Evaluation of Existing GIS Hydrological Toolsets for Modeling Stream Networks with LiDAR and Updating the National Hydrography Dataset (NHD)

The Oregon LiDAR Hydrography Pilot Project Evaluation of Existing GIS Hydrological Toolsets for Modeling Stream Networks with LiDAR and Updating the National Hydrography Dataset (NHD) Bill Kaiser (USFS), Craig Ducey (BLM), and Dan Wickwire (BLM) August 9, 2010

2 Table of Contents ACKNOWLEDGEMENTS... 3 INTRODUCTION... 4 D8 FLOW DIRECTION SENSITIVITY ANALYSIS AT PANTHER CREEK... 7 Summary and Future Recommendations... 14 INTEGRATION OF LiDAR DERIVED STREAM NETWORKS INTO NHD... 16 Introduction... 16 Evaluation and Issues... 18 LiDAR-based Stewardship Process... 21 Labor Cost Estimates... 23 OVERALL CONCLUSIONS AND RECOMMENDATIONS... 23 LITERATURE CITED... 25 SELECTED REFERENCES... 26 ACRONYMS... 27

3 ACKNOWLEDGEMENTS The authors would like to thank all team members and reviewers for their important contributions to this project: Jay Stevens, BLM Tamiko Stone, BLM Don Hsiao, BLM Chester Novak, BLM Brian Staab, USFS Chris Park, USFS Emmor Nile, ODF Bob Harmon, OWRD Any remaining errors in the report are the responsibility of the authors.

4 INTRODUCTION The Oregon hydrography LiDAR pilot project, sponsored by the State of Oregon, evaluates the use of LiDAR-based Digital Elevation Models (DEMs) to semi-automatically model stream networks for inclusion into the National Hydrography Dataset (NHD). Project conclusions and recommendations will guide future LiDAR derived delineations by Pacific Northwest Hydrography Framework (PNWHF) partners. Geospatial representations of surface water features stored in the NHD require continuous updating as land surface conditions change or more accurate data sources become available. To help satisfy this demand, terrain analysis of DEMs are frequently used to automate the delineation of stream networks (Tarboton et al. 1991, Osborn et al. 2001). These techniques possess several benefits over traditional surveys and manual interpretations of topographic maps. Specifically, results can be reproduced at reduced resource costs and are typically more easily integrated with other geospatial datasets for subsequent research. The raster data structure of DEMs, however, can make estimating flow direction and accumulation challenging when defining stream networks in areas of internal drainage or low-relief (Garbrecht and Martz 2000). Depending on the spatial resolution of the DEMs, fine-scale hydrologic features may be unobservable, resulting in oversimplifications of the stream network (Saunders 2000). DEMs produced from LiDAR (Light Detection and Ranging) data provide measurements of the earth s surface with improved accuracy, precision, and resolution compared to existing USGS National Elevation Dataset (NED) products (Lewis 2006). These enhancements are especially pronounced in forested areas where the ground is not visible (Lefsky et al. 2002) (Figure 1). Recent LiDAR acquisitions over large portions of western Oregon represent an opportunity to significantly improve the accuracy of NHD stream features. Presently, no standard guidelines exist for either deriving stream networks from LiDAR or integrating the results into the NHD. The absence of agreed-upon guidelines increases the likelihood that inconsistent stream network delineations will be submitted to NHD. In addition, challenges associated with terrain analysis of high-

5 resolution elevation data for automatically delineating stream networks are not completely documented. Figure 1. Visual comparison between (A) 3 foot natural color aerial photography, (B) 10 meter USGS National Elevation Dataset DEM, and (C) 3 foot LiDAR DEM products at Panther Creek, OR. Hillshading is used to improve visualization of DEMs. Blue lines are existing NHD streams.

6 This report reviews the existing ESRI ArcGIS Hydrological Tools (ESRI 2008) commonly used to generate stream features from DEM-based flow direction and accumulation estimates. Initial evaluations of a second, publicly-available hydrological toolset, Terrain analysis using Digital Elevation Models (TauDEM) (Tarboton 2009) were also conducted. As indicated above, the report establishes a proposed framework for delineating stream networks using LiDAR and a process for integrating the resulting dataset into the NHD. Labor related cost estimates associated with the entire process are also provided to PNWHF partners. This assessment is conducted using LiDAR DEMs acquired over the Panther Creek and Ashland Creek 12 digit Hydrologic Units (HU) pilot areas in Oregon (Figure 2). Figure 2. Location of the Panther Creek and Ashland Creek 12 digit HU pilot areas. This report fulfils the overall objectives of the project charter. However, some discrepancies between the charter and this report exist in light of a greater understanding of the required scope and content once the project was underway. Specifically, four pilot areas were narrowed down to the two shown in Figure 2. The charter also stipulated that only COTS (commercial-off-the-shelf) software was to be used, but the team began investigating the use of an academic software package.

7 D8 FLOW DIRECTION SENSITIVITY ANALYSIS AT PANTHER CREEK The suite of tools available in ESRI ArcGIS for extracting hydrological information from raster models of terrain elevation are based on an eight direction pour point (D8) conceptualization of surface water movement. In this approach, water flows from each cell into one of its eight adjacent neighbors in the direction of steepest descent. Assuming no surface water is impeded or lost to groundwater or evapotranspiration, the total amount of water flowing through a given cell is measured by calculating the cumulative area of all contributing upslope cells. Streams are defined by selecting cells whose flow accumulation values exceed a predetermined threshold. Breaks in the connectivity of modeled stream networks occur at areas of internal drainage called sinks or pits. These situations exist wherever a cell or cluster of spatially connected cells is surrounded by higher elevations in all directions, and are resolved by artificially raising elevation values within the sink until a valid flow direction can be determined. The recommended workflow for applying the ESRI ArcGIS hydrology tools is accomplished in seven successive steps (Figure 3). In its simplest form, analysts can affect the outcome of the modeling process by: a. Adjusting the spatial extent and/or cell size of the DEM, b. Defining alternative Z-limits for filling sinks, and c. Selecting different stream initiation flow accumulation thresholds. Spatial extent refers to the total area of the landscape under investigation. Drainage basins, for example, are conventionally used to define the spatial extent in hydrological applications. The grid cell size establishes the minimum observation unit and is functionally equivalent to the analysis grain. Altering either the spatial extent or cell size produces a corresponding change in the spatial variability characteristic of specific landscapes.

8 DEM Pre-processing (Cell Size) Fill Areas of Internal Drainage (All Sinks or Specific Z-limits) Calculate Flow Direction Identify Drainage Basin Extent (All Sinks Filled) Calculate Flow Accumulation Define Streams with Flow Accumulation Thresholds Convert Streams to Lines within Drainage Basin Extent Figure 3. The process workflow for generating stream networks with LiDAR-derived DEMs using ESRI ArcGIS D8 hydrology tools. Boxes with bold outlines identify steps accepting alternative model parameters. The default option to fill all sinks can be controlled by adjusting the Z-limit parameter and expressly defining the maximum allowable elevation difference between the bottom of a sink and its outlet pour point. For example, a sink with an elevation difference form it pour point equal to 4 feet would not be filled with a Z-limit set at 3 feet. As stated previously, flow accumulation modeled across a surface will terminate at any sinks left in a DEM and create discontinuities in the resulting stream network. Furthermore, sinks act as pour points when delineating drainage basins representing upslope contributing areas. These consequences are typically undesirable unless evidence supporting inclusion of a sink is available prior to analysis.

9 Flow accumulation measures the total number of upslope pixels draining into each DEM cell, and is used to model stream locations and events (e.g., periodicity) along the network. By converting pixel counts to areal measurements, stream channel initiation points and transitions in periodicity can be identified by specifying flow accumulation thresholds characteristic of the landscape being studied. Lower thresholds predict stream channels further upslope, resulting in higher density stream networks. For this report, we evaluated stream networks generated using variations in the ESRI ArcGIS D8 hydrology tool input parameters and a 3-foot cell size digital elevation model (DEM) interpolated from a LiDAR dataset acquired at Panther Creek, OR in August 2008. The LiDAR point-cloud acquisition and interpolation specifications for generating the bare-earth surface satisfy requirements set by the Oregon LiDAR Consortium (OLC)*. The DEM covers an approximately 6,700 acre area located within the western 30% of the Panther Creek WBD sixth-field watershed (Figure 4). Figure 4. The 2008 Panther Creek LiDAR acquisition covers the western portion of the Panther Creek 12-digit hydrological unit. * http://www.oregongeology.com/sub/projects/olc/default.htm

10 We compared results at cell sizes equal to 3, 6, 9, 12, and 15 feet after resampling of the original DEM using a cubic convolution algorithm. At each cell size, we filled all sinks and at Z-limits of 6 and 3 feet. Flow accumulations thresholds were identified along each of the 15 stream networks at one acre intervals between 2 and 8+ acres (Figure 5). The analysis extent was limited to those streams occurring within the drainage basin most closely approximating the geometry of the Panther Creek WBD 12-digit hydrological unit watershed. These basins were generated using the DEMs at each cell size where all sinks were filled. All processing steps were automated using Python scripting. Increasing cell size led to a corresponding expansion in the minimum length of stream channel line segments evident by a stair-step appearance along flow paths. The more homogenous patterns in flow direction resulted in a steady decrease in mean stream segment sinuosity and a greater propensity for line segments to follow straight-line paths over longer distances (Table 1). Overall, the density of streams within the modeled drainage basins was nearly constant with cell size. Flow accumulation values greater than 2 acres, however, exhibited a high spatial variability depending on the image cell size and treatment of sinks. When all sinks were filled, flow accretion into road-side ditches in the watershed s upper limits depreciated our confidence in the positional accuracy and accumulation values of higher-order streams. Specifically, water that would, in reality, pass under roads through culverts was instead diverted into ditches and carried downstream long distances until eventually spilling over the top of roads. As ditches were averaged out of the surface models at larger cell sizes, the frequency of these events diminished at the expense of finer-scale details. Flow line termination points were generally unpredictable when sinks were present, and did not exclusively coincide with likely culvert positions.

11 Figure 5. A subset of results for the D8 flow direction sensitivity analysis at Panther Creek. The tendency for surface flow to become trapped and carried within upslope ditches along roadways is evident in all frames except those where the cell size is adjusted to 15 feet.

12 Table 1. The effects of increasing cell size on the overall density and mean stream segment sinuosity within the corresponding drainage basin extent. An example stream segment provides visual evidence of the loss of local precision that occurs with increasing cell size. Cell Size Stream Density (All Sinks Filled) Mean Stream Segment Sinuosity Example Stream Segment 3 ft 0.00281 1.212 6 ft 0.00270 1.177 9 ft 0.00265 1.162 12 ft 0.00261 1.144 15 ft 0.00254 1.129

13 We observed fewer areas of internal drainage with increasing pixel grain (Table 2). Relative to all sinks, the number of areas filled with 6 and 3 foot Z-limits, however, generally increased with cell size. Approximately 35% of all sinks present in the original DEM surface were removed by re-sampling the pixel grain to 15 feet. As expected, limiting the number of sinks filled by specifying maximum elevation differences to pour points generated discontinuous stream networks. Table 2. The percentage of all cells elevated to fill areas of internal drainage for Panther Creek DEMs at different spatial resolutions. Pixel Grain (feet) All Sinks Z-limit = 6 feet Z-limit = 3 feet 3 1.21% 1.17% 1.11% 6 1.01% 0.96% 0.91% 9 0.91% 0.85% 0.81% 12 0.85% 0.80% 0.75% 15 0.78% 0.75% 0.69% Discontinuities in the modeled stream networks occurred wherever sinks were not filled (Figure 5). Inconsistencies in sink locations between the rescaled surfaces led to high variability in flow accumulation estimates and, consequently, contrasting representations of the hydrological stream network. If addressed with high-quality ancillary road information, not filling all sinks may be useful for identifying potential culvert locations within the study area. Known culvert locations could be serve as break-points to either modify the existing surface models or inform conversion of the original LiDAR point cloud to new DEMs.

