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Hydrography Change Detection: The Usefulness of Surface Channels Derived From LiDAR DEMs for Updating Mapped Hydrography

Authors

  • Sandra K. Poppenga,

    1. Respectively, Geographer, Topographic Science, U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, South Dakota 57198; Research Physical Scientist, Topographic Science, USGS EROS, Sioux Falls, South Dakota; and Senior Scientist, Topographic Science, Stinger Ghaffarian Technologies (SGT), Inc. contractor for the USGS EROS, Sioux Falls, South Dakota
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  • Dean B. Gesch,

    1. Respectively, Geographer, Topographic Science, U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, South Dakota 57198; Research Physical Scientist, Topographic Science, USGS EROS, Sioux Falls, South Dakota; and Senior Scientist, Topographic Science, Stinger Ghaffarian Technologies (SGT), Inc. contractor for the USGS EROS, Sioux Falls, South Dakota
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  • Bruce B. Worstell

    1. Respectively, Geographer, Topographic Science, U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, South Dakota 57198; Research Physical Scientist, Topographic Science, USGS EROS, Sioux Falls, South Dakota; and Senior Scientist, Topographic Science, Stinger Ghaffarian Technologies (SGT), Inc. contractor for the USGS EROS, Sioux Falls, South Dakota
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  • Paper No. JAWRA-12-0013-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.

(E-Mail/Poppenga: spoppenga@usgs.gov).

Abstract

Abstract:  The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.

Introduction

The United States Geological Survey (USGS) National Hydrography Dataset (NHD) 1:24,000-scale flow lines (Kelmelis, 2003; Kelmelis et al., 2003; Simley, 2006) need to be improved to reflect current topographic conditions (Colson et al., 2006; Sheng et al., 2007; Kloiber and Hinz, 2008; Kaiser et al., 2010; Ducey et al., 2012; Quinn and López-Torrijos, 2012). These mapped hydrography updates are needed because of temporal changes in surface channels. The USGS NHD 1:24,000-scale dataset, also known as high-resolution NHD, is a digital vector dataset containing hydrographic features and is the surface water component of The National Map (Kelmelis et al., 2003). Although vector NHD flow lines are frequently used in geographic information systems (GIS), the tools used for collaborative maintenance of the dataset are quite complex (Kloiber and Hinz, 2008) and require upfront labor cost estimates associated with maintenance edits (Kaiser et al., 2010). Thus, updating a nationwide dataset as complex as the NHD is not a trivial task.

Historically, hydrography data were derived by digitizing surface water features from aerial photography and paper maps (Guptill, 1979, 1983; Stephens et al., 1980; Marks et al., 1984; Usery, 2012). Duplicating those laborious efforts to obtain nationwide time-relevant hydrography is inefficient and cost-prohibitive (Marks et al., 1984; Colson et al., 2006). With the increasing availability of computing power, digital methods for updating mapped hydrography can employ change detection analysis using bare earth digital elevation models (DEMs). Because the shape of the land exerts strong control over the collection and flow of surface water, changes to the topography can have a significant effect on local drainage conditions (Hjerdt et al., 2004; Gesch, 2006). Therefore, terrain analysis of elevation data, including light detection and ranging (LiDAR) DEMs, is frequently used to extract surface water information (O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Jenson, 1991; Moore et al., 1991; Tarboton et al., 1991; Garbrecht and Martz, 1997; Tarboton, 1997; Maidment, 2002; Liu et al., 2005; Colson et al., 2006; Jones et al., 2008; Stoker et al., 2008; Höfle et al., 2009; Jenkins and Frazier, 2010; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; Quinn and López-Torrijos, 2012). The extracted LiDAR-derived features are useful for updating NHD-mapped hydrography flow lines.

In this article, we illustrate methods for improving NHD mapped hydrography that employ change detection analysis of LiDAR-derived surface channels and NHD hydrography flow lines to identify anomalies that exceed 12.2 m, a National Map Accuracy Standard (NMAS) guideline for 1:24,000-scale maps (USGS, 1999; National Digital Elevation Program, 2004; Maune et al., 2007b). Anomalies that exceed that informational metric are spatially validated by sampling and quantifying elevation values of both the LiDAR-derived surface channel and mapped hydrography flow lines. In other words, LiDAR surface channels that deviate in excess of 12.2 m horizontally from mapped hydrography flow lines are locations of potential surface channel changes. These locations are validated by measuring the vertical elevation differences between the LiDAR surface channels and mapped hydrography flow lines. These methods are beneficial for updating NHD because only the locations suspected of hydrography change will need to be reviewed for spatial accuracy and currency, rather than an entire hydrography dataset. Even though surface flow alteration studies have been well documented in the scientific literature (King and Tennyson, 1984; Brown and Bauer, 2009; Arrigoni et al., 2010; Carlisle et al., 2010, 2011), the spatial accuracy, or the spatial component, of elevation-derived surface channels has not been emphasized in terrain analysis methods.

Background

The accuracy of surface water information is essential for many applications (Höfle et al., 2009; Petroselli, 2012), including habitat descriptions and river restoration (Jones, 2006), water land boundary delineations (Mandlburger and Brockmann, 2001), monitoring of river corridors for natural hazard management (French, 2003; Brügelmann and Bollweg, 2004; Hollaus et al., 2005), sediment transport modeling (Carrivick et al., 2010), distribution of landslide activity (Passalacqua et al., 2010), wetland dynamics (Jenkins and Frazier, 2010), estimation of gully erosion and depth of depressions (Perroy et al., 2010; Zandbergen, 2010), geomorphological change of floodplains (Thoma et al., 2005; Jones et al., 2007; Höfle et al., 2009), and surface water mapping (Heine et al., 2004; Liu et al., 2005; Höfle et al., 2009; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; López-Torrijos et al., 2012). Therefore, terrain analysis using LiDAR DEMs has become increasingly important for surface water applications because of the spatial resolution and vertical accuracy that is essential for time-relevant mapped hydrography.

Several hydrologic and hydrographic models, created over a decade ago, employ terrain analysis methods to coarser (30 m) DEMs to automate the delineation of surface channels (O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Jenson, 1991; Tarboton et al., 1991; Garbrecht and Martz, 1997; Tarboton, 1997; Maidment, 2002). Because coarser (30 m) DEMs contain less detailed elevation information than LiDAR DEMs, surface channel networks can be derived that consistently flow downstream. However, applying the same hydrologic analyses to the fine spatial resolution of LiDAR DEMs does not generate the same results. For example, geographic features such as bridges, roads, or other elevated surfaces over conduits are more prominent in LiDAR DEMs. Thus, elevations above conduits will function like dams, so a surface channel network from terrain modeling would erroneously indicate that the water cannot pass through the conduits (Maune et al., 2007a; Poppenga et al., 2010, 2012). This is problematic for commonly used ESRI GIS hydrology tools that fill depressions to derive overland flow direction. Many standard GIS hydrology tools do not analyze the next steepest downslope neighbor as an underground conduit, so hydro-enforcement is not implemented (Poppenga et al., 2010, 2012).

Arc Hydro® tools (Maidment, 2002) contain options to burn in stream networks to raster elevation data by lowering the elevation of the stream network cells (Maidment, 1996). This process changes elevation values in burned locations and alters the integrity of the DEM. Furthermore, using NHD flow lines that need to be updated to burn stream networks into newly acquired LiDAR DEMs may cause integration problems between LiDAR DEMs and mapped hydrography. Considering that LiDAR DEMs contain current topographic and hydrographic information, burning stream networks into LiDAR DEMs does not solve the issue of updating the NHD and does not solve the problem of elevations above conduits functioning like dams.

Tarboton’s (1997) method, termed D-∞ (D-infinity), uses triangular facets to identify an infinite number of possible single-direction flow paths, and the threshold is determined by multiple flow direction of one or two downslope cells. The procedure is based on representing flow direction as a single angle taken as the steepest downwards slope on the eight triangular facets centered at each pixel (Tarboton, 1997; Seibert and McGlynn, 2007; Nardi et al., 2008; Li and Wong, 2010). According to Tarboton, this procedure offers improvements over prior procedures that have restricted flow to eight possible directions (introducing grid bias) or proportioned flow according to slope (introducing unrealistic dispersion). It is questionable whether one or two cells in a 1-m LiDAR DEM are sufficient for identifying the next steepest downward slope pixel beyond elevations over conduits to hydrologically enforce surface channels, leaving the hydrologic barrier issue unresolved. Infinite flow direction methods are valuable for defining downslope direction in complex braided streams, but for updating 1:24,000-scale NHD flow lines, more readily available and commonly used tools are needed.

There are several common issues with hydrology models when hydrologic analyses are applied to DEMs. Most methods have been tested in applications of coarser (30 m) DEMs (Mark, 1983; Marks et al., 1984; O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Tarboton et al., 1988; Jenson, 1991; Martz and Garbrecht, 1992; Band, 1993; Nardi et al., 2008; Pan et al., 2012), but few have been tested on hydro-enforcement of high-resolution LiDAR DEMs. The topographic complexities of detailed LiDAR DEMs cause problems for most hydrologic and hydrographic models because hydro-enforcement is needed to generate fully connected surface channels. For example, in coarser (30 m) DEMs, the elevation data over conduits are usually not as problematic to defining a downstream path as they are in LiDAR DEMs (Maune et al., 2007a; Poppenga et al., 2010, 2012; Ducey et al., 2012). Thus, if currently available hydrologic analyses are applied to LiDAR DEMs, additional techniques are needed to generate consistently connected surface channels. Some models that attempt to solve this problem contain computationally complex mathematical processes that are time-consuming and not readily available in standard GIS software. The models are not easily applicable to LiDAR DEMs and not economically efficient for updating NHD. Additionally, if LiDAR DEMs are to be useful for updating NHD, methods are needed that can be applied to larger geographic areas. The D8 method (Jenson and Domingue, 1988) was implemented at a nationwide scale to generate a hydrologically conditioned version of the 30-m National Elevation Dataset (NED) (Gesch et al., 2002; Gesch, 2007). The resulting Elevation Derivatives for National Applications (EDNA) database activity was proposed to improve the density of 1:100,000-scale information in NHD (Kost and Kelly, 2001; Kelmelis, 2003; Franken, 2004). Considering that the nationwide EDNA database was generated with D8 hydrology tools that were readily available in standard GIS software, the D8 method is a probable framework to efficiently and cost-effectively extract LiDAR-derived surface channels for updating the NHD.

The D8 method implemented by Jenson and Domingue (1988) and Jenson (1991) is a contributing area threshold model that assigns a flow direction value to each cell that describes the direction of its steepest downslope neighbor. This is accomplished by first generating a depressionless DEM to route overland flow. Additional selective drainage methods, as proposed by Poppenga et al. (2010, 2012), can be incorporated into the contributing area threshold model to hydrologically enforce depressions located upstream of conduits and extract vector surface channels from LiDAR DEMs. By differencing a LiDAR DEM from a depressionless, or filled, LiDAR DEM, depressions that need hydro-enforcement are detected with assigned parameters. Using a least accumulative cost path analysis, the elevation values over conduits are adjusted to the lowest elevation cell value within the depression allowing a continuous downstream path. D8 hydrologically conditioning processes (Jenson and Domingue, 1988) are then applied to the hydrologically enforced DEM to derive a hydrologically enforced flow direction grid that is converted to vector surface channels. The original LiDAR DEM (unfilled), the hydrologically conditioned LiDAR DEM (filled), and the hydrologically enforced LiDAR DEM are preserved thereby minimally modifying the elevation data. The selective drainage methods, written in Python scripts and executed in an ESRI ArcGIS geoprocessing environment, have been the catalyst for several LiDAR-based research projects, including those attempting to update NHD (Kaiser et al., 2010; Ducey et al., 2012; López-Torrijos et al., 2012; Quinn and López-Torrijos, 2012).

It is well documented in the scientific literature that the high-vertical accuracy and spatial resolution of LiDAR DEMs provide the topographic detail needed for more accurate and current surface water information (Casas et al., 2006; Colson et al., 2006; Jones et al., 2008; Murphy et al., 2008; Stoker et al., 2008; Höfle et al., 2009; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; López-Torrijos et al., 2012; Petroselli, 2012; Quinn and López-Torrijos, 2012). Li and Wong (2010) state that high-resolution LiDAR DEMs offer superior results in extracting river networks. Murphy et al. (2008) indicate that LiDAR-derived DEM networks were the most accurate representation of field-mapped networks, even more accurate than photo-derived networks. Poppenga et al. (2010) conclude that LiDAR-derived surface channels more closely reflect what is currently occurring on the landscape. Jones et al. (2007) reported that the use of airborne LiDAR data and GIS technology allows the rapid production of detailed geomorphological maps of river valley environments. Liu et al. (2005) demonstrate that LiDAR-derived DEMs with high-accuracy and high-resolution offer the capability of improving the quality of hydrological features extracted from DEMs. According to the National Research Council (2009), high-quality digital mapping is essential to communicating flood hazards to those at risk. Their report, Mapping the Zone, Improving Flood Map Accuracy, concludes that even the most expensive aspect of making more accurate maps — collecting high-accuracy, high-resolution topographic data — yields more benefits than costs.

Materials and Methods

Study Areas

We tested methods for detecting change in hydrography in both low relief and rugged terrain. The first study area is located in the headwater region of the Slip-up Creek watershed in the low relief plains of eastern South Dakota (Figure 1). Surface waters flow through this agricultural landscape that has numerous culverts and bridges that serve as conduits for overland drainage. Since the 1:24,000-scale NHD flow lines were digitized for this study area, some of the meandering streams in the watershed have been channelized to optimize the amount of arable land or for road construction. These types of surface channel changes are frequent throughout the drainage area, and collectively have altered the slope of surface water drainage. Although a potential source of agricultural surface water run-off, Slip-up Creek was considered to be one of several potential water supply sources for the nearby city of Sioux Falls, the largest and fastest growing city in the state (Barari et al., 1989).

Figure 1.

 Hydrography Change Detection Was Conducted in the Headwater Region of the Slip-up Creek Watershed in the Low Relief Plains of Eastern South Dakota Where Some Meandering Streams Have Been Channelized. In this study area, the minimum elevation value is 450.45 m and the maximum elevation value is 502.79 m. The area of the study site is 26.65 km2.

The second study area is located in the Tehachapi Mountains along Interstate 5 (I-5) in southern California (Figure 2). I-5, also known as the Golden State Freeway, traverses rugged terrain subject to earthquakes, intense weather events, and landslides. These mountainous slopes have been altered within the last 50 years to construct an important commerce corridor that connects metropolitan Los Angeles with the agricultural San Joaquin Valley. Massive amounts of earthen materials were carved out of mountains and deposited into canyons to construct the eight-lane I-5 freeway (Foster, 2003; Scott, 2003), an impressive major public works project that altered the landscape considerably and impacted the directional flow of surface water. Topographic changes such as these have been quantified by Gesch (2006) in a national inventory of significant topographic changes in the United States (U.S.); the inventory was based upon seamless multitemporal elevation data that are valuable for detecting hydrography change in high relief terrain.

Figure 2.

 Hydrography Change Detection Was Conducted in Rugged Terrain Along the I-5 Corridor in Southern California Where Massive Amounts of Earth Materials Were Carved Out of Mountains and Deposited in Canyons to Construct the Freeway. In this study area, the minimum elevation value is 630.21 m and the maximum elevation value is 1,116.70 m. The area of the study site is 5.10 km2.

Data for Hydrography Change Detection Analysis

High-resolution LiDAR DEMs were used in both study areas to extract vector LiDAR surface channels. The LiDAR DEMs were obtained from the USGS NED (Gesch et al., 2002; Gesch, 2007), which is the elevation component of The National Map (Kelmelis et al., 2003). The horizontal resolution of the LiDAR DEMs for both study areas was 1/9-arc-second, or approximately 3 m. The vertical accuracy for the Slip-up Creek study area (Figure 1) was 18.5 cm root-mean-square error (RMSE) on open bare earth terrain and 37.0 cm RMSE in vegetative areas (Poppenga et al., 2010). The vertical accuracy as reported by the LiDAR vendor for the southern California study area (Figure 2) was 11.0 cm RMSE on open bare earth terrain and 14.3 cm RMSE for vegetative areas.

Ancillary aerial imagery obtained for the Slip-up Creek study area was collected within one month of the LiDAR acquisition. Both LiDAR and aerial imagery acquisitions were a collaborative effort between USGS, City of Sioux Falls and Minnehaha County, South Dakota, and Sanborn Map Company, Inc. Ancillary aerial imagery for the southern California study area was accessed from Bing Maps aerial imagery web mapping service, which is available in licensed ArcGIS products.

Mapped 1:24,000-scale hydrography vector flow lines were obtained from the USGS high-resolution NHD (Simley, 2006). The positional accuracy for the high-resolution NHD was compiled to meet the NMAS guideline, which indicates mapped features need to be within 12.2 m of their true location at a 90% confidence level for 1:24,000-scale maps (USGS, 1999; National Digital Elevation Program, 2004; Maune et al., 2007b).

Comparable Scale for LiDAR Surface Channels and Mapped Hydrography

In order to conduct hydrography change detection for the high-resolution NHD, the scale of LiDAR surface channels should be analogous to 1:24,000-scale mapped hydrography or USGS topographic base maps before applying selective drainage methods (Poppenga et al., 2010, 2012). To obtain comparable scales for LiDAR surface channels, NHD flow lines were analyzed to extract the end points on the uppermost location of the NHD headwater reaches. The NHD headwater points and LiDAR flow direction grid were used to define comparable flow paths downstream. The cells in the LiDAR flow direction raster represent flow to the adjacent cell in the steepest downslope direction (Jenson and Domingue, 1988). Each cell in this raster is coded with a value representing one of eight neighboring cells that has the steepest downward slope. The cell-to-cell flow connectivity of the LiDAR flow direction raster provides the capability to route a flow path across the surface. The uppermost end point of each NHD flow line is used to initiate a starting point in the LiDAR DEM for deriving a downstream path using GIS cost path analysis techniques. This creates a raster path from the NHD headwater points downstream to the edge of the LiDAR DEM. This technique is often called downstream trace or raindrop trace. Using the selective drainage methods (Poppenga et al., 2010, 2012), elevations above culverts and bridges were identified and adjusted to the lowest elevation cell value within depressions that needed hydro-enforcement. The hydrologically enforced LiDAR flow direction grid was converted to vector surface channels representing a high-resolution raster DEM. The LiDAR surface channels were generalized with a maximum offset tolerance of 3 m to derive surface channel representation at a level of detail more comparable to the mapped hydrography.

Some headwater end points on the uppermost reaches of NHD flow lines may not be spatially aligned with the LiDAR elevation data. For example, if an NHD headwater point were located on the other side of a LiDAR elevation ridge rather than in the study area watershed, the LiDAR flow direction would spill downstream into the adjacent watershed causing spatial inaccuracies in the derived LiDAR surface channel. In such a case, the NHD headwater point should be repositioned to an appropriate LiDAR raster pixel located inside the study area watershed for the cost path analysis to follow the correct flow direction in the LiDAR DEM. Identifying NHD headwater points that are spatially misaligned in the LiDAR DEM is one step that is necessary to obtain comparable scales between NHD flow lines and LiDAR surface channels.

