TEMPORARY FLOODWATER STORAGE VOLUME ESTIMATIONS USING 1-m
LiDAR AND 30-m NED DEMS OF THE RED RIVER OF THE NORTH BASIN
Sarita Pachhai, Graduate Student
Bradley C. Rundquist, Associate Professor
Department of Geography
University of North Dakota
Grand Forks, ND 58202-9020
[email protected]
[email protected]
Bethany B. Bolles, Senior Research Manager
Wesley D. Peck, Research Scientist
Xixi Wang, Research Scientist
Energy & Environmental Research Center
University of North Dakota
15 North 23rd Street
Grand Forks, ND 58203
[email protected]
[email protected]
[email protected]
ABSTRACT
Compared here are storage volumes determined for 120 transect locations from 1-m Light Detection and Ranging
(LiDAR) and 30-m National Elevation Dataset (NED) Digital Elevation Models (DEMs). The transects represent
flat-, moderate-, and high-relief terrain in the Forest River Watershed of the Red River of the North Basin. Results
indicate that either DEM yields acceptable storage estimates for terrain with flat to moderate-relief and LiDAR
provides the more accurate estimate in areas of high-relief. In general, storage depths derived from LiDAR DEMs
are greater than those extracted from NED DEMs, although storage estimates are not significantly different between
the two datasets for flat-and moderate-relief areas. Each dataset can be used for storage estimation as a function of
the local relief conditions, and certain section characteristics, which provided a basis to explain the patterns of
observed discrepancies.
INTRODUCTION
The Red River of the North Basin covers 116,500 km2 with nearly 103,600 km2 in the United States. It occupies
substantial portions of North Dakota, northwestern Minnesota, and southern Manitoba and a very small portion of
northeastern South Dakota. The drainage basin is remarkably flat: at Wahpeton, the elevation is 287 m above mean
sea level, and at Lake Winnipeg, the elevation is 218 m, a vertical difference of 69 m over a horizontal distance of
about 877 km (Red River Basin Board, 2000). The basin is roughly 100 km across at its widest and subjected to
frequent inundation from minor and major flood events. Historical analyses indicate that flows of the Red River are
erratic and variable. The floods of 1950, 1966, 1979, and 1997 were the largest and most damaging since records
started in 1882 (International Joint Commission, 1997, 2000). Floods usually occur during spring because of winter
snowmelt and the resulting runoff. Basin residents suffered monumental economic losses during the 1997 flood,
causing an extensive evacuation of Grand Forks, North Dakota, and East Grand Forks, Minnesota (combined
population of about 57,000). In terms of per-capita dollar-value damage, the 1997 flood in Grand Forks was the
costliest in U.S. history (International Joint Commission, 2000). Various flood control measures such as dikes and
dams have been implemented to mitigate the damages from flooding, but these measures are inadequate to protect
against the large-magnitude floods still threatening the economic infrastructures of the basin, including roads and
residential and commercial properties.
The aftermath of the devastating 1997 flood has forced a reevaluation of existing flood control measures and
exploration of new and innovative approaches to flood management in the basin. The Energy & Environmental
Pecora 16 “Global Priorities in Land Remote Sensing”
October 23 – 27, 2005 * Sioux Falls, South Dakota
Research Center (EERC) at the University of North Dakota, in cooperation with the U.S. Department of
Agriculture’s Natural Resources Conservation Service (NRCS), is investigating the feasibility of temporarily storing
springtime runoff using low-relief fields bounded by a raised network of section roads. Stored water from this
network could be released into the Red River and its tributaries in a controlled fashion after a flood crest passes,
thereby reducing peak flows. It is estimated that this microstorage or “waffle” flood mitigation concept may achieve
30 to 35 percent reduction in flow for several consecutive days at any location along the Red River during a major
flood event (Groenewold et al., 1999). Determining the amount of temporary storage that can be held in the basin is
an important component of this concept, called the “Waffle’ Project. It includes the calculation of water storage
using the road elevations of different sections of land.
A DEM is a digital representation of the Earth’s surface and provides an array of elevation points (Chang,
2002). DEMs are commonly used for terrain representation and analyses. They are now a standard tool in
hydrological applications (Moore et al., 1991). Commercially available DEM datasets have different horizontal and
vertical resolutions that greatly influence topographic digital representations (Thieken et al., 1999; Zhang et al.,
1999; Holmes et al., 2000; Wolock and McCabe, 2000; Kienzle, 2004). The utility of the LiDAR has been
demonstrated for hydrological applications through a number of recent studies (e.g., Ritchie et al., 1994; Cobby et
al., 2001; White and Wang, 2003). LiDAR can obtain high resolution elevation data with a vertical accuracy better
than 30 cm. It is widely used for mapping floodplains or conducting visibility studies because those applications
require absolute elevation (Hodgson et al., 2005). A small discrepancy in topographic elevation may cause a large
error in the calculation of the temporary floodwater storage. Therefore, it is important to understand the effect of
vertical accuracy of a DEM on storage estimations. Vertical accuracy is dependent upon the horizontal grid spacing,
collection methods, digitizing systems, and the quality of the source data (Adkins and Merry, 1992). In this study,
reliability of the elevation data contained within the U.S. Geological Survey (USGS) NED is being compared with
the LiDAR elevation dataset as part of an ongoing effort to determine the storage volume available in fields and
depressions throughout the basin, even though error in the DEMs can influence the outcomes of terrain and flood
extent mapping (Hodgson et al., 2003, Wang and Zheng, 2005). To determine the effect that both datasets have on
estimating storage volume, we selected sites with high, moderate and flat relief. Geographic information system
(GIS) analysis is useful to locate storage areas ideally suited for holding water and to make the storage assessments
in these areas.
MATERIALS AND METHODS
The Study Area
The Forest River Watershed, covering approximately 2200 km2, is one of the 24 subbasins that make up the Red
River Basin (excluding the Assiniboine River Basin). The watershed lies in the northwestern part of the state of
North Dakota in the United States and offers a site of contrasting landscapes ranging from flat floodplains to rolling
upland hills. This area is characterized as glaciated plain with lakes and wetlands. The entire watershed
encompasses about 997 sections of land. We selected this watershed for study because both NED and LiDARderived digital elevation models are available.
Datasets
The high-spatial-resolution LiDAR DEMs were developed using an airborne LiDAR that collects thousands of
spot heights every second by transmitting pulses toward the ground and measuring the time lag until their return. To
minimize the vegetation influence, the data were acquired in spring 2004, when deciduous vegetation was at a leafoff stage. The EERC, in partnership with the NRCS National Cartography and Geospatial Center (Fort Worth,
Texas) and Sanborn Mapping (Colorado Springs, Colorado), collected nearly 4200 km2 of high resolution
topography data (1 × 1 m bare earth DEM). The data were provided in tiles, elevation in meters, with each tile
covering about 8 km2. These tiles (usually 16) were merged to produce quads similar in coverage area to
conventional USGS 7.5-minute (1:24,000-scale) quadrangle maps. Comparison between LiDAR DEMs and 101
ground control points surveyed using a global positioning system (GPS) showed a fit of better than ±11 cm root
mean squared error (RMSE). This corresponds to a vertical accuracy of 20 cm at the 95 percent confidence level.
The NED is a seamless raster derived primarily from the individual DEMs of USGS 7.5-minute quadrangles
and updated with aerial photographs, satellite images, and ground surveys. The DEM has a spatial resolution of
about 30 × 30 m, with vertical accuracy better than 15 m. Elevation units are standardized to decimal meters (Gesch
et al., 2002). DEMs and metadata were downloaded from the USGS National Center for Earth Resources
Pecora 16 “Global Priorities in Land Remote Sensing”
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Observation System (EROS) Data Center, Sioux Falls, South Dakota (http://ned.usgs.gov/). USGS reported that the
RMSE for NED 30-m DEMs ranges between 1 and 2 m for the Forest River Watershed and 1 and 6 m for the Red
River Basin.