14 Summary and Future Recommendations No single combination of cell size, sink depth, or flow accumulation threshold produced completely satisfactory results. Spurious stream segments branching off main channels occurring at each cell size suggested flow accumulation thresholds were not constant throughout the study area. Stream channel predictions appeared qualitatively better or worse in different regions of the study area depending on which combination of parameters was used. Attempts to rectify issues with flow lines generated after all sinks were filled were frustrated by the large number of streams captured by road ditches and complex channel definitions in areas experiencing active soil movement. Increasing cell size alleviated many of these situations, but resulted in only slight improvements in stream geometry predictions over the existing NHD and well below the potential accuracy and precision of the original LiDAR dataset. Stream features modeled using the ESRI ArcGIS D8 hydrology tools do not account for the numerous biophysical and human processes influencing the hydrology at Panther Creek. Consequently, the model results are accurate if the landscape is viewed as an impervious surface devoid of vegetation and without human modification. Future modeling efforts including ancillary information predictive of the spatial patterns resulting from these types of processes combined with spatial metrics descriptive of topographic conditions are likely to extend beyond the sophistication of the ESRI toolset. Trials conducted during this pilot exercise suggest adjusting the measurement scale while maintaining the cell size using alternative image processing techniques such as Gaussian convolution have the potential to more effectively mitigate localized variance in the LiDAR dataset. Future research should also consider more realistic models of flow direction such as the D-Infinity approach and the influence of topographic conditions beyond a focal cell s eight immediate neighbors (Tarboton, 2009) Panther Creek represents a unique opportunity to explore the use of LiDAR for stream channel delineation. Presently, five LiDAR acquisitions spanning the past four years are available for conducting comparative analysis within the study area. A multi-partner LiDAR research consortium with participants from the BLM, EPA, NRCS, non-government

15 groups, and others are investigating the value of LiDAR for a wide-range of environmental science applications. These efforts are being informed by extensive field-based information including weather monitoring, vegetation and stream surveys, soil classifications, etc. Incorporating these data in future work identifying stream channels with LiDAR will allow more detailed evaluations of the accuracy and precision of results, the effects of alternative image pre-processing techniques, differences between multiple LiDAR acquisitions, as well as help reduce subjectivity during parameterization (e.g., stream initiation flow accumulation thresholds).

16 INTEGRATION OF LiDAR DERIVED STREAM NETWORKS INTO NHD Introduction PNWHF partners are currently updating the NHD using a variety of data sources. The partners have implemented agreed upon stewardship processes that accommodate these various data sources. The partnership anticipates that the percentage of NHD updates derived from LiDAR elevation data will increase through time. This project evaluated methodologies for updating the NHD with LiDAR derived stream networks within the context of established stewardship agreements and procedures. Generating a stream network from the LiDAR DEM is only the first step in integrating these data in to the NHD. Processes that incorporate existing NHD edit tools and stewardship relationships needed to be developed to support these LiDAR based edits. Secondly, labor costs associated with these types of edits needed to be determined to assist PNWHF partner organizations in their planning of future workloads. NHD updates resulting from the two pilot areas have been used to develop cost estimates for labor associated with these updates. The project team incorporated the use of the NHD GeoEdit toolset for this evaluation. This toolset has been developed by the U.S. Geological Survey (USGS) to support NHD updates although its effectiveness on large edits with significant changes in stream geometry has been under question. The LiDAR-based delineations for Panther Creek and Ashland have these characteristics so the project team evaluated the use of the NHD GeoEdit toolset to support these NHD edits. The Ashland Creek HU is the pilot area used in the NHD integration discussion as it went through the entire update and review process required by the NHD and the PNWHF protocols.

17 Figure 6. Perennial (dark blue) and intermittent (light blue) stream channels derived using the D8 hydrology model within the Ashland Creek 12-digit hydrologic unit..