The Usefulness of LiDAR DEMs for Detecting Changes in Mapped Hydrography

Horizontal and Vertical Components of Elevation Data

Both horizontal and vertical components of elevation data were useful for detecting hydrography changes in the study areas. Using LiDAR DEMs, two methods were employed to quantify displacements between the LiDAR surface channels and NHD flow lines: (1) quantifying horizontal offsets between vector LiDAR surface channels and vector NHD flow lines that exceeded a 12.2-m threshold, resulting in horizontal candidate corresponding change pairs, and (2) quantifying vertical elevation differences between candidate corresponding change pairs. The 12.2-m threshold is based on a NMAS guideline that indicates mapped features need to be within 12.2 m of their true location at a 90% confidence level for 1:24,000-scale maps (USGS, 1999; National Digital Elevation Program, 2004; Maune et al., 2007b). Using both the horizontal and vertical components of LiDAR DEMs ensured that locations suspected of hydrography change were not only quantified by measuring horizontal differences but also validated by vertical elevation differences. These methods make full use of the three-dimensional nature of LiDAR DEMs. Such quantification is important for ranking the magnitude of hydrography change for individual locations. Because it is not economically feasible to update the entire 1:24,000-scale NHD, horizontal and vertical rankings are important for automated methods to detect locations of hydrography change so that the largest discrepancies can be updated in the NHD to bring currency to the dataset.

Horizontal Displacement – Candidates for Corresponding Change Pairs

To identify horizontal displacements in the study areas, the first step was to buffer LiDAR surface channels (channels) and mapped hydrography flow lines (flow lines) by 12.2 m on both sides of the line networks. This step was needed to create reciprocal buffers rather than to measure the horizontal distance between the buffers. The next step was to differentiate between channel or flow line segments that were located inside or outside of their reciprocal buffers. This boundary was defined by the intersection of the line segments and their reciprocal buffers. For example, line segments located within their reciprocal buffers were offset <12.2 m. However, line segments located outside of their reciprocal buffers were offset by more than 12.2 m, and thus were considered candidates for corresponding change pairs (Figure 3). The corresponding change pair line segments were extracted from their original datasets and horizontal offsets were quantified by associating each channel with the nearest flow line (and vice versa). This was accomplished by creating a feature class containing midpoints for each channel and flow line change pair, determining the nearest midpoints between the change pair, and joining the attribute tables to define a complete association for all candidates. The distances between each change pair midpoints were measured and ranked to define the largest horizontal displacements.

Figure 3.

 NHD Flow Line and LiDAR Surface Channel Segments That Exceeded Their Reciprocal Buffers Were Considered Candidates for Hydrography Change and Were Extracted as Corresponding Change Pairs to be Quantified. The USGS 2008 aerial photograph was acquired within one month of the LiDAR acquisition.

These methods identified all channels and flow lines exceeding their reciprocal 12.2 m buffers (candidate corresponding change pairs) regardless of their linear length. Some line segments located outside of their reciprocal buffers were very short and did not constitute significant hydrography change because the reciprocal buffers were in close parallel proximity to each other and the horizontal offset barely exceeded 12.2 m. In some locations, the channel and flow line confluences were spatially misaligned. This anomaly caused the linear lengths of the candidate corresponding change pairs to be substantially different. Therefore, additional criteria were defined to exclude change pairs that had segment lengths <15 m each. Also, if the flow line or channel had substantially uneven line lengths in confluence locations and at least one of the line lengths was <20 m, they were excluded from the horizontal displacement results. The channels and flow lines with horizontal displacements exceeding 12.2 m that remained following exclusions for short segments and uneven line lengths at confluences were considered candidates for change pairs. The 15- and 20-m thresholds were arbitrary and were based on interactive viewing and analysis of the reciprocal buffering results. Different thresholds may need to be applied if this method is used in a different study area.

In addition to identifying and ranking the horizontal displacements between change pairs, the length of each flow line and channel were quantified from upstream to downstream. Based upon these measurements, a length ratio was derived by dividing the length of the flow line by the length of the corresponding channel. The length ratio was then ranked consecutively from highest length ratio to lowest length ratio. When the lengths of the corresponding flow line and channel differed greatly, resulting in a high length ratio, then the probability of surface hydrography change was greater. The probable candidate locations for hydrography change, based upon the horizontal displacements between channels and flow lines, were quantitatively identified by combining the horizontal displacements with the length ratios for a composite ranking. The candidate corresponding change pairs with the highest composite ranks were considered highly probable locations for hydrography change that needed to be updated in the mapping.

Vertical Displacement – Elevation Differencing of Corresponding Change Pairs

LiDAR DEMs were used to define elevation values for corresponding change pairs (channel and flow line segments) that were horizontally offset by more than 12.2 m. The elevation values were used to quantify vertical elevation differences between the change pairs. This was accomplished by overlaying each channel and flow line segment on a LiDAR DEM in a GIS. The line segments were divided into 10 percentage parts (10% of the line length in the downstream direction), and the elevation values at the end points of each percentage part was recorded. The differences in elevation values from one adjacent percentage part to the next were converted to absolute values that were summed to define the absolute vertical elevation differences per change pair. The summed values were ranked consecutively from greatest to least vertical elevation differences. A simple addition of the ranked scores for horizontal displacements/length ratios and vertical elevation differences was employed to produce composite ranking.

To visualize vertical elevation differences between corresponding change pairs, elevation profile graphs were created using a customized profile graph tool that was developed for this research to obtain functionality not available in the standard ESRI profile tool. This tool samples elevation values directly beneath overlain line segments rather than creating profiles from a drawn (digitized) line. The tool uses an automated elevation sampling process that employs Python in a geoprocessing environment to create routes along a vector line, such as a flow line or channel. Linear referencing was initiated along the routes to create points at specified measured intervals. Initially, the points were created at intervals equivalent to the (3-m) horizontal resolution of the LiDAR DEMs in the study areas. However, this sometimes resulted in multiple points being created within the same elevation pixel whenever the linear referencing crossed the pixel diagonally. Therefore, additional sampling distance criteria were defined. To determine the appropriate elevation profile tool-sampling distance for the 3-m LiDAR DEMs, the length of the hypotenuse, or the length of the diagonal of the pixel, was derived by calculating the square root of the sum of the squares of the LiDAR DEM resolution, for example:

image

Thus, the elevation profile tool-sampling distance used in the study areas needed to be >4.24264 m. The resulting points contained elevation value attributes derived from the LiDAR DEM that were used to create flow line and channel elevation profile graphs. Profile graphs revealed if a consistent downslope path was present in the corresponding change pair segments. If the elevation profile graph did not continue on a downslope path, we inferred that the hydrography was not vertically integrated with the elevation data and the change pair segments were potential locations of hydrography change.

