Photogrammetry, LiDAR, Remote Sensing and GIS Together at Last

A 1.093Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE

Heres a hypothetical situation that happens all too often. Sam has been tasked with developing a map of the floodplain of a local river to help insurance companies and other decision makers prepare for a season of expected high rainfall. The LiDAR data for the entire state has been captured, which is the good news, but finding the specific data for this project is another story.

The spreadsheet with tile numbers and the DVD names never seem to match. After putting in some tedious man hours, Sam resolves the spreadsheet issue, finds the DVD and gets to work. Of course no good deed goes unpunished and he quickly finds that one tile in the center of the project is on a corrupt DVD.

Although inconvenient, this can be resolved because imagery was also acquired. After getting IT to issue a license from the mapping department and getting re-acquainted with the photogrammetry GUI, Sam manages to generate a stereo pair and he is able to create a dense RGB encoded point cloud but he can do nothing more without switching applications again. In doing so, Sam discovers that, with recent budget cuts, the department hasnt purchased the necessary add-on module required to work with LiDAR data. But its an important project; the insurance companies need this data in preparation for the rainy season. He prepares a request and submits it to his supervisor.

The requisition is subsequently approved and Sam is back on course in a few days. Until he realizes the photogrammetry software tweaked the format and dropped the projection information and he doesnt know who to call. Should he call the photogrammetry vendor or GIS vendor for technical support? Despite the setback Sam is glad his supervisor didnt choose the open source software where the web forum only offers dozens of suggestions to work around the problem.

Finally, he gets the situation resolved and is able to access the data. Its easily visualized, allowing him to draw cross-sections and view the data in 3D. His frustration grows, however when he cant open and view the points with the raster and vector layers. He struggles to get a real feel for the project area and cannot determine which vector layers need updating.

As Sam inspects the data, he also starts to notice a few errant outliers, which happens to be of all things: birds. Wasnt the data supplier supposed to clean that up? Although he is new to LiDAR processing, Sam assumes that deleting a few points should be simple.

This is exactly the type of challenge that many state and local government employees face across the nation. Fortunately, there are solutions that allow government employees like Sam to exploit geospatial data and provide information to decision makers that is timely, relevant and actionable. One of these solutions is Intergraphs ERDAS APOLLO.

Once the LiDAR survey is complete and the digital tiles are delivered a geospatial catalog in ERDAS springs into action. The scheduled crawlers search the data directories for new LIDAR files, harvest the metadata, create user preferred overviews, set permissions and build the catalog.

Sams first task it to find the data for the project area. He opens his web browser and zooms in on the area of interest He then draws a bounding box. It turns out the Mapping Department has already created the ortho mosaics of the project area. The project manager found some historical documents that reference previous high water levels and some video footage from a local news channel, which were also catalogued on the serverproviding Sam with valuable reference material.

For now, he just needs the LiDAR data. As such, he refines the search using the key word LiDAR. The corresponding files show up in the browser map view and Sam can do a quick quality check. He notices a missing tile and will need to fill the gap photogrammetrically. Using the clip-zip and ship routine in ERDAS APOLLO, all the necessary files are delivered to his workstation. His photogrammetry tools are fully integrated and use the same ribbon interface as the remote sensing and LiDAR capabilities. And, within no time Sam can recreate the missing data using dense matching.

After opening the LiDAR data, Sam notices that the data supplier did not remove the outliers. The box selection and delete in the profile view allow him to remove these. To start the report and provide context for the project, Sam adds a Bing Maps layer to the view and then clicks the send to PowerPoint button.

Its the end of the day but before he goes home theres one more thing to do. The missing tile created using dense matching is RGB encoded and Sam feels this will make the data far easier for the insurance people to understand. So he sets up a batch process to encode all the LiDAR tiles with the ortho imagery overnight. And since nobody will be at the office tonight, he decides to distribute the work to a few more machines using their Condor installation.

In the morning, Sam continues to clean up the data. Using the area operators like constant value and bias he easily flattens out the lake and removes noise like cars. From there, using the LiDAR and imagery, he edits the existing vector layers and creates a new layer with all the vegetation extracted from the LiDAR.

He also makes use of an auto roaming capability to examine the river corridor. Sam then measures the grade along the length of the profile and makes notes and measurements of overhanging trees and bridges, which are easily visible in the side and front views. Sam can also interrogate the newly installed monitoring system that detects tiny movements in the dam and automatically alerts the authorities if the tolerances are breached.

Further downstream, a covered bridge, which happens to be a historical landmark, has been scanned in detail so engineers can inspect it for flawsallowing it to be exactly rebuilt if the flooding destroys it. This scan is easily incorporated into the airborne point data to create a comprehensive view of the area.

Using the color infrared imagery, Sam then generates a land use classification within a 10 mile buffer of the river. This will be useful in determining where land use practices may impact water quality and run-off. Then using the LiDAR as a surface, and combining it with a land cover classification, all the areas vulnerable to landslides are detected and marked.

To determine where a breach in the levee will have the biggest impact, Sam constructs a least cost path spatial model. Since the inputs to this model may change over time he decides to publish the model to the server with a description of its application and the ability to automatically detect appropriate inputs. Now others can use his expertise through a web client when the need arises.

The analysis automatically updates and creates new attributes in Sams GIS layers. Then, the GIS software incorporates the remote sensing and LiDAR analysis to provide the insurance company with a list of all the property owners in the floodplain, each with a risk potential based on the available data. And since we now know which properties are at risk, an automated notice will be generated and sent to the property owners.

Finally a high quality tourist map of all the biking and walking trails is created for the Parks Department. And, once the project is complete all the new data is put back on the server for other to access.

This hypothetical example shows how photogrammetry, LiDAR, remote sensing and GIS can all easily and efficiently work in concert. Sam was able to quickly access the right data to effectively plan for a natural disaster situation that requires information for the insurance companies, homeowners and much more. When it comes to life-or-death situations, geospatial solutions need to be fast, accurate and reliable. Fortunately for Sam and other geospatial professionals, it is possible for all of these mapping elements to work together in harmony. The right information gets into the hands of decision makers and Sam gets to be the hero.

Steve du Plessis is the Global Product Line ExecutiveRemote Sensing at Intergraph. He has more than 15 years of experience in the geospatial industryliving and working in such amazing locations as Singapore, South Africa, Australia and the USA.

A 1.093Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE