Solving the LiDAR Challenge in Hydrographic Mapping
At a state and local level, comprehensive and accurate hydrographic mapping helps stakeholders to monitor and improve the effectiveness of regulatory activities and better understand potential human impacts, such as climate change and pollution, on both surface and sub-surface waters. High resolution airborne LiDAR data can significantly improve hydrographic mapping, but it comes with a unique set of challenges.
The standard commonly in use today for airborne LiDAR collections is the USGS National Geospatial Program (NGP) LiDAR Guidelines and Base Specification (draft version 13, released in 2010), which provides guidance for the generation of digital elevation models (DEM). However, state and local government entities may be disappointed to learn that a LiDAR project based on this specification will not capture all of the water features that should be included in a hydrographic map. The USGS specification for mapping hydrographic features applies to surface water features larger than approximately 2 acres and inland streams and rivers greater than 100 feet in nominal width. Only hydrographic features that meet those size thresholds will be mapped, even though a multitude of other water features exist that do not meet those parameters. Conversely, the specification allows for the inclusion of additional breaklines, which are often collected to enhance the terrain surface but are not generally used to update hydrography because of their scope and extent.
“It is important to recognize that the focus of the USGS LiDAR specification is terrain modeling, not hydrographic mapping,” explains Ben Houston, CEO of GroundPoint Technologies, a geospatial data service provider based in upstate New York that specializes in developing watershed-based high resolution topographic data to support hydrologic analysis and mapping. “The developers of the specification recognize this and are clear about it in the specification’s language, but many users and clients don’t necessarily understand it.”
Houston believes that terrain modeling and hydrographic mapping should be separated to advance the idea that incremental improvements in both datasets are possible, but that they need to be done in concert with one another and with forethought. To provide guidance in this direction, GroundPoint Technologies has been developing a set of modular hydrographic specifications. The additional requirements enhance the USGS LiDAR specifications to allow for updated hydrographic data to be integrated with the new terrain data.
Including these specifications as part of a clients’ LiDAR project on the front end helps avoid unexpected costs for additional work on the back end if the deliverables fail to meet clients’ expectations. “Establishing those expectations is largely a process of education,” Houston says. “Looking at the long term impact on things like wetland connectivity, coastal resiliency, and ecosystem services requires good hydrographic mapping, which is only possible by obtaining the right kind of data.”
“Organizations that have invested in LiDAR data for any number of reasons, as part of a specific project or part of an overall GIS framework, are getting really high quality data. But the USGS LiDAR specification doesn’t meet the needs of local users in the area of hydrography,” Houston continues. “For example, there is a significant gap between what is called for in LiDAR projects using the USGS specification and the typical ’blue lines’ from the National Hydrography Dataset (NHD). With a little extra effort, the breakline datasets included in most LiDAR projects could be updated or enhanced to provide adequate hydrographic mapping.”
The NHD, available at scales of 1:24,000 and 1:100,000, is considered the “gold standard” for hydrographic mapping and is available across the nation. The data is geocoded so every segment of every stream has a unique ID, which enables agencies to tie permitting and other activities to a stream segment. The formal data model also includes a geometric network that allows modelers to use the data for flow tracing and hydrologic modeling. The NHD includes many smaller water features than the USGS LiDAR specification allows for, but it is normally only available at a much lower resolution than a typical aerial imagery or LiDAR project being collected today. The use of newer, higher resolution data frequently creates discrepancies between topographic maps, planimetric maps, hydrographic maps and other derived datasets. This results in users trying to manage disparate datasets that are out of alignment both horizontally and vertically. Once additional hydrography data is captured, adding it to the terrain model is straightforward as part of a "hydro enforcement" effort. “The challenge is that all these datasets are ultimately integrated in a GIS,” says Houston. “Clients expect their terrain model, their contours, and their hydrography to all line up.”
One example of a modular specification would be to re-map the streams currently identified in the NHD and align them with their proper position in accordance with the LiDAR. If needed, that data could be captured as breaklines and "enforced" into the terrain or could simply be mapped separately to align with the terrain. Included with the hydrography could also be an accompanying specification for dealing with culverts and small bridges that are not accounted for in the USGS specification but still can have a significant impact on the flow network.
“Ideally, what I’d like to see is a two-pronged approach to the problem,” Houston says. “First we establish specifications appropriate for hydrographic mapping that can be added to the USGS LiDAR specification, followed by migrating that hydrographic dataset into a data model like the NHD so that users can begin to do analysis, upstream/downstream flow tracing, runoff modeling, water quality modeling and anything else that they need. Basic modeling tools that provide management options for addressing concerns over water pollution, flooding and climate change are all becoming more available to decision makers, but the data is not there to support them. The NHD data model ultimately provides a good starting point, and we can provide new data to NHD from LiDAR. We just need better specifications at the beginning of LiDAR projects so that users of the data don’t get left hanging, unable to accomplish their goals because they didn’t receive the necessary deliverables.”