EVALUATION OF HIGH-RESOLUTION DIGITAL ELEVATION MODELS FOR CREATING INUNDATION MAPS. Mark A. Wonkovich. A Thesis

Transcription

1 EVALUATION OF HIGH-RESOLUTION DIGITAL ELEVATION MODELS FOR CREATING INUNDATION MAPS Mark A. Wonkovich A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 2007 Committee: Enrique Gomezdelcampo, Advisor Jeffrey Snyder Robert K. Vincent

2 ii ABSTRACT Dr. Enrique Gomezdelcampo, Advisor The Village of Pemberville, Ohio experiences frequent flooding. Accurate flood maps are needed to reduce damage caused by these floods. Photogrammetry software was used to create a high-resolution DEM using two overlapping USGS NAPP aerial photographs each with resolutions of 1:40,000 and scanned at 1800 DPI. The resulting DEM had a resolution of 0.57 m (1.87 ft) and a minimum detectible elevation of 0.95 m (3.1 ft). HEC-GeoRAS was used to produce cross-sections of the North Branch of the Portage River and an inundation map of a 100- year flood from the high-resolution DEM. The inundation map was compared to a 100-year flood inundation map produced with ground surveyed cross-sections provided by TMACOG and the FIRM for Pemberville. The datasets produced flood profile elevations of 196 m (643 ft) along the North Branch of the Portage River in the Village of Pemberville, which is the same as on the FIRM. When compared areally, the high-resolution DEM data produced larger flood extents than both the surveyed data and the FIRM of the village due to the length and distance of the cross-sections. Different lengths and large distances between cross-sections can cause large changes in the width of the bounding polygon, not accurately representing the floodplain, and limiting the extent of the estimated inundation.

3 iii AKNOWLEDGEMENTS I would like to thank Dr. Vincent, Dr. Snyder and Dr. Gomezdelcampo for helping me out whenever I needed. Also, thank you to Kurt Erichsen of TMACOG for providing me with the Portage River HEC-RAS data which was an essential part of my thesis. Furthermore, thank you to Bill Butcher who worked hard to keep my computer running during my research. Finally, I would like to thank my friends and family for their love and encouragement which helped to keep me going throughout graduate school.

4 iv TABLE OF CONTENTS INTRODUCTION...1 MATERIALS AND METHODS.12 RESULTS. 20 DISCUSSION..30 CONCLUSIONS...42 FUTURE WORK.44 REFERENCES...46 APPENDIX A..50 APPENDIX B..52 APPENDIX C..56

5 v LIST OF FIGURES/TABLES Figure Page 1 High-resolution DEM High-resolution DEM of North Branch of Portage River TMACOG Inundation map High-resolution DEM Cross-section Inundation Map Superimposed TMACOG and High-resolution DEM Inundation Maps Zoomed in View of Water St. Bridge Superimposed Inundation Maps TMACOG Flood Extent High-resolution DEM Flood Extent FIRM of the Village of Pemberville, Ohio Cross-hatch Noise Cross-section Comparison Bounding Polygons Flooding within TMACOG Bounding Polygon Extending Bounding Polygon Table Page 1 Inundation areas... 22

6 1 INTRODUCTION To reduce damage caused by flooding, accurate flood hazard maps are needed to delineate the probable extent of future flooding events. Flood maps show what structures, such as homes, businesses, and utilities will be affected by flooding events because of their location on the 500-, 100-, or 10-year floodplain. With these maps communities are able to protect citizens by restricting development in flood-prone areas and estimating damages caused by floods. Currently, the Federal Emergency Management Agency (FEMA) produces flood hazard maps called flood insurance rate maps (FIRMs). Two methods are used to produce FIRMs. The first method consists of manually interpolating the flood height from contour maps for a small amount of cross sections that best represent the average setting of the area. The second method utilizes a computer and elevation points to generate a large number of cross sections in the study area. The elevation points are obtained from a digital elevation model (DEM). A DEM models the elevation of an area in equally spaced intervals, which are presented as a grid. Regardless of the method used, the FIRM map shows the flood extent of the 500- and 100-year floods and displays any structures that are affected by such a flood. FIRMs are in the process of being updated and produced in digital form (DFIRM) (ODNR, 2005). The digital format allows the data to be used in conjunction with Geographic Information System (GIS) software and could be easily accessible over the internet. These digital maps are created by scanning current FIRMs or gathering new data to create a DFIRM. Completed DFIRMs contain a base map representing the best available topography of the area, and vector data, showing transportation features (roads,