Along with these two main datasets, we used USGS 1:24,000 digital topographic maps, USGS 1:24,000 digital
orthophoto quadrangles (DOQ), and USGS Public Land Survey System (PLSS) digital line graph datasets for
information about sites. We used sections defined by PLSS as the areal storage unit.
Methods
The study area was classified using a criterion under which relief terrain was independent of the spatial
resolution of datasets. That is because the accuracy of terrain parameters and features derived from each DEM
exhibited scale dependencies (Gao, 1997; Kienzle, 2004).We decided to match the terrain relief units for a section
from NED to LiDAR datasets. As a result, the terrain unit should be the same range for sections derived from the
DEMs at both scales. For example, we only examined a given section if its relief classifications derived from both
LiDAR and NED were in agreement. To achieve this goal, average and median elevations were calculated for
elevation values that fell within each section from both DEMs using the “zonal statistics” function of the spatial
analyst in ArcGIS 8.3 (Environmental Systems Research Institute, Redlands, California). The relief terrain unit for
every section was computed by subtracting the median elevation from the average elevation value for the whole
study area. The absolute relief terrain unit shows that it is effective for distinguishing for relief terrain unit classes,
with over 83 percent of evaluated sections classified under the same category between the two datasets. To quantify
the differences in relief terrain, we performed a t-test, the results of which showed that the relief terrain units derived
from LiDAR and NED for all sections were indistinguishable (t = -0.27, p = 0.786, n = 997 sections). We set
threshold relief values of 0.35 m and 0.80 m as the upper limit of flat- and medium-relief terrain, respectively,
justifying this value through examination of break points on the cumulative graph (Figure 1).
Cumulative Relief Terrain (m)
400
350
LiDAR
300
NED
High Category
250
200
150
Moderate Category
100
50
Flat Category
0
1
51
101 151 201 251 301 351 401 451 501 551 601 651 701 751 801 851 901 951
Section Number
Figure 1. Graphs of cumulative relief terrain unit (average – median) of LiDAR and
NED elevations against sections.
We have eliminated 165 mismatched sections (17 percent of the study area) from further analysis. Thus the
watershed was classified by region (Figure 2): flat (614 sections), moderate (104 sections), and high-relief terrain
(114 sections) and subsampled by sections, representative of storage units (flat = 44 sections, moderate = 30
sections, and high = 24 sections) for further analysis in each relief terrain category.
Once sections in the study area had been classified, south-north oriented transects spaced approximately 3 km
apart were selected to examine the storage estimations. Two sites (sections) on the transect were extracted for closer
examination. These sections were selected randomly from all three-relief terrain areas to minimize the influence of
watershed morphology by rivers, streams, lakes, and wetlands, which are permanently wet year-round on storage
Pecora 16 “Global Priorities in Land Remote Sensing”
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estimations. Sections containing these features were excluded from the sampling because they naturally store water
and are not candidates to enhance floodwater storage. In addition, sections that contained towns, airports, and
landing fields were not considered (determined from visual interpretation of digital raster graphics (DRGs) and
DOQs). An ideal section for floodwater storage does not contain such features and is surrounded by raised roads
and/or railways. If a section did not meet the requirements, then new random selections were made.
Figure 2. Location of three study areas in the Forest River watershed of the Red River Basin.