Results – Slip-Up Creek Study Area

Ranking of Horizontal Displacements and Length Ratios

Within the Slip-up Creek study area (Figure 1), there were 114 cases where the horizontal displacements between hydrography flow lines and LiDAR surface channels exceeded 12.2 m. The distribution of the horizontal displacements is shown in Figure 4. The greatest horizontal displacement was calculated at 160.7 m (Figure 3 and Table 1, change pair IDs #85/133). For this location, the mapped hydrography needs updating as the stream course was altered and channelized several decades after the creation of the NHD flow line (Figure 3). In addition to ranking horizontal displacements, the difference in the lengths of the features in each change pair was measured. Based upon those measurements, length ratios were derived by dividing the length of the flow line segment by the length of the channel segment. The maximum calculated length ratio was 3.5 for corresponding change pair IDs 85/133 (Table 1). This location also ranked as the highest horizontal displacement (Figure 3 and Table 1).

Figure 4.

 Distribution of Horizontal Displacements in the Slip-up Creek Study Area.

Table 1. Composite Rankings of Greatest Horizontal Displacements and Length Ratios in the Slip-up Creek Study Area.
Change PairsHorizontal Displacement Between Mapped Flow Line and LiDAR Surface Channel Segments (m)Ranking of Horizontal DisplacementsMapped Flow Line Segment Length (m)LiDAR Surface Channel Segment Length (m)Length Ratio: Mapped Flow Line/LiDAR Surface ChannelRanking of Length RatioComposite Ranking of Horizontal Displacement Rankings and Length Ratio Rankings
Mapped Flow Line Segment IDLiDAR Surface Channel Segment ID
 85133160.71435.8122.93.512
  013470.64245.792.22.726
  313792.13387.8213.41.858
  113545.110124.477.41.61222
 178529.42846.822.32.1331
 2212539.516119.376.01.61531
1195337.12194.459.21.61435
1033539.51735.425.91.41835
 951743.712127.3101.41.32436
 4211933.324186.6138.21.41943

By combining the greatest rankings for horizontal displacements and length ratios, the most probable locations in the Slip-up Creek study area where hydrography change may have occurred are quantitatively identified in Table 1. Based upon the composite rankings, which are a simple addition of ranked scores, the location with the highest probability of hydrography change is change pair IDs #85/133, which is illustrated in Figure 3.

Using Ancillary Aerial Imagery for Defining Horizontal Change Pairs

A few horizontal candidate corresponding change pairs did not appear to be correlative, especially in vegetative areas. For example, in some locations, the LiDAR channel diverged from what appeared to be a stream course. Most likely, the cause was inherent in misclassified bare earth LiDAR points. In other words, in LiDAR acquisitions, multiple points from one transmitted pulse can be returned to the (airborne) platform. If some of those points in the vegetative areas are slightly suspended above the ground, they should be classified as vegetation. However, because the LiDAR point cloud classification process is subjective, LiDAR points within centimeters of the ground surface may be inadvertently interpreted as bare earth points. Consequently, the misclassified points are used to triangulate a DEM that affects the downslope path of surface flow. Therefore, ancillary aerial imagery was needed to verify some of the horizontal change pairs in vegetative areas.

Extracting surface channels can be problematic if the bare earth LiDAR DEMs contain anomalies, which demonstrates the need for consistent and successful bare earth filtering. Fortunately, in the Slip-up Creek study area, the LiDAR point cloud data were acquired within one month of an aerial imagery acquisition. Because of the temporal proximities of acquisition dates, the aerial imagery, or digital orthorectified photograph, was frequently used to decipher any horizontal discrepancy change pairs affected by vegetation artifacts in the LiDAR bare earth DEM.

Elevation Profile Graphs of Vertical Elevation Differences

To visualize the vertical elevation differences between corresponding change pair IDs #85/133 (Figure 3), elevation profile graphs were created (Figures 5 and 6). Figure 5A illustrates how LiDAR elevation values were sampled to create an elevation profile graph. Note that the sampled locations were overlain on a LiDAR-shaded relief image for visualization purposes only. The circles represent the locations of sampled LiDAR elevation values where the overlain NHD flow line did not exceed its reciprocal buffer. The squares represent sampled LiDAR elevation values where the overlain NHD flow line did exceed its reciprocal buffer (also see Figure 3). The sampled elevation values were used to create the elevation profile graph shown in Figure 5B. In the profile graph, the x-axis represents the distance in meters from upstream (left) to downstream (right) of the sampled locations and the y-axis represents the elevation values (m) that were sampled. The flow line segment in the profile graph (Figure 5B) corresponds to the circles and squares in Figure 5A and the flow line overlain on the aerial photograph (Figure 5C).

Figure 5.

 LiDAR Elevation Values Were Sampled Where the NHD-Mapped Hydrography Flow Line Was Overlain on the LiDAR DEM. The squares represent the flow line locations that exceeded its reciprocal LiDAR surface channel buffer (compare with Figure 6).

Figure 6.

 LiDAR Elevation Values Were Sampled Where the LiDAR Surface Channel Was Coincident with the LiDAR DEM. The circles represent the LiDAR surface channel locations that exceeded its reciprocal NHD-mapped hydrography flow line buffer (compare with Figure 5).

In Figure 5B, the flow line variations in the profile graph do not represent the vertical dimensions along a typical stream channel. The flow line may not correspond to an expected surface channel shape for several reasons: (1) the flow line may have been digitized from mapped hydrography that was less spatially accurate than the high-resolution, high-accuracy LiDAR DEM; (2) the flow line discrepancy could be due to compilation error when the NHD flow line was originally digitized; (3) the LiDAR channel does not reflect a downstream path due to incomplete vegetation removal or other bare earth DEM processing issues; (4) land management may have changed the elevation surface conditions over time; or (5) the stream channel course may have been changed over time from natural causes. It is probable that Items 4 or 5 apply to this particular location because in a 1958 USGS aerial photograph of the area (Figure 5C), the flow line follows the stream course. Therefore, in 1958, the flow line was spatially correct. However, in a 2008 aerial photograph (Figure 6C), the stream course has been altered. Regardless of the cause, the methods described in this article detected this large discrepancy.