7 2 railroads, etc.), hydrologic features (rivers, lakes etc.), and hydraulic structures (levees, dams, etc.) (ODNR, 2005). Accurate, updated, and accessible FIRMs are needed for decision making (ODNR, 2005). FIRMs created without a high-resolution DEM are not very accurate. The resolution of the publicly available DEMs (30m (98.4ft)) that can be used in creating FIRMs is too low to accurately estimate flooding in areas that do not have a large amount of elevation change. These small changes in elevations are missed because the spacing between known elevation points is too large for interpolation to resolve them. High-Resolution DEMs One method for obtaining a high-resolution DEM is from computer analysis of a stereo pair of aerial photographs. These images share a common overlap region and in digital format can be processed with various soft-copy photogrammetry software packages such as PCI Geomatica or Automatic Topographic Mapper (ATOM), to produce a high-resolution DEM. The softcopy photogrammetry programs extract elevation data from pixels in the overlapping regions of a stereo pair, and then use the difference in parallax of corresponding pixels to produce a new image containing elevation data (Geospectra, 1989). Aerial photography is increasingly becoming available in digital format from various sources such as the USGS. The availability of downloadable digital images and personal computers makes the process of producing high-resolution DEMs relatively easy and affordable. ATOM, developed by Geospectra, uses a dense-net algorithm to extract elevation data for every pixel in the overlapping region of two aerial photographs (Vincent, 1997). This program will co-register the generated DEM with the right input image and orthorectify both of them. Orthorectifying an image removes offsetting of features due to

8 3 differences in parallax, the horizontal displacement of objects. The co-registered orthophoto and the DEM can be overlaid for further data analysis. The vertical accuracy (root-mean-square error or z(rms)) of the pixels in the produced DEM is based on the resolution and scale of original digitized aerial photographs. The z(rms) in elevations measured by ATOM is directly proportional to the pixel size of the two digitized image. This allows the user to achieve a specific z(rms) by using pixels of a specific size. PCI Geomatica is a geospatial software package containing tools for various remote sensing and GIS operations (PCI Geomatics, 2005). Through the use of the OrthoEngine tool, it is possible to produce a high-resolution DEM of the overlapping region of two aerial photos. This tool performs an operation similar to the one by ATOM, and can produce a DEM with the same resolution when extracting elevation data from every pixel in the image. OrthoEngine is based on an algorithm known as exterior orientation that uses the pixel and line positions of points in the overlapping regions of images to create a high-resolution DEM (PCI Geomatics, 2005). Similar to ATOM, elevation data are calculated from the differences in parallax between corresponding pixels, GPS ground control points, and image tie points. Geomatica allows users to control more parameters of the DEM creation process. For example, elevation extraction can be done for every pixel or for groups of pixels specified by the user. PCI Geomatica also allows the user to perform post-processing on the created DEM, such as correcting uncorrelated pixels, interpolation, clipping, etc. Other remote sensing techniques and sensors can be used to obtain highresolution DEMs. Although, soft-copy photogrammetry is the most cost effective because of the use of inexpensive aerial photographs.

9 4 SAR and LiDAR Synthetic Aperture Radar interferometry (SAR or InSAR) is a remote sensing sensor primarily used to produce DEMs (Mather, 2004). SAR systems detect the amount of backscatter given off by an object when hit by microwave energy. The DEM is created by determining the distance from the sensor to the object from the time it takes for the pulse of microwave energy to hit an object and return to the sensor. In order to produce these images, SAR systems take two readings using two antennas located in different places. Most commonly, the two antennas are located in different positions on a single aircraft or satellite and one antenna acts as a transmitter and receiver while the other acts only as a receiver (single-pass interferometry) (Chipmen et al., 2004). Systems that use only one antenna are called repeat-pass interferometry, as they have to make two or more passes over the same area in order to generate an image. To produce DEMs with the single pass interferometry the distance between the two antennas, known as the baseline length, must be kept short to take advantage of small changes in phase angles amplifying changes in elevation. With repeat-pass systems the distance is determined from the difference in positions of the flight paths. However, if the baseline is too long then the two images become decorrelated and the phase differences cannot be measured (Mather, 2004). SAR can produce DEMs with horizontal accuracies of 10 m (32.8 ft) and vertical accuracies of m (33-49 ft) (Mather, 2004). Light Detection and Ranging (LiDAR) is another method of obtaining highresolution DEMs. This systems uses pulses of laser light directed toward the ground and measures the time for the pulse to return to calculate the distance between the sensor and the object it reflected off (Chipman et al., 2004). LiDAR and SAR are both active system as they provide their own source of electromagnetic radiation and do not depend on