We used GIS to process, analyze DEM datasets, and identify potential storage areas in each section selected for
analysis. The lowest elevation of surrounding raised roads, as printed on the DRG, was used to determine the
potential depth of storage on each section. These spot elevations are typically provided for each section and quartersection corner, for a total of eight road elevations per section. DRG spot elevations are based on the National
Geodetic Vertical Datum of 1929 (NGVD 1929). The NED and LiDAR, on the other hand, are based on the North
American Vertical Datum of 1988 (NAVD 1988). Consequently, we used datum conversion tool i.e., VERTCON
North American Vertical Datum Conversion Utility software (version 2.03), developed by the National Geodetic
Survey to transform the road elevations to the NAVD 1988 datum. The lowest NAVD 1988 road elevation from the
DRG was then compared to within-section grid cell elevations as reported by the NED and LiDAR datasets. Cells
with elevations lower than the lowest road elevation have the potential to store water (sink cells). Storage depth of
each sink cell was extracted, and depth values were summed across each section. Floodwater storage volumes were
estimated by multiplying the storage areas, as calculated by the GIS procedure described above, times a depth.
These estimates were compared using descriptive statistics, correlation, and regression analysis, together with a
simple visual comparison of storage maps.
Pecora 16 “Global Priorities in Land Remote Sensing”
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RESULTS AND DISCUSSION
LiDAR
3
35
NED
30
Average Storage (10 × m )
40
4
The disparity between the NED and LiDAR datasets was evaluated by examining the difference in storage
volume estimates. Average storage volume per section estimated from LiDAR and NED DEMs was 37×104 m3 and
31×104 m3, 28×104 m3 and 26×104 m3, and 33×104 m3 and 24×104 m3 for flat-, moderate- and high-relief terrain
sections, respectively. LiDAR estimated greater average storage volume per section for all types of relief terrain
classifications (Figure 3).
Statistical differences between the storage volume estimations for all sections were evaluated by relief terrain
classification using paired t-tests. Results indicate no statistically significant discrepancy between the two datasets at
the 95 confidence levels for flat- and moderate-terrain relief, although NED storage volume estimates in sections
classified as high-relief were significantly lower than paired estimates made from LiDAR DEMs.
25
20
15
10
5
0
Flat
Moderate
4
High
3
Figure 3. Average storage volume (10 × m ) per sample section estimated from LiDAR
and NED DEMs in the study area.
When storage volume was cumulated by relief terrain class, the LiDAR dataset yielded the highest values
(Figure 4). The difference in cumulative storage volume estimates for high-relief was greatest. These results
revealed that storage volumes were significantly affected by DEMs in high-relief sections, as higher-relief sections
generally exhibit greater elevation variability that is better characterized by LiDAR. The cumulative storage curves
of these two datasets tracked best for areas of moderate-relief (Figure 4) and consistently for areas of flat-relief.
LiDAR
1200
NED
4
3
Cumulative Storage (10 × m )
Flat
1400
High
Moderate
1000
800
600
400
200
0
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Section Number
Figure 4. Cumulative storage volumes (10 4 × m3) for sample sections for all relief for NED and LiDAR datasets.
Pecora 16 “Global Priorities in Land Remote Sensing”
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We used linear regression to examine the nature of the association between storage volume estimates derived
from the NED and LiDAR DEMs by relief terrain class. When regression is applied to comparison data, it can
provide useful information about proportional, constant, and random error via, respectively, slope, intercept, and
standard deviation of the residuals (Stockl et al., 1998). Our analysis revealed positive trends and a statistically
significant relationship between LiDAR- and NED-based storage volume estimates for all relief terrain classes
(Table 1).
Table 1. Summary Statistics of Regression Analysis on the Storage Volumes between LiDAR and NED DEMs (P
Value is for the Two-tailed t-Test).
Relief Class
Slope Coefficient
t
R2
P
N
Flat
1.06
14.3
0.83
< 0.001
44
Moderate
0.97
12.83
0.85
< 0.001
30
High
1.36
20.47
0.95
< 0.001
24
Average of Maximum Depth (cm)
The slope coefficients are close to 1 for flat- and moderate-relief, but much greater than 1 for high-relief,
indicating that LiDAR overestimates the storage in high-relief sections. In other words, the coefficient of 1.36 for
high-relief means that LiDAR-based storage volume estimates increase by 1.36 for each cubic meter estimated by
NED. Overall, LiDAR-derived calculations yielded larger volumes in every section classified as high-relief. The
slope coefficients show that characteristics of relief terrain do influence the distribution of storage estimate error.
