point” because it returns information specific to locations. The other type of data receipt is called “waveform,” which records data nearly continuously from the unit.
The method in which a moving unit obtains its results is slightly more complicated than one might think. Since the unit is in motion, its location as well as its orientation at pulse transmission must be accounted for; the same goes for unit orientation and location during pulse receipt. The use of other measurement methods, such as GPS units and inertial measurement devices, is necessary in order to achieve accurate results.
Since its inception, LIDAR has been used in numerous applications, but has become particularly valuable in the Geography/GIS user community. Aerial based LIDAR data collection is used to create DEMs (Digital Elevation Models), and is dramatically faster and less expensive that surveying. A single aircraft can take a “swath,” or obtain virtual results, for an area as wide as one kilometer during a flyover. The resulting x,y,z data obtained can be input and processed by GIS tools to produce a DEM. The DEM can then be used to determine areal flood vulnerability, site orientation, and site slope. And all this can be accomplished “sight-unseen.”
A little over a year ago, prior to joining Zekiah, I was tasked to process LIDAR for each county in Maryland and amalgamate all the results into a DEM to return to the State. My participation in the endeavor was to work as part of a team to QC and to organize the incoming data so that it would be compatible with the LIDAR processing model we had created. The model sought to take the data from the county provided .txt file format and process it into a fully mosaicked raster data set.
Each county had divided up the area inside its boundaries into multiple “tiles,” or roughly 25 square mile areas to make data collection easier. When each county provided my team with its data, each county folder was made up of multiple .txt files for the created tiles. After resolving some of the LIDAR data’s’ significant digit conformity issues, we successfully converted the files from .txt to .csv format using MS Excel. Using ArcGIS’s Model Builder, we set up an iterator to convert each .csv tile into a multipoint feature. Once that was done for all the tiles, we took all those multipoint features and created rasters from them, then mosaicked all those rasters together to form one homogeneous raster dataset for the entire county.
The goal for this project is to give the State of Maryland a product they can use to better define their flood prone areas, as well as give them an idea of which counties need to have their data re-flown using a finer resolution in order for it to be usable for this application. Using LIDAR to obtain the topographic information was a much more cost-effective option to traditional surveying techniques, and was able to save the State a lot of time as well.
This LIDAR analysis capability is exploitable, now more than ever, in incident planning and response. It can be used to support projects relating to planning for flood prone inland and coastal areas, and identifying what infrastructure/resources will be affected by a hypothetical flood height. It can also be used to help create a line-of-sight model. This will aid in key decision makers creating an event security plan, as well as analyze areal weaknesses in a hypothetical “active shooter” scenario. The collected LIDAR can also be ingested and used to model the area affected following a fuel or chemical spill. Using this information to analyze where the spill will go will help key decision-makers form their plan of attack to contain the spill and secure the area. For all of these scenarios, generating map products to support the analyses will be a powerful aid in addressing any applicable situation.
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