10 5 ambient radiation for operation. However, LiDAR operates in the visible and near infrared wavelengths, whereas radar operates in the microwave wavelengths of the electromagnetic spectrum. Since LiDAR operates in the visible to near infrared wavelengths its signals are susceptible to interference by environmental conditions such as clouds. However, being active sensors, both LIDAR and SAR can operate at night. LiDAR is a nadir-looking sensor, whereas SAR is a side looking sensor. A Nadirlooking sensor looks directly down from the top of an object (Mather, 2004) helping eliminate any errors associated with changes in parallax in the resulting image. The size of the LiDAR pulse determines the resolution of the resulting data. Small pulse sizes allow for a more detailed look at the objects being scanned. Normally, LiDAR data have vertical resolutions of 15 cm (0.49 ft) to 1 m (3.3 ft). However, accuracy depends on comparing LiDAR elevation data with a set of ground control points or by stereo superposition using photogrammetric softcopy software. To take aerial photographs, a LiDAR system is setup in an airplane with a global positioning system (GPS), so that as the LiDAR scans the terrain surface below the aircraft, the GPS records the aircraft position. Since the system is recording GPS locations, the data are georeferenced from the beginning (Chipman et al., 2004), saving the time and effort of georeferencing the images after obtaining the data. The system records everything on the surface, including trees and buildings. This creates a digital surface model (DSM). The DEM is obtained by removing surface objects from the DSM, leaving just the elevation points of the terrain surface. Advanced LiDAR systems can convert the DSM to a DEM automatically by recording two signals. The first return signal records the time between the release of the pulse and the reception when it is bounced back. The second signal, called the last return, records the time of the last

11 6 indication of backscatter of the pulse. If there is no difference between the two signals, the object does not transmit light and the time difference is proportional to the height of the object above the ground (Mather, 2004). Objects such as concrete do not transmit light, whereas a forest canopy does. Solid objects such as buildings and the ground are not penetrated by the energy emitted by LiDAR systems and only one signal is recorded for these objects (Mather, 2004). Since buildings would be recorded as if they are ground, these values would have to be removed and replaced with true surface values. The best available DEMs, not produced with LiDAR, have a spatial resolution of 10 m (33 ft) with a vertical resolution of ±0.5 m (1.6 ft) (Mather, 2004). LiDAR can provide a horizontal spacing of less than 10 m (33 ft) with a vertical resolution of ± m ( ft) (Mather, 2004). The high-resolution images of LiDAR makes it a valuable source for obtaining DEMs for hydraulic modeling and floodplain mapping. However, LiDAR is expensive. According to the Galileo Group, Inc. (Michael Frank, pers. comm.., June 6, 2006), a company that specializes in the collection and processing of hyperspectral images, LiDAR data for a km 2 (10 12 mi 2 ) area would cost $30,000 to $35,000. One USGS National Aerial Photography Program (NAPP) image covers an area about twice this size and only costs $24 per image plus a data transfer free ranging from $30 to $60. The NAPP images can be used with softcopy photogrammetry to create DEMs with comparable resolutions to LiDAR. The most common use of SAR is to produce DEMs for calculating land subsidence, movement of glaciers, and changes in land due to volcanic activity, but it has also been used for vegetation classification using differences in the intensity of SAR readings (Mather, 2004). Wang et al. (1995) and Sipple et al. (1994) showed how

12 7 backscatter, recorded by SAR systems, can be used to map inundation. Crevier and Pultz (1997) conducted a study in North Dakota in 1996 mapping the flooding of the Red River. Their images, taken at various incident angles, showed that flooded areas resulted in dark areas and non-flooded section produced multi-toned areas. SAR data are usually combined with optical or infrared data from other sources such as maps and different imaging sensors to produce images that show flood extent and geographic locators (Kite and Pietroniro, 2000). LiDAR has a wide range of uses including hydrologic mapping, glacier monitoring, and DEM generation (Mather, 2004). Penetration depth of water increases as wavelength decreases, thus first return recordings of LiDAR data come from the surface of a body of water and the second return from the bed. However, this penetration depends on how deep and clear the water is. Clear water can allow for penetration of up to 70 m (229.7 ft) (Mather, 2004). LiDAR is also effective for mapping glaciers (Favey et al., 2000). The reflectivity of snow is high in the 800 nm range, which is the range LiDAR operates in. The high reflectivity allows the creation of accurate DEMs of a glacier, and if DEMs are generated at different times, they can be compared to see if the glacier is shrinking or growing (Mather, 2004). Roberts et al. (2007) used a LiDARproduced triangulated irregular network (TIN), along with the Army Corps of Engineers HEC-RAS hydraulic model, to predict the extent of flooding before and after removal of a dam. The TIN had a vertical resolution of 0.3 m (0.98 ft) allowing for a detailed examination of the topography in the area. The ability of LiDAR data to produce highresolution and highly accurate DEMs makes LiDAR a very valuable tool for various fields of research, where detailed, accurate measurements are important.