18 Evaluation and Issues The LiDAR hydrologic algorithms produce candidate stream lines on the basis of flow direction that is derived from the elevation and slope grids. They do not take into account important natural and anthropogenic features that can affect the flow of water. Performing an NHD update that is based on these LiDAR sources relies on a variety of ancillary data including current imagery, existing NHD, Digital Raster Graphics (DRG), etc. This information is vital in resolving ambiguous data issues resulting from use of the LiDAR delineations. New information not reflected on the DRG or USGS topographic map, such as dam spillways, road and ditch intersections can assist the editor. The most recent NAIP (National Agriculture Imagery Program) infrared imagery in Oregon is an excellent resource that is available to resolve problems. The issue of stream periodicity, e.g. perennial vs. intermittent, generated many discussions by the project team. Some members wanted to incorporate other data such as precipitation, soils, or stream gage data to help in this determination. In the end the USFS and BLM hydrologists were adamant that GIS editors should not determine the periodicity. Instead, the USFS hydrologists provided flow accumulation thresholds (thirty acres for perennial and fifteen acres for intermittent streams) in the Ashland Creek area for defining periodicity. On the other hand, the BLM hydrologist requested points along the stream to symbolize different flow accumulation thresholds to help determine inception points and periodicity during his review. In general, there is a need to preserve NHD reach codes 1 because the reach codes may have important information tied to them (e.g., information related to Clean Water Act, sections 303(d) and 305(b)). Even though the states of Washington and Oregon have not tied their Clean Water Act reporting requirements to the NHD in the past, the Environmental Protection Agency (EPA) has integrated these reports with the NHD reach codes in their national database as well as their public web sites. It should be noted that 1 See the EPA web site http://www.epa.gov/waters/about/geography.html for a good explanation of the reach code and how impaired waters (as defined by the Clean Water Act) are tied to the specific reach codes.

19 State of Washington Department of Ecology is in the process of migrating its Clean Water Act data to the NHD. It is critical to preserve these reach codes wherever possible, and this is the rationale behind the instructions given to the editors for preserving reach codes whenever a stream possessed a valid GNIS name or was depicted on the medium resolution (1:100,000 scale) NHD. The medium resolution NHD dataset is currently housed in a separate database from the high resolution NHD. The NHD Geoeditor utilizes a Task Assistant to control the edit workflow while revising the NHD. To preserve the existing GNIS names and reach codes, the editor chooses REPLACE NHD FLOWLINE under NHD FLOWLINE TOOLS. For those streams that do not have a valid GNIS name or do not appear in the medium resolution NHD, it is recommended to use the ADD NHD FLOWLINE IMPORT NHD FLOWLINE GEOMETRY. Figure 9 depicts the named streams in the original NHD, which represents 39% of the streams. Using LiDAR to derive waterbodies such as lakes or two dimensional rivers was outside the scope of this project. However, we did use the new NAIP imagery to update the shape of one reservoir in Ashland Creek. The existing NHD geometry was incorrect at Reeder Reservoir and artificial paths had to be moved. The recommendation to preserve the artificial paths in NHD waterbodies is important because aquatic habitat or water quality information may be tied to existing artificial paths and preserving these paths minimizes event migration costs.

20 Figure 7. Ashland Creek Watershed the light blue lines represent streams with GNIS names and make up approximately 39% of the total stream length in the original NHD dataset. These would be edited with REPLACE NHD FLOWLINE tool from the Task Assistant. One useful way of thinking about the differences in the stream data derived from LiDAR vs. the NHD is to compare the stream length divided by the area of the watershed. In the Ashland Creek watershed, for example, this ratio is 80.93 km/15,786 acres for the original NHD and 140.24 km/15,786 acres for the LiDAR-based stream network. 2 Other statistical information such as perennial and intermittent stream length from before and after the LiDAR Ashland Creek update is given in Table 3. 2 The reason for mixing units is the original stream length in the NHDFlowline attribute table is in kilometers and the Hydrologic Units are traditionally defined in acres.