In Figure 6A, the circles represent the locations of sampled LiDAR elevation values where the overlain channel did not exceed its reciprocal buffer. The squares represent sampled LiDAR elevation values where the overlain channel exceeded its reciprocal buffer (also see Figure 3). The sampled locations (Figure 6A) correspond to the profile graph (Figure 6B) and aerial image (Figure 6C). The LiDAR channel in the elevation profile graph (Figure 6B) follows a continuous downslope path, whereas the NHD flow line in Figure 5B did not follow a downslope path.

A comparison of the elevation profile graphs in Figures 5B and 6B shows there are numerous locations where the flow line is vertically displaced from the channel. At one point, the elevation difference between the flow line and channel exceeds 6.82 m vertically. These variations are strong indicators of either a spatially misaligned dataset or actual hydrography change. Also, note the length difference between the flow line and the channel in the elevation profile graphs. The mapped hydrography flow line length is substantially longer than the length of the LiDAR channel. Therefore, in the case of change pair IDs #85/133, the flow line is not only horizontally and vertically displaced from the actual streambed but also has a substantially different stream length.

Ranking of Absolute Vertical Elevation Differences

Vertical elevation differences were quantified and ranked for all flow line and channel segments that exceeded the 12.2-m horizontal threshold in the Slip-up Creek study area. This is illustrated for change pair IDs #85/133 in Table 2. For each percentage part, the distance along the flow line or surface channel was calculated (cols. 2 and 6), and an elevation value was sampled from the LiDAR DEM (cols. 3 and 7). The flow line or channel elevation values per percentage part were subtracted from the next percentage part elevation values (cols. 3 and 7) to derive the vertical elevation difference per part (cols. 4 and 8). The vertical elevation differences per part were converted to absolute values (cols. 5 and 9). The absolute vertical elevation difference values were summed (bottom of cols. 5 and 9) so that each change pair could be ranked.

Table 2. Computation of Absolute Vertical Elevation Differences for the Change Pair of Flow Line ID #85 and Surface Channel ID #133 in the Slip-up Creek Study Area.
Percentage PartDistance Along Flow Line ID #85 (m)Flow Line ID #85 Sampled Elevation Values (m)Vertical Elevation Difference Per Percentage Part (m)Absolute Vertical Elevation Difference (m)Distance Along Surface Channel ID #133 (m)Surface Channel ID #133 Sampled Elevation Values (m)Vertical Elevation Difference Per Percentage Part (m)Absolute Vertical Elevation Difference (m)
  00.0459.1--0.0459.1--
 1043.6459.50.40.413.1459.10.00
 2087.2459.4−0.10.126.3459.10.00
 30130.8462.32.92.939.4459.10.00
 40174.3465.93.63.652.5459.10.00
 50217.9464.7−1.21.265.6459.10.00
 60261.5462.7−2.02.078.8459.10.00
 70305.1463.30.60.691.9459.10.00
 80348.7459.0−4.34.3105.0459.0−0.10.1
 90392.3463.54.54.5118.1459.00.00
100435.8459.1−4.44.4131.3459.00.00
Sum   24.0   0.1

In Table 2, the flow line elevation values for ID #85 increase or decrease between subsequent percentage parts (col. 3). This is indicative of an elevation profile along a mapped flow line that is not consistently moving downslope. In contrast, the surface channel elevation values for ID #133 (col. 7) are either the same or decrease, indicating an elevation profile along the LiDAR channel that correctly decreases consistently downslope.

Table 3 shows the absolute vertical elevation rankings for several horizontal change pairs. The greatest absolute vertical elevation difference for mapped flow line and LiDAR surface channel segments is 24.1 (col. 5) (change pair IDs #85/133). This change pair ranked highest in horizontal displacements (Table 1) and vertical elevation differences (Table 3) in this study area.

Table 3. Ranking of Greatest Absolute Vertical Elevation Differences in the Slip-up Creek Study Area.
Change PairsMapped Flow Line Segment Absolute Vertical Elevation Difference (m)LiDAR Surface Channel Segment Absolute Vertical Elevation Difference (m)Sum of Absolute Vertical Elevation Differences for Mapped Flow Line and LiDAR Surface Channel (m)Ranking of Summed Absolute Vertical Elevation Differences
Mapped Flow Line Segment IDLiDAR Surface Channel Segment ID
 8513324.00.124.11
 98253.63.16.72
 361132.92.15.03
133611.92.24.14
 451223.60.03.65
 14211.71.63.36
 50411.61.63.27
129931.21.93.18
143712.50.53.09
 67760.82.02.810

Results – Southern California Study Area

Ranking of Horizontal Displacements and Length Ratios

Our methods identified 28 horizontal displacements between hydrography flow lines and LiDAR surface channels that exceeded 12.2 m in the southern California study area. The greatest horizontal displacement between mapped flow line and LiDAR surface channel segments was calculated at 384.6 m (Table 4, change pair IDs #4/17). Although this change pair was ranked first in horizontal displacements (col. 4), the change pair was not ranked with the greatest length ratio (col. 8).

Table 4. Composite Rankings of Greatest Horizontal Displacements and Length Ratios in the Southern California Study Area.
Change PairsHorizontal Displacement Between Mapped Flow Line and LiDAR Surface Channel Segments (m)Ranking of Horizontal DisplacementsMapped Flow Line Segment Length (m)LiDAR Surface Channel Segment Length (m)Length Ratio: Mapped Flow Line/LiDAR Surface ChannelRanking of Length RatioComposite Ranking of Horizontal Displacement Rankings and Length Ratio Rankings
Mapped Flow Line Segment IDLiDAR Surface Channel Segment ID
141034.03139.0137.91.036
 22725.9593.989.31.127
 417384.61705.91,126.90.6910
24216.31024.221.51.1111
 32619.8732.143.30.7512
13923.4645.367.40.7612
27517.7922.834.90.7716
1916184.72182.1445.30.41517
 72015.61348.662.80.8417
 51819.1827.645.50.61220

The horizontal displacement for change pair IDs #4/17 is shown in Figure 7 (circle). The source location (headwater point) of both the flow line (dark line) and the channel (light line) are spatially consistent, yet, the path that each takes thereafter is not consistent. The NHD mapped hydrography was digitized for a time frame prior to excavation of the mountain, but with the topographic changes the stream course will no longer follow the same path from the mountaintop downslope to the valley as does the NHD flow line. The land was dramatically altered. Thus, the LiDAR surface channel that commences at the same location as the flow line follows a different downslope path.