13 8 Hydraulic Modeling and HEC-RAS In order to predict flood extent and inundation areas, hydraulic models are needed to replicate how water flows on river channels. Increases in the availability of computers and digital data have made the use of hydraulic models simple, affordable, and more common. The use of remote sensing technologies, such as aerial photography and satellite imagery, has made more data required by hydraulic models accessible to researchers. However, remote sensing does not provide direct measurement of most hydraulic processes. Although, remote sensing and related technologies, such as image processing and Geographical Information Systems (GIS), can provide vital data and products for many hydraulic models (Cruise and Miller, 2003). Hydraulic data, used in various models, are obtained after interpretation of imagery (Kite and Pietroniro, 2000). The use of remote sensing in hydraulic models has proved to be an important tool. Townsend and Walsh (1998) found that the use of radar and optical remote sensing combined with GIS modeling is an effective technique for calculating potential inundation areas, even in regions with subtle topographic relief. Furthermore, Townsend and Walsh (1998) found that DEMs developed from contour data can predict floodplain properties and that the use of additional data, such as flood elevation can enhance the analysis. Most DEM-based analyses of fluvial environments have been limited to areas with significant topographic relief, as low resolution DEMs neglect small changes in elevation in relatively flat areas (Townsend and Walsh, 1998). Since topography is an important factor in hydraulic models (Horritt and Bates, 2001a), high-resolution DEMs are needed to provide accurate elevation data. Horritt and Bates (2001b) studied the effects of spatial resolution on raster based flow models and found that increasing spatial

14 9 resolution allows the model to more accurately represent water storage near the channel and therefore more accurately calculate inundation areas. The U.S. Army Corps of Engineers first started developing automated hydrologic/hydraulic models in the 1960s. In 1968, the Corp of Engineers released a computerized hydrologic model called HEC-2. This model allowed various open channel hydraulic parameters to be easily calculated with one program. Changes in computer technology forced the Corps of Engineers to upgrade HEC-2. In 1991, the Corps of Engineers started development of a replacement called HEC-RAS, which has been publicly available since 1995, and has continually been improved, becoming one of the most widely used floodplain hydraulic models in the world. HEC-RAS uses steady, gradually varied flow computation procedures (Benn et al., 2003) to simulate steady and unsteady flow conditions, while most other models are designed to simulate one or the other. HEC-RAS calculates water surface elevations at points designated along a stream. This allows for users to create flood maps of rivers based on the amount of water in a stream and the shape of river channel. Due to the continuous upgrading of HEC-RAS, the model can account for many more hydraulic processes and parameters than its predecessor (Benn et al., 2003). For example, improvements include channel modification analysis, mixed-flow capabilities, bridge scour analysis, WSPRO bridge analysis procedures, ice jam hydraulics, hydraulic simulation of gated structures, modeling of changes in Manning s n in the vertical direction, and GIS integration (Benn et al. 2003). HEC-RAS can be integrated into GIS software through the use of a plug-in developed by the Army Corps of Engineers named Geo-RAS. The plug-in works with ESRI s ArcMap software and allows users to integrate HEC-RAS data with GIS data.

15 10 Four equations are normally used to calculate water surface profiles, continuity, energy, momentum, and Manning s equation (Benn et al., 2003). Using these four equations, HEC-RAS produces water surface profiles depicting the water surface elevations for a given amount of water in a channel. In order to use these equations, data collection is needed to estimate the different parameters. This includes the collection of cross-section channel geometry, discharge, Manning s n (Manning s roughness coefficient), expansion and contraction coefficients, boundary conditions, and flow regimes. Two techniques, which are generally applied on a regular basis, for estimating water surface profiles are the direct step method and the standard step method. However, most computerize models, such as HEC-RAS, use the standard step method because it is easily applied to many different stream conditions. The direct step method solves for distance given a known depth or change in depth, whereas the standard step method finds depths at a specific location. The standard step method uses the continuity, energy, and Manning's equations to solve for depth of the water at selected locations along a stream (Benn et al., 2003). The momentum equation is used in place of the energy equation when it is not adequate, such as when flow changes from subcritical to supercritical or when analyzing a hydraulic jump (Benn et al., 2003). The discharge, cross-section geometry, roughness values, and expansion and contraction coefficients must be known at each location in order to calculate the water depth. Furthermore, the standard step method uses the concept of conveyance to account for streams with a left and right floodplain. The conveyance modifies how other parameters are calculated in order to account for the difference in stream conditions. Due to the nonlinearity of the equations,

16 11 this process can become very repetitive making computerized programs, like HEC-RAS, very practical for obtaining the water surface profiles. Objective The objective of this study is to determine if high-resolution DEMs produced with softcopy photogrammetry can be used to create more accurate inundation maps than the current FIRMs. To accomplish this objective, a high-resolution DEM was created with PCI Geomatics for the area around the Village of Pemberville, Ohio. Two inundation maps of a 100 year flood event at the North Branch of the Portage River were created using the hydraulic software program HEC-RAS and HEC-GeoRAS. The flood profiles and extents of the two analyses were compared. The first map utilized the highresolution DEM in conjunction with existing hydraulic data and HEC-RAS. The second map used HEC-GeoRAS to create cross-sections from the elevation data of the highresolution DEM.

17 12 MATERIALS AND METHODS Study Area All elevation values discussed in this study are above mean sea level (amsl). This study utilizes high-resolution digital elevation models (DEMs) for calculating inundation areas in the Village of Pemberville, Ohio. Pemberville, founded in 1876, is located in Wood County at the junction of the Northern and Middle branches of the Portage River. The average elevation of the area is 198 m (650 ft). The village s population is approximately 1,200. The town s major contributor to the economy is farming (Rakas, 2005). The North Branch, running from the west to the northeast, causes the village to experience frequent flooding. Flooding has become an annual occurrence, with the last major flood taking place in January The current FIRM available for the village was completed in 1982, most likely using the Army Corps of Engineering HEC-2 standard step-backwater computer program to perform the hydraulic analysis (FEMA, 1982). However, since the creation of the FIRM map the hydraulics of the area may have changed if any new bridges and construction was done near the river. Federal regulations are being followed in order to correct existing FIRMs. The Ohio Department of Natural Resources (ODNR) has implemented a plan to update FIRMs throughout Ohio (ODNR, 2005). Wood County has not yet begun updating existing FIRMs (ODNR, 2005). Data Aerial Photographs Three overlapping images from the USGS National Aerial Photography Program (NAPP) were used in this study. These aerial images were captured on film on October 12, 2000, and each one covers an area of 8.8 by 8.8 km (5.5 by 5.5 miles). The aerial

18 13 photographs were digitally scanned by the USGS at a resolution of 1,800 dots per inch (DPI) and have a scale of 1:40,000. Having the images scanned by the USGS lessens the chance of distortion to the image created by a typical flatbed scanner. Hydraulic Data The Toledo Metropolitan Area Council of Governments (TMACOG) conducted a study in 2002 on the entire Portage River Watershed (TMACOG, 1997). This study included an examination of flooding and drainage along the major streams within the Portage River Watershed in five counties using satellite imagery and hydraulic modeling. The HEC-HMS rainfall-runoff model was used to determine peak flood discharge and flood profiles were calculated using the HEC-RAS model. The HEC-RAS model used data obtained from FEMA flood insurance studies (HEC-2 data). TMACOG provided all the data used in their study. These data include 34 cross-sections (measured and interpolated), with flow data of the North Branch of the Portage River near Pemberville. However, only 27 are within the extent of the DEM used in this study. Cross-sections that were not in the extent of the DEM were not used. Also, these data provided the results of an unsteady flow analysis of the reaches in the study area that estimated the water surface profiles for the two year, twenty-five year, and one hundred year floods. All branches of the Portage River were included in the data set, but only the North Branch data were utilized in this study. Ground Control Points and Tie Points High water marks (see Appendix A) along the North Branch of the Portage River, from the January 2005 flood in Pemberville were obtained from the Wood County

19 14 Engineers office. A Trimble Pathfinder Pro XRS GPS (global positioning system) was used to obtain the location and elevation of the high water marks. Only high water marks within the extent of the DEM were located with the GPS receiver. Points throughout the study area were used as ground control points needed for DEM creation. The location of these points were collected with a Trimble Pathfinder Pro XRS GPS and were differentially corrected using the Pathfinder Office (Trimble, 2003) software and the City of Bowling Green, Ohio base station on the same day the GPS points were taken. The photogrammetry software fits a plane through the elevation data of the ground control points to best represent the surface of the study area. Tie points are used to show the photogrammetry program how each pixel in an image correlate to the pixels in another image. A point is placed on a pixel in one aerial photograph and it is tied to its corresponding pixel on the second photograph by placing a point on easily recognizable structures, such as driveways and street corners. Nine tie points were used, spread out evenly in a grid, throughout the overlapping region of the aerial photographs. DEM Creation A high-resolution DEM was created with PCI-Geomatica version 10 (PCI Geomatics, 2005), using the Orthoengine extension. First, the images were converted into epipolar pairs, which are stereo pairs reprojected so that the left and right images have a common orientation and all parallax is horizontal, not vertical.. Next, ground control points and tie points were entered. Tie points show the program how each of the images overlap and the ground control points show elevation. When the DEM is created, the elevations of the ground control points are used to calculate elevation data for the

20 15 every pixel. Nine tie points and eight ground control points were used in the overlap area of the two images. PCI extracted elevation data from correlating pixels in the overlapping area of the two images. This process was done by sampling every pixel in the overlapping area, which resulted in a horizontal resolution of 0.57 m (1.87 ft) and a minimum detectible elevation of 0.95 m (3.1 ft). The resulting DEM was then geocoded resulting in a DEM with the same coordinate system as the ground control points, namely, NAD 1927 UTM Zone 17 North. This process was repeated to create a second DEM. The two DEMs were then put together in a mosaic to create one DEM of the whole area around the Village of Pemberville. The final DEM for this study was produced by piecing together two created DEMs to create one file. When the two images were joined, a line where the two met could be seen. This line is in the middle of the image (Figure 1). The line resulted from drastic changes in elevation from the top image to the bottom image. Elevation differences ranged from 2 to 3 m (7-10 ft) along the line. The study area is relatively flat and a large change in elevation over a small area, as seen when crossing that line, should not occur. Because of the big change in elevation going from the top image to the bottom image, it was concluded that the accuracy of the bottom DEM was not high enough to use for this study. However, the accuracy of the top image was adequate. The top image contained the entire North Branch of the Portage River and was used in this study. When creating a DEM, some areas may be miss-correlated or result in noncorrelated pixels. Miss-correlated pixels occur when pixels in one aerial photograph are not tied to their corresponding pixel on the other photograph. For example, if a pixel of a tree is not tied to the pixel of the same tree on the overlapping image, miss-correlation occurs. Miss-correlation happens when tie points are incorrectly placed. These pixels

21 16 are shown as elevation and are not given a special value in the DEM, which makes them hard to identify. Non-correlation is when elevation values can not be obtained from corresponding pixels. During the creation process, non-correlated pixels are given a value of -100 which can easily be used to interpolate new values from surrounding pixels. OrthoEngine provides a tool for editing these pixels. Editing of the uncorrelated pixels was done because these pixels are gaps in the DEM. They contain no elevation data and interpolation is needed to fill in the gaps with elevation values. These pixels were masked and new values were interpolated from surface values of the surrounding pixels. Areas around the river containing vegetation and buildings were also removed by masking those areas and interpolating new values from neighboring pixels. This was performed because buildings and vegetation are not surface values, and these objects needed to be to obtain surface values.

22 17 Figure 1. High-Resolution Digital Elevation Model of the area around the Village of Pemberville, Ohio. Areas of high elevation are white, whereas areas of low elevation are black. Hydraulic Analysis Two approaches were taken for the hydraulic analysis. The first approach utilized all the data provided by TMACOG of the North Branch Portage River. The second approach used the DEM created from the aerial photographs and only the depths of the river channel from the TMACOG data. Flow and bridge data were kept constant between both analyses. The cross-sections in the TMACOG dataset were not georeferenced. In order to be used with Geo-RAS and the high resolution DEM, coordinates were added. Some of the cross-sections were labeled with their location, location relative to other crosssections, or were unlabeled. To locate the cross-section accurately, the high-resolution

23 18 DEM, aerial photographs, and road maps were used. Therefore, cross-section locations are in the same coordinate system as the DEM and aerial photographs. Coordinates for cross-sections which were labeled and easily located on these maps were recorded first. Coordinates for these cross-sections were taken from the DEM or aerial photographs and entered into the geometry data in HEC-RAS. Some cross-sections had labels which referred to their locations relative to the labeled cross-sections and bridges. For example, a cross-section was been labeled 100 ft upstream from Water St. Bridge. The coordinates for these cross-sections were found by using the measuring tool in ArcGIS on the highresolution DEM or aerial photographs and measuring the given amount on the Portage River. The unlabeled cross-sections were given coordinates based on how far away they were from other cross-section in the original HEC-RAS geometry data. The original geometry data give each cross-section a station number based along its location on the river, which is the surveying standard. By examining this information the coordinates for the unlabeled cross-sections were estimated. Once all the cross-sections were geocoded, the data for the North Branch of the Portage River were exported to HEC-GeoRAS along with the flood profile elevations for the 100 year flood. HEC-GeoRAS determines the floodplain as the area within the maximum extent of the cross-sections and all calculations are carried out within a polygon connecting the ends of these cross-section. GeoRAS then calculates the inundation areas within the floodplain by subtracting the DEM elevations from the flood profile elevations. If the flood profile elevation was higher than the DEM pixel value, the area is flooded. GeoRAS creates a polygon layer showing the extent of the flood area.

24 19 Two inundation maps of the 100 year flood were created for this study. However, this study only utilized cross-sections on the North Branch of the Portage River near Pemberville.

25 20 RESULTS In this study a high-resolution DEM of the area around the Village of Pemberville was created from aerial photographs using a softcopy photogrammetry software (PCI Geomatica v. 10) as well as two inundation maps of the 100-year flood for the North Branch of the Portage River, one using data from TMACOG and the other from crosssections created with GeoRAS from the high-resolution DEM. DEM The DEM created for this study (Figure 1) resulted in elevation values ranging from 170 m (557.7 ft) to 240 m (787.4 ft) with a pixel size of 0.57 m (1.87 ft) and a minimum detectible elevation of 0.95 m (3.1 ft). The DEM is 5.29 by kilometers (3.29 by 8.15 miles). The North and Middle Branch of the Portage River, and the Portage River (downstream from the confluence of North and Middle branches) are clearly visible in the DEM. The high-resolution DEM of the North Branch was used for this study. It is 7.6 km long (4.7 mi) (Figure 2).