The cause of the difference in estimates could be related to the topographic complexity in high-relief sections, since
the smoothing effects or lower spatial resolutions are greatest on steep terrain with short-length-scale features
(Wolock and McCabe, 2000), resulting in a loss of landform information. When the DEMs are coarse, terrain
description is also inaccurate (Tate et al., 2002). Apparently, the NED DEMs lack sufficient spatial and vertical
resolution to identify fine-scale water course channels, steep slopes, and small wetlands that are dominant in the
study area. LiDAR better characterizes those fine-scale features; a finding in agreement showed error introduced by
interpolation of LiDAR is very low, adding only up to 3.3 cm to any land-cover class (Hodgson and Bresnahan,
2004).
In general, LiDAR DEMs tended to produce larger estimates of storage depth (Figure 5). LiDAR DEMs
exhibited a larger range in storage depth estimates extracted for all sample sections: from 14 cm to 940 cm versus 5
cm to 716 cm for NED. A t-test showed that LiDAR-derived maximum storage depths were significantly greater
than those estimated from NED for flat- (t = 5.36, df = 43, p < 0.001) and moderate-relief terrain (t = 2.04, df = 28, p
< 0.05) areas.
200
150
LiDAR
100
NED
50
0
Flat
Moderate
High
Relief Terrain
Figure 5. Variation of the average of the maximum storage heights (depth) with relief terrain and DEM datasets.
Pecora 16 “Global Priorities in Land Remote Sensing”
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The association of storage height disparity between the two datasets may be related to several factors. First,
LiDAR is better able to characterize both lower and higher extreme topographic elevations across a wide range of
surface types. Most of the sections in moderate-relief areas contain wetlands and watercourses. Surface elevation of
wetlands depends on the time of elevation data collection, since wetland hydrology is complex with spatial and
temporal variation. It is very difficult to acquire accurate elevation data for areas inundated by water through
photogrammetric methods (Tate et al., 2002). LiDAR was collected at the end of spring, when most of the wetlands
were in an almost dry condition. That contributed to the greater maximum storage height calculated from LiDAR.
When average storage depth of a section was averaged again for sections of each relief terrain class, the differences
between two datasets was not prominent, as in the case of the maximum storage depth of sections, although average
storage depths extracted from NED were greater than those from LiDAR DEMs for moderate- and high-relief
terrain. This indicates that the storage water level is high in some areas of storage fields in moderate and high-relief
terrain from the NED throughout the sections; however these differences are generally negligible. A visual storage
comparison and corresponding storage depth or heights from profile lines between datasets for a section in each
relief terrain class in the study area are shown in Figures 6 and 7. These figures show the storage area coverage in
blue. The brighter the tone is the less the storage depth. The absolute elevation difference image between LiDAR
and NED of the study area is also shown in Figure 8.
i) Flat-relief terrain
180
LiDAR
a)
Storage Depth (cm)
150
NED
120
90
60
30
0
0
200
400
600
800 1000 1200 1400 1600
Distance (m)
b)
Figure 6. An example of visual comparison of the storage estimations for a section from a) LiDAR and b) NED
DEMs in i) flat-relief terrain and corresponding transect (shown in green) comparisons of the storage depths
between DEM datasets. The underlying shaded relief model was created from 3-m LiDAR DEMs.
Pecora 16 “Global Priorities in Land Remote Sensing”
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ii) Moderate-relief terrain
150
LiDAR
NED
a)
Storage Depth (cm)
120
90
60
30
0
0
200
400
600
800 1000 1200 1400
Distance (m)
b)
iii) High-relief terrain
a)
Storage Depth (cm)
240
210
LiDAR
180
NED
150
120
90
60
30
0
0
100 200 300 400 500 600 700 800
Distance (m)
b)
Figure 7. An example of visual comparison of the storage estimations for two sample sections from a) LiDAR and
b) NED DEMs in ii) Moderate- and iii) High-relief terrain and corresponding transect (shown in green) comparisons
of the storage depths between DEM datasets. The underlying shaded relief model was created from 3-m LiDAR
DEMs.