Figure 7.

 Southern California Study Area Greatest Horizontal Displacement (circle) Between NHD Flow Line and LiDAR Surface Channel.

Composite Ranking of Horizontal Displacements and Length Ratios

The greatest horizontal displacements and length ratios for corresponding change pairs in the southern California study area were combined as composite rankings (Table 4). The top ranking change pairs in Table 4 were considered probable locations for hydrography change; three of these are all shown in Figure 7 (Table 4, change pair IDs #14/10, 4/17, and 19/16). The flow lines (dark line) in Figure 7 need to be updated because of topographic changes involving removal of mountaintop earthen materials (Road Cut) that were deposited into the adjacent valley (Road Fill). These topographic changes have altered the downslope directional flow of water. Thus, Figure 7 exemplifies the importance of topographic change inventories for detecting hydrography change and demonstrates the importance of using LiDAR DEMs to detect conduit locations that route flow under roads (Poppenga et al., 2010, 2012).

Topographic change inventories are useful not only for detecting locations where existing hydrography should be updated and for identifying conduit locations to enforce downstream surface channels, but also for locating where new hydrography should be defined. For example, before alterations of the mountains in this study area, a flow line would not have been defined on a mountain summit. However, once the mountaintop was altered (Figure 7, Road Cut), the slope was also altered; therefore, new hydrography flow lines need to be defined.

Ranking of Absolute Vertical Elevation Differences

Absolute vertical elevation differences were quantified for all corresponding change pairs in the southern California study area (Table 5). A 10th percentile cumulative sampling of elevation differences along change pair line lengths was calculated using the elevation profile tool. A composite ranking of the sum of the absolute vertical elevation differences shows that the greatest ranking locations for absolute vertical elevation differences (Table 5) also ranked highly among the composite rankings for horizontal displacements and length ratios (Table 4), all of which are visible in Figure 7. To visualize the vertical elevation differences for the highest ranking corresponding change pair IDs #4/17 (Table 5), elevation profile graphs were created (Figures 8 and 9). The hydrography flow line in Figure 8, which has a source at the same location (elevation value) as the LiDAR surface channel in Figure 9, rises up and over the I-5 freeway and then descends on a fluctuating path down the mountain slope. In comparison, the LiDAR surface channel in Figure 9 continues on a steepest downslope path.

Table 5. Composite Rankings of Greatest Absolute Vertical Elevation Differences in the Southern California Study Area.
Change PairsMapped Flow Line Segment Absolute Vertical Elevation Difference (m)LiDAR Surface Channel Segment Absolute Vertical Elevation Difference (m)Sum of Absolute Vertical Elevation Differences for Mapped Flow Line and LiDAR Surface Channel (m)Ranking of Summed Absolute Vertical Elevation Differences
Mapped Flow Line Segment IDLiDAR Surface Channel Segment ID
 417173.8144.0317.81
191675.078.7153.72
141039.537.376.83
24219.51.521.04
 51816.22.218.45
16137.89.016.86
 82113.02.315.37
 72011.72.113.88
1398.83.512.39
1278.51.39.810
Figure 8.

 Elevation Profile Graph of NHD-Mapped Hydrography Flow Line ID #4 (Table 5) in the Southern California Study Area (see Figure 9 for the Corresponding LiDAR Surface Channel).

Figure 9.

 Elevation Profile Graph of LiDAR Surface Channel ID #17 (Table 5) in the Southern California Study Area (see Figure 8 for the Corresponding Hydrography Flow Line).

The results for absolute vertical elevation differences were consistent in both the South Dakota and California study areas. The LiDAR surface channel, which is derived solely from the LiDAR DEM, commences at the same location (elevation value) as the hydrography flow line and continues on a consistent downslope path. However, the hydrography flow line, when overlain on the LiDAR DEM, does not follow a continuous downslope path.

Discussion

Benefits of Surface Channels Derived from LiDAR-Derived DEMs for Mapped Hydrography

One of the benefits of using LiDAR DEMs to identify and track changes for updating NHD mapped hydrography is that the effort of reviewing an entire dataset for currency becomes more manageable. By using hydrologically enforced LiDAR-derived surface channels (Poppenga et al., 2010) in tandem with hydrography flow lines and LiDAR DEMs, horizontal and vertical discrepancies can be detected to determine whether actual hydrography change has occurred over time. Using spatially referenced LiDAR surface channels to update mapped hydrography results in a vector flow line dataset that is fully integrated with the elevation data. Additional benefits for many spatial analysis applications will be available by integrating elevation data with NHD mapped hydrography flow lines.

A misconception about deriving surface channels from LiDAR DEMs is that users perceive the information as a static dataset representing one map scale (1:24,000). The purpose of extracting vector surface channels from LiDAR-derived flow direction grids is to have the ability to model surface waters at different drainage density thresholds. Although LiDAR surface channels can represent the current state of surface waters at a particular map scale, the methods used to create the channels are highly efficient for defining any specified drainage density threshold. This is beneficial for NHD mapped hydrography in that LiDAR-derived surface channels can be generated at drainage densities similar to medium (1:100,000) or even local (1:5,000) resolution NHD flow lines, without regenerating another flow direction grid or redigitizing an entire NHD network.

There may be concerns whether LiDAR-derived surface channels should be used to update 1:24,000-scale NHD. Perhaps, a more relevant concern is what map scale can be used for NHD flow lines in a GIS for various applications. If the intended purpose of mapped hydrography is for the data to be used at the scale from which it was created (such as 1:24,000 map scale), then what map accuracy standard should apply if the 1:24,000-scale data are used at a finer scale in a GIS? For example, if 1:24,000-scale flow lines were used in a GIS at a research study area scale of 1:1,200, then according to the NMAS, the 90% confidence level of the hydrography should be within 1.02 m rather than the originally intended 90% confidence level of 12.2 m for 1:24,000 map scale (USGS, 1999; National Digital Elevation Program, 2004; Maune et al., 2007b). Therefore, considering that the use of hydrography has evolved beyond its intended purpose of creating paper maps into the more frequently used digital world of mapping, the confidence levels of the defined error thresholds for mapped hydrography need to be evaluated.