21 Table 3. Stream length information from before and after the LiDAR update for Ashland Creek. Ashland Creek NHD Original LiDAR Percentage change Total Stream Length 80.93 140.24 +73 % (km) Acres 15786 15786 Perennial streams 72.34 100.50 + 39% (km) Intermittent streams 8.59 39.74 + 462 % (km) GNIS streams (km) 31.56 32.13 + 2% LiDAR-based Stewardship Process BLM and the Forest Service hydrologists at the districts and forest levels act as data stewards for NHD streams and lakes data. Consequently, they must play an integral part in any process that updates the surface waters on their agency s lands. The workflow proposed by this pilot project includes at least two reviews by these data stewards (Figure 8). In addition, the LiDAR update process for the NHD has to go through the existing stewardship review process of the PNWHFP partnership.

22 1. Spatial analyst delineates stream network from LiDAR, generates supporting data layers (e.g., hillshade and contours), and provides required metadata documentation 2. GIS editor reviews stream network, records comments and initial edits, and prepares data layers for NHD data steward. 3. NHD data steward reviews GIS editor s comments and initial edits, annotates areas of concern, and makes recommendations. 4. GIS editor modifies stream network based on NHD data steward s review. Interaction between GIS editor and NHD data steward repeated, as necessary. 5. After NHD data steward s approval, GIS editor finalizes metadata documents before preparing NHD update. 6. Following NHD stewardship procedures, any affected parties are notified of pending update, and LiDAR-based streams are submitted to the USGS. Figure 8. Flowchart illustrating the interactions between the spatial analysts, GIS editor, and NHD data steward leading to the submission of LiDAR-based streams to the NHD.

23 Labor Cost Estimates The cost estimate to update the NHD is presented in hours rather than dollars as each agency s personnel or contractor costs may vary. It is important to note that the NHD integration and update was performed by a highly skilled NHD GIS editor. The total editing hours required to update the NHD in Ashland Creek were 30 hours/15,786 acres. If we extrapolate this time to the average size of a 10 digit Hydrologic Unit (formerly 5 th Field HUC) in Oregon which is approximately 117,650 acres, then it would take the editor approximately 220 hours/117,650 acres to accomplish this work. However, some efficiencies of scale may be gained by working in an entire 10-digit HU. On the other hand, in an agricultural area that contains many lakes and rivers that require artificial paths, the editing time could increase. Therefore, it is recommended that a complete 10-digit HU be updated in Phase 2 of the project to obtain more realistic metrics of the time involved in updating the NHD with LiDAR-derived data. OVERALL CONCLUSIONS AND RECOMMENDATIONS Automatically deriving acceptable LiDAR-based stream networks using traditional spatial analyst tools proved to be difficult and time consuming. The project team made contact with several other agencies using these tools and these projects all reported similar problems. Many of these agencies abandoned an automated approach and performed head-up digitizing from a LiDAR-based shaded relief. These difficulties forced the project team to investigate a toolset based on the principles of terrain analysis, TauDEM. This toolset proved to be effective in overcoming most, but not all problems encountered with the out-of-the-box spatial analyst tools. Therefore, it is recommended that future efforts to delineate stream networks using LiDAR-based DEMs in areas further evaluate the TauDEM tool-set. Spatial analyst tools using the D8 flow direction model may generate an acceptable network, but requires interactive editing before a stream network meeting NHD requirements is generated.

24 Integrating the LiDAR stream network requires the use of ancillary data such as the NAIP imagery, DRGs, and the original NHD. Special emphasis is placed on preserving streams with existing GNIS names even though this could make the editing process more expensive. The time required to integrate these stream data into the NHD appears to be high enough to justify a cost-benefit analysis before embarking on an update of one or more 10-digit HUs. Stewardship review and communication with other PNWHF partners are critical components for a successful NHD update based upon LiDAR. Since only two small 12-digit HUs on the west side of the Cascades were included in the first phase of this pilot, the team recommends updating a complete 10-digit HU to better calibrate the costs and technical processes. In addition, the team recommends picking a HU with a greater percentage of private ownership so that an institutional process can be defined to review the new hydrographic data that is derived from LiDAR. Not all areas or jurisdictions have on-the-ground experts available to review updates like the Forest Service, BLM, and the Oregon Department of Forestry. Possible HUs should contain flatter, agricultural areas or a greater percentage of suburban development. Another consideration for future work is that the HU contain larger number of lakes and wide streams with artificial paths to not only better estimate the time involved editing, but to help clarify the cost of any event migration required from a LiDAR update of the NHD. And finally, this project should be placed into the context of national efforts that utilize LiDAR to update the NHD. The national NHD steering committee is interested in defining procedures and estimating costs to integrate LiDAR-derived stream networks into the NHD. It is imperative that the PNWHF communicate their results to this group as well as being able to learn from other initiatives that are taking place around the nation.