26 21 Figure 2. DEM of the North Branch of the Portage River. Areas of high elevation are white, whereas areas of low elevation are black. Inundation Maps Figure 3 presents the inundation map created using the data provided by TMACOG for a 100-year flood. This figure shows that the river flooded an area of 478,394 m 2 (5,149,390 ft 2 ) including the river channel. Most of the flooding occurred on the northwest side of the river. Figure 4 shows the extent of the flooding during the same 100 year flood as in Figure 3, but using the cross-sections created from the high resolution DEM to perform the hydraulic analysis. For this case, the area flooded covered 521,798 m 2 (5,616,587 ft 2 ). Figure 5 shows both inundation maps superimposed allowing for a graphical comparison of the flooded areas. The inundation map produced with cross-sections

27 22 obtained from the high-resolution DEM floods a larger area than the inundation map from the TMACOG data. The difference between the two inundation maps can be seen more clearly in a zoomed-in view on the Water St. Bridge in the Village of Pemberville (Figure 6). This figure shows that the high-resolution cross-section hydraulic analysis flooded a larger area by 43,404 m 2 (467,197 ft 2 ). However, while the extent of flooding calculated from the high-resolution DEM cross-sections has a greater area near the Water Street bridge, both maps only showed flooding occurring up to an elevation of 196 m (643.1 ft) (Figures 7 and 8). Since both computer runs, with TMACOG data and highresolution DEM cross-sections, calculated the same flood profile elevations for the 100- year flood, the cross-sections obtained from the high-resolution DEM are similar to the TMACOG cross-sections. This means that the high-resolution DEM is accurate enough to produce cross-sections of the Portage River to be used in a hydraulic model. Table 1. Area of inundation calculated with both TMACOG data and high-resolution DEM cross-sections. Areas of inundation around Water St. Bridge were estimated using the measurement tool in ArcMap. Study Total Area of flooding (m 2 ) TMACOG Data 478,394 19,000 Estimated Area of flooding around Water St. Bridge (m 2 ) High-resolution DEM 521,798 22,000 Data

28 Figure 3. Inundation map of the North Branch of the Portage River for a 100-year flood using the data provided by TMACOG. The yellow area represents the area flooded. 23

29 Figure 4. Inundation map of the North Branch of the Portage River for a 100-year flood using cross-sections created from the high-resolution DEM. The light blue area represents the area flooded. 24

30 Figure 5. Superimposed inundation maps of the North Branch of the Portage River for a 100-year flood. In yellow is the inundation area generated from the TMACOG data and in light blue is the inundation area from the high-resolution DEM cross-sections. 25

31 Figure 6. Zoomed in view of the Water Street Bridge in the Village of Pemberville. The yellow is the area flooded according to the TMACOG data and the blue is the area flooded according to the high resolution DEM cross-section. 26

32 Figure 7. Extent of the flood using TMACOGs data. Each color represents a unique elevation value. The flood does not reach above 196 m (643.1 ft). 27

33 28 Figure 8. Extent of the flood using the high resolution DEM cross-sections. As in figure 6, each color represents a unique elevation value. The extent of the flooding is greater, but like the TMACOG data, the flood does not reach above 196 m (643.1 ft). The inundation maps in the Village of Pemberville are similar to the 1982 FIRM map (Figure 9) when comparing flood elevations. The flood profile elevation given on the FIRM is m (646 ft) along the North Branch of the Portage River and 196 m (643 ft) in both inundation maps (figures 7 and 8). However, the extent of flooding was greater on the inundation maps (figures 7 and 8) than on the FIRM (figure 8). For example, flooding on the west side of the Water St. Bridge on the inundation maps (figures 6 and 7) extends approximately 61 m (200 ft) from the banks of the river, while flooding on the FIRM extends to approximately 46 m (150 ft).