Pecora 16 “Global Priorities in Land Remote Sensing”
October 23 – 27, 2005 * Sioux Falls, South Dakota
Figure 8. Absolute elevation difference surface produced by subtracting resampled 3-m NED DEM from resampled
3-m LiDAR DEM in the Forest River Watershed
The storage depths constructed from the NED DEM do not contain the same amount of detail as the LiDARderived profile storage depth map. It illustrates the lack of road and ditch representation that frequently occurs in
NED DEMs. An analysis between DEMs and GPS-derived elevations at 106 random locations shows that the
absolute average difference is 0.10 m and 1.16 m LiDAR and NED, respectively. The absolute elevation difference
image (Figure 8) produced by subtracting resampled 3-m NED from 3-m LiDAR DEMs shows the elevation
difference scatters over the watershed, large differences (the darker the tone, the greater the elevation difference) is
in river, stream channel, ditch areas, and high terrain relief areas.
CONCLUSION
This study has shown that DEM selection affects storage volume estimation. However, the degree that estimates
are affected depends upon characteristics of the terrain (i.e., the amount of relief terrain). This is demonstrated by
comparison between storage volume estimates over flat-, moderate-, and high-relief terrain in the Forest River
Watershed of the Red River of the North Basin. Storage estimates from 30-m resolution NED DEMs are smaller
than those from 1-m LiDAR DEMs. Based on our observation, the LiDAR storage volumes match NED storage
volumes fairly well over moderate- and flat-relief, but significant discrepancies exist in areas of high-relief. The
results were consistent except for sections intersected by small wetlands, watercourses, and roads.
Findings indicate that both DEMs can produce representative storage volume estimates for areas with relatively
flat- or moderate-terrain complexity. It is therefore expected that using NED-derived estimates as a storage depth to
calculate storage for a section in high-relief terrain would yield a smaller storage depth. This may ultimately result in
a shallower water storage depth, compared to the LiDAR storage depth estimated under the same conditions using
the NED and LiDAR datasets. The greater storage depth mapped by LiDAR leads to higher storage volumes in all
relief terrain classes although storage estimations are not significant for flat-and moderate-terrain relief areas. This is
Pecora 16 “Global Priorities in Land Remote Sensing”
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probably due to the higher spatial resolution of the LiDAR product, which enables it to provide a more detailed
picture of storage in these high-relief areas. Meanwhile, no significant effects of area on mapped storage between
the two datasets were observed in all relief terrain. By using a rectangular DEM structure, the elevation of the area
under each grid cell becomes lumped property and is replaced by a single value. This procedure results in averaging
or generalization of some features, such as ditches and local storage areas, which have sizes smaller than the
selected grid cell size. Such generalization has affected the depth of these naturally occurring features when the
NED dataset is adopted. The elevation disparity between the two datasets shows the large difference found in river,
stream channel, ditch, and high-relief terrain areas; however, we have not considered the sections with these features
(except high-relief terrain areas) under this study.
The results of this study indicate that each dataset can be used for storage estimation as a function of the local
terrain relief conditions and certain sections’ characteristics. Such comparisons provided us with the basis of
reasoning to explain the variation pattern of the observed discrepancies. However, this comparison does not give the
clear picture of the storage accuracy between datasets, which needs to be tested to determine their validity. A future
step is to compare our results with the field measurements at the outlet of the sections estimated here. Furthermore,
this study did not address engineering, socioeconomic, or other aspects of floodwater storage.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the EERC at the University of North Dakota for providing the LiDAR
DEMs and financial support to conduct this study. We are also grateful to the Department of Geography for support
and encouragement during the study.
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