Terrain data comes in a variety of display and geometries. Elevation data can
come in the form of contours similar to what is seen on a topographic map. Another
geometry type would be points in which each point represents an elevation. Of the two
datasets mentioned they can each be used to create a triangular irregular network or TIN.
Creation of a TIN is important as it acts as a segway between features and the most useful
of elevation datasets which would be a raster or grid. When a raster or grid is used and
contains elevation data where each pixel value represents an elevation it is called a
Digital Terrain Model DTM or Digital Elevation Model. When referring to elevation
datasets we will refer to them as DEM in the truest sense, as DTM can be an elevation
dataset but also can be a number of different representation of elevation such as slope,
aspect, drainage and other terrain attributes (Yures). The DEM’s used in education,
research, and industry today are fundamentally the same represent elevation data in a
raster format, but are created in drastically different methods and procedures.
The most common DEM is provided from the USGS and was created from
1:24,000 contours at a 30M resolution and covers all the US seamlessly. In certain areas
of the US mainly highly urban higher resolutions are available such as 10M (USGS).
DEM’s can be created from remotely sensed data from space or aircraft. LiDAR, Light
Detection and Ranging are the source for very high resolution DEM creation. LiDAR
data is created by flying at low altitude approximately 300-2000 meters, utilizing a swath
of light to and from an onboard sensor which calculates the time distance traveled to
return point data with elevation information (NOAA). The light emits a pulse return an
elevation this is called the first return and is a measure of the elevations of the canopy,
building roof elevations, and other unobstructed surfaces (AeroMap U.S.). The next pulse
or second return is a canopy penetrating return; this return can be used by subtracting first
return data (canopy) to derive an interpolated bare earth point data. These points can be
very dense and take up considerable hard drive space. These points contain X, Y, Z data
in which X and Y are ground locational information and Z is the elevation data. The
points can be very dense, thus taking up considerable hard drive space. The points are
converted to a TIN and then to a DEM that is now in a useable form.
DEM’s are currently the most useable form of elevation datasets because of the
capabilities of Raster Math for analysis. The USGS derived datasets are free to the public
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while LiDAR data can be very expensive even for the wealthiest consumer. What does all
this DEM business mean for practical use? In the water resource industry namely surface
water modeling the DEM is the single most important dataset used in the analyst of or
earth’s river systems. The DEM can derive hydrologic as well as hydraulic datasets used
in this type of analyst. DEM’s can delineate stream centerlines, watersheds, longest
flowpath of a watershed, and slope all important for the development of hydrology for
watersheds. The DEM can also be used to develop hydraulic models, in Raster Math to
calculate the extent of the watersheds watersurface elevations, and delineate this data for
display. This brings up an interesting situation is the most expensive highest resolution
data always the best choice for use in hydrology and hydraulic modeling. This situation
will be tested by utilizing ArcHydro tools a watershed delineation and analysis software
for ArcGIS. ArcHydro tools will be used to calculate flows for a particular watershed
using a side by side comparison approach. The DEM datasets to be compared will be the
USGS 30M DEM and LiDAR obtained two foot cell spaced DEM.
Figure 1: An example of one thirty meter (99ft) DEM over lay with 2 foot cell spaced LiDAR
data.
Hydrology in the sense of water surface modeling can be considered calculating
the behavior of the substance in a natural environment. In a more layman statement
hydrology can be considered the calculation of the flow of water usually in CFS (cubic
feet per second) at a particular point on a stream. This calculation takes certain
parameters into consideration including but not limited to upstream contributing area,
upstream centerline length, and slope. Important tools to derive these calculations are
hardware, software, data, and people. An important factor in calculating the most
accurate hydrology (flows) is the accuracy of the underlying terrain data. Terrain data
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itself can carry significant cost not only in its acquisition, but in its storage, manipulation,
and output. The remainder of this paper will concentrate on hydrology and hydraulic
technologies and the underlying terrain data that afford there remarkable ability to model
our earth’s water surface features. Namely LiDAR (Light Detection and Ranging) is the
emerging king of source elevation data in surface water resource modeling. Other
elevation data sources will be discussed and their pros and cons will be unearthed.