25 LITERATURE CITED ESRI. 2008. ArcGIS 9.3. Environmental Systems Research Institue, Inc. (ESRI), Redlands, CA, USA. Garbrecht, J. and Martz, L.W. 2000. Digital elevation model issues in water resources modeling. In: Maidment, D. and Djokic, D., eds. Hydrologic and Hydraulic Modeling Support with Geographic Information Systems. Redlands, California: Environmental Systems Research Institute, Inc.: 1-27. Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J. 2002. Lidar remote sensing for ecosystem studies. BioScience. 52: 19-30. Lewis, D. 2006. Seeing landslides with LiDAR. Cascadia. 4: 1,3,8. Osborn, K., List, J., Gesch, D., Crowe, J., Merrill, G., Constance, E., Mauck, J., Lund, C., Caruso, V., and Kosovich, J. 2001. National Digital Elevation Program (NDEP). In: Maune, D.F., ed. Digital Elevation Model Technologies and Applications: The DEM Users Manual. Bethesda, Maryland: American Society for Photogrammetry and Remote Sensing: 83-120. Saunders, W. 2000. Preparation of DEMs for use in environmental modeling analysis. In: Maidment, D. and Djokic, D., eds. Hydrologic and Hydraulic Modeling Support with Geographic Information Systems. Redlands, California: Environmental Systems Research Institute, Inc.: 30-51. Tarboton, D.G., Bras, R.L., and Rodriguez-Iturbe, I. 1991. On the extraction of channel networks from digital elevation data. Hydrological Processes. 5:81-100. Tarboton, D.G. 2009. Terrain Analysis Using Digital Elevation Models (TauDEM) 4.0. http://hydrology.neng.usu.edu/taudem/

26 SELECTED REFERENCES Passalacqua, P., Do Trung, T., Foufoula-Georgiou, E., Sapiro, G., and Dietrich, W.E. 2010. A geometric framework for channel network extraction from LiDAR: Nonlinear diffusion and geodesic paths. Journal of Geophysical Research 115: F01002, doi:10.1029/2009jf001254 Poppenga, S.K., Worstell, B.B., Stoker, J.M., and Greenlee, S.K. 2009. Comparison of surface flow features from LiDAR-derived digital elevation models with historical elevation and hydrography data for Minnehaha County, South Dakota: U.S. Geological Survey Scientific Investigations Report 2009 5065, 24 p. Tarboton, D.G. 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research 33: 309-319. Tveite, H. 1999. An accuracy assessment method for geographical line data sets based on buffering. International Journal of Geographical Information Science 13: 27-47. Wilson J.P. and Gallant J.C. 2000. Digital terrain analysis. In: Wilson J.P., Gallant J.C., eds. Terrain analysis: principles and applications. New York: John Wiley and Sons 1-27.

27 ACRONYMS BLM Bureau of Land Management DEM Digital Elevation Model GIS Geographic Information System HU Hydrologic Unit LiDAR - Light Detection and Ranging NAIP - National Agriculture Imagery Program NED National Elevation Dataset NHD National Hydrography Dataset ODF Oregon Department of Forestry OWRD Oregon Water Resources Department PNWHF Pacific Northwest Hydrography Framework TauDEM Terrain Analysis Using Digital Elevation Models USFS U.S. Forest Service USGS U.S. Geological Survey