As researchers, educators, and the general public become more reliant upon geographic data for decision-making processes, the gateways to geographic user communities, such as The National Map (Kelmelis et al., 2003), have become increasingly important. However, if data served through these gateways do not contain current information, users will be impacted. Figure 10 is an image of The National Map Viewer that shows some of the greatest horizontal discrepancies (Table 1) and vertical elevation differences (Table 2) in the Slip-up Creek study area (Figure 1). Although the aerial photograph in Figure 10 may be current, the mapped hydrography is shown as it was relevant in 1958 (Figure 5C, aerial photograph). Had the LiDAR-derived surface channels been used to update the stream courses, the flow lines visible on The National Map Viewer in Figure 10 would display up-to-date hydrography information. Therefore, the usefulness of surface channels derived from LiDAR DEMs for mapped hydrography goes beyond digital mapping within a GIS; it is also beneficial for gateways to geographic user communities.

Figure 10.

The National Map Viewer Displays NHD Mapped Hydrography Flow Lines in the Slip-up Creek Study Area. The arrows show where the hydrography needs to be updated.

Last, if LiDAR-derived surface channels are considered for updating mapped hydrography, the data will most likely be generalized for its relevant purpose. Users should be cautioned that once the generalization has taken place the surface channels may no longer be vertically integrated with the source elevation LiDAR DEM. Therefore, any derived elevation profile (from upstream to downstream) may not follow a continuous downslope path. To ensure complete vertical integration between datasets, data providers would need to generate the hydrography without generalization.

USGS Topographic Change Inventory for Detecting Hydrography Change

As modifications to the landscape occur throughout time, the slope of the land surface is altered. Because the slope of the land, or the topography, is a determining factor in surface water drainage, changes to the landscape can affect stream courses (Gesch, 2006). Therefore, in addition to detecting hydrography changes by measuring linear horizontal displacements and profiling vertical elevation differences between flow lines and channels, hydrography change detection could also be conducted with a quantitative analysis of topographic changes. An example of hydrography change resulting from topographic change is illustrated in the southern California study area shown in Figures 2 and 7. This high relief study area was selected because of detection of topographic changes resulting from road construction. These types of land surface alterations have been quantified in a topographic change inventory developed by the USGS.

The national inventory of significant topographic changes in the U.S. (Gesch, 2006) was derived from the seamless NED (Gesch et al., 2002; Gesch, 2007), Shuttle Radar Topography Mission (SRTM) (Farr et al., 2007), and the National Land Cover Dataset (1992) (NLCD) (Homer et al., 2004). The national inventory contains spatially referenced topographic change information of significant changes, such as anthropogenic landform modifications, that have occurred in the U.S. The topographic modifications include surface mining, road construction, urban development, dam construction, and landfills. Elevation modifications that remove local drainage divides or that fill local stream valleys alter the surface hydrology of watersheds draining the disturbed area (Gesch, 2006).

At the time the multitemporal topographic change study was conducted (2005-2006), the NED and SRTM data consisted of a nominal spatial resolution of 1 arc-second (∼30 m), while the NLCD provided land cover information at a 30-m resolution (Gesch, 2006). Within the last decade, the NED has evolved from a static 30-m resolution dataset to a multiresolution dataset with increased vertical accuracy that consists of not only 30-m DEMs but also 10- and 3-m DEMs (Gesch, 2007). Therefore, with the advent of the multiresolution NED, comprised from various source data acquisitions including LiDAR, the potential exists for a topographic change analysis conducted from multitemporal LiDAR DEMs. This opens up exciting new possibilities for a high-resolution, high-accuracy topographic change analysis useful for detecting hydrography change at a fine scale.

Conclusion

Whether by anthropogenic or by natural causes, as land surface conditions change over time, the spatial information that geographically represents hydrography needs to be updated to reflect the current state of surface waters. Accomplishing this monumental task by redigitizing an entire nationwide NHD hydrography dataset for currency would be exceedingly expensive. To improve the efficiency and reduce costs associated with updating NHD mapped hydrography, we developed methods to detect only the locations where changes in hydrography have occurred. These methods employ highly detailed LiDAR DEMs to define surface channels that are beneficial for hydrography change detection.

Although digitized hydrography data may appear to be vertically integrated with other geospatial datasets, they are often not vertically integrated with the ground surface elevation data. Because spatial accuracy and currency are expected by those who use hydrography data for water-related decisions, methods using LiDAR DEMs to detect hydrography changes provide an efficient solution for updating the NHD. LiDAR point cloud data have become more prevalent as source data for generating DEMs with high spatial resolution and high vertical accuracy. These elevation data contain ground surface detail that is valuable for defining the direction that water would flow based upon gravity. Although frequently referenced by their high horizontal resolution, the actual inherent value of LiDAR DEMs is their vertical component, or the elevation value of the topographic ground surface. Therefore, hydrography change detection methods discussed in this article were based upon identifying NHD-mapped hydrography that exceeded the horizontal defined error thresholds established by NMAS, and based upon quantifying the vertical component of mapped hydrography.

We show the usefulness of surface channels derived from LiDAR DEMs for detecting changes in various types of topography for updating NHD-mapped hydrography. The LiDAR surface channels were generated for study areas located in both low relief and rugged terrain, and were compared with available 1:24,000-scale NHD hydrography flow lines to detect horizontal discrepancies that exceeded the 12.2-m NMAS guideline. The line sets, or corresponding change pairs, that exceeded the NMAS guideline were quantified and ranked according to their horizontal displacements and length ratios. Additionally, absolute vertical elevation differences were quantified and ranked for the corresponding change pairs by using a newly developed elevation profile tool. A composite ranking of horizontal displacements, length ratios, and absolute vertical elevation differences defined locations within each study area that were considered candidates for hydrography changes. These change metrics were developed for the purpose of directing attention to those locations that are most in need of being updated in the NHD. By using both the horizontal and vertical components of LiDAR DEMs, it is possible to detect changes in surface water channels that are valuable for updating the NHD-mapped hydrography.

Acknowledgments

The authors wish to thank Norman B. Bliss, Ph.D., Principal Scientist, ASRC Research and Technology Solutions (ARTS), contractor to the USGS Earth Resources Observation and Science (EROS) Center, for his scientific and technical review of this manuscript. The authors also wish to thank three anonymous reviewers from the Journal of the American Water Resources Association (JAWRA) for providing valuable comments that significantly improved the manuscript. Acknowledged for her management of the LiDAR-derived digital elevation data within the National Elevation Dataset (NED) is Gayla Evans, Geographer, USGS EROS, Sioux Falls, South Dakota.

Ancillary