For Hydrology ArcHydro tools a free extension from ESRI is used in the
development of the perimeters, inputs, for flow calculations. The Preprocessing in
ArcHydro tools of the data varies in significantly between the two types of DEM data
tested. USGS 30 meter DEM and LiDAR obtained DEM data where tested. Both data
sets where clipped to the same extent. The higher resolution LiDAR data is the same
extent as the USGS 30 meter DEM but the dense sampling makes the actual computer
file size is considerable different. It is this difference that was found to increase computer
computation times shown below.
Figure 2: ArcHydro preprocessing steps times are in minutes
When all data is processed the LiDAR data took six times as long to process for
the same extent as the USGS 30 meter DEM. The preprocessing is necessary to derive all
the inputs for the USGS Regression equation in order to calculate flows. There were
slight differences in the watershed area and longest flowpath lengths for the two differing
datasets. This difference was insignificant in the final flow values produced with a
difference of 10 cfs. The following is the regression equation used in the flow calculation
details about the perimeters used are included.
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Figure 3: USGS Regression equation note differences in inputs but actual flow value Rural 100 yr
has a 10 cfs difference for a 10,800 cfs discharge)
In conclusion the extremely high cost and processing time involved do not
warrant utilizing LiDAR data. USGS 30 meter DEM is considered acceptable for
developing hydrology for these purposes. It should be noted that further test would need
to be conducted in order to substantiate this claim. Other areas of study would be flat
coastal terrain verses mountainous terrain, differing regression equations flow
calculations, and different resolution of DEM’s.
Hydraulics in the simplest terms can be stated as the behavior of water in a
conduit whether a natural riverine system or cylinder. Hydraulics in itself is a complex
and time consuming methodology and beyond the scope of this investigation. For
hydraulic comparison between the USGS 30 meter DEM and LiDAR DEM already
developed hydraulics models were used. The models were run for the 100 year flood
event and the delineated floodplains were compared. In floodplain delineation
horizontal/vertical accuracy is a must. The following is a comparison of the differing
outputs.
Figure 4: Mapped Floodplains
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The longer jagged lines represent USGS 30 meter DEM delineation while the
tightly spaced lines are LiDAR DEM. The two examples are termed rasterization. This is
an undesired trait in floodplain delineation and made minimal by using high resolution
LiDAR DEM’s. Although it is undesired in floodplain delineation due to the possibility
of loss of life and/or property it might not be necessary for all delineation. If the data is to
be used for small scale projects the rasterization might not be noticeable at that scale and
lower resolution DEM would be acceptable.
In conclusion the need of LiDAR data is viable in floodplain delineation even
with the high cost, expensive hardware/software, and time consumption. It is probable
that large government organizations would take on such task and thus warrant the
procurement and use of this data. In studies in which the need of highly accurate
delineation is not necessary one would consider using data that is of lesser resolution and
free in most cases. In carefully planned project these issues would be predetermined and
addressed accordingly.
Future research as with hydrology would be flat coastal terrain verses
mountainous terrain. The investigation could be on mapped outputs and delineation
behaviors in these differing topographic geographies. In previous experience it can be
noted that the steeper the terrain, i.e. the closer the contour lines, lessen rasterization
provides a more realistic depiction of what is happening on the ground.
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Sources:
AeroMap U.S. Company online website Lidar Basics.
http://www.aeromap.com/lidar_basics.htm
NOAA Coastal Services Center. Remote Sensing for Coastal Management LiDAR.
http://www.csc.noaa.gov/crs/rs_apps/sensors/lidar.htm
United States Geologic Survey USGS. Online List of Products, National Elevation
Dataset NED 1 arc second. http://seamless.usgs.gov/website/seamless/products/1arc.asp
Yures, Gabriel. Online GIS Tutorials: Chapter 3 Digital Terrain Model (DTM)
http://www.profc.udec.cl/~gabriel/tutoriales/giswb/vol2/cp3/cp3-1.htm
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