34 Figure 9. FIRM from 1982 of the Village of Pemberville. Flood elevation along the North Branch of the Portage River is m (646 ft). 29

35 30 DISCUSSION DEM The accuracy of the DEMs was, in part, controlled by the number of GPS points used in the creation process (PCI Geomatics, 2005). As discussed in the Materials and Methods section, the final DEM (Figure 1) was produced by merging two DEMs created from the three aerial photographs. A line can be seen, in the middle of the image, where the two images were joined. One image is above the line and the other below the line. Eight total ground control points were used to create the DEMs. However, only three ground control points were used in the bottom DEM (below the join line), while five ground control points were used on the top image (above the join line). Also, both DEMs did not use the same plane through the ground control points, which caused the two DEMs to be drastically different in plane orientation. Therefore, collecting more control points with a GPS and fitting the same plane through all the points in both DEMs would improve the quality of the final DEM. Further editing of the DEM after creation with PCI Geomatics was needed to produce a DEM suitable for hydraulic analysis. Only areas around the North Branch of the Portage River were edited because these were the only areas being used for hydraulic analysis. Tree stands not near the North Branch and the other branches of the Portage River were not edited. Due to vegetation around the river, DEM values in those areas did not show values for the land surface. Also, because water is in the channel values for the river bed were not correct. This is a result of the inability of aerial photography cameras to see through water. Elevation values collected for these pixels reflect the elevation of the water surface and not of the actual river bed. In order to obtain accurate crosssections from the DEM, elevation values of the water surfaces need to be removed and

36 31 elevations of the river bed need to replace them. To correct this, a mask of the river channel was created in ArcMap. New elevation values for the river bed were then taken from the provided cross-sections to replace the calculated values. Next, a mask was created for the vegetation around the river. True surface values were determined from the DEM pixel values in the area around the vegetation. Pixels under the mask that were above the true values were selected and resampled to 999. The DEM was then edited again with PCI-Geomatica s DEM editing tool to interpolate new values for the 999 values from the neighboring true surface values. Editing the DEM was necessary due to noise throughout the image. Noise results in sudden changes in elevation on the DEM that are not true representations of the surface elevations. The noise could be caused by poor correlation of pixels, that is, elevation values were not calculated by the photogrammetry software correctly during DEM creation due to differences in brightness. These differences can be produced by things such as shadows on the image. Also, objects on the ground such as buildings and vegetation can be considered noise. Objects on the ground cause photogrammetry software to calculate the elevation of the object instead of the surface. Buildings and vegetation have a higher elevation than the surface which results in a DEM showing these objects as a sudden rise in elevation. This is a problem because the objects are being shown as surface values when they are not. Editing was done using masks as discussed above. When replacing values in a DEM, values calculated during the DEM creation process are lost. Interpolated values, or new values which the user chooses, replace the DEM creation software s calculated values. Masks can cover large areas, but care must

37 32 be taken to keep correct calculated values and only replace incorrect values with the new data. In the high resolution DEM of the North Branch of the Portage River, along the banks of the river, noise was reduced by interpolating values from surrounding pixels. However, the noise was not completely eliminated. Cross-hatching patterns, as shown in Figure 8, were created during interpolation using the nearest neighbor interpolated method. New values were interpolated from neighboring pixels, but some of those neighboring pixels were higher than actual surface values and resulted in the interpolated values being incorrect. The incorrect values, before interpolation, most likely resulted from shadows and objects on the surface. The area around the Village of Pemberville is a relatively flat area and the high resolution DEM should not be showing many changes in elevation. As seen in Figure 10, interpolation of values around the channel of the North Branch and northwest of the channel, which is the village of Pemberville, resulted in gradually changing elevation in the interpolated areas with the cross-hatching noise. As discussed before, the cross-hatching noise appeared during the interpolation process because incorrect surface values in the neighboring cells were used (Figure 10). The area southeast of the river channels shows noise resulting from buildings and vegetation. Between these values, true surface values can be seen. In order to distinguish between noise and surface values, various sources such as topographic maps and other DEMs must be examined to determine the range of elevations in the area or by determining the average elevation on the DEM. If the values on the DEM match the other sources then those values are not noise. However, if the images used in creating the DEM are not correlated correctly, for example by using poorly placed tie points and inaccurate ground control points, the elevation values will not be correct.

38 33 Figure 10. Zoomed in view on the Village of Pemberville near the confluence of the North and Middle branches of the Portage River. Each color represents a single, unique elevation value (notice the cross-hatch noise in the northwest section of the figure). Problems with the high resolution DEM Values Due to the presence of objects on the surface, accurate surface elevations were not calculated for every pixel in the high resolution DEM. These objects must be removed in order to obtain true surface values. However, even when the objects were removed as described in the preceding section, noise still remained in the DEM. The entire aerial photograph of the North Branch of the Portage River shows the river surrounded by vegetation, and in some cases, buildings. In some sections, the river channel is completely obscured by vegetation. The river channel was digitized and values for the elevation of the river bed from the cross sections provided by TMACOG were added in

39 34 to the high resolution DEM. The river channel was digitized by identifying the banks on the high resolution DEM and aerial photographs, and other maps of the area. Since the channel was obscured and difficult to identify in some sections, the shape and length of the channel may not be a true representation of the whole extent of the channel. Once the river had elevation values for the river bed and not the water surface, vegetated and constructed areas surrounding the river reach were reclassified with interpolated values from the nearest neighbor. Figure 11 presents a comparison of the two types of cross-sections used in the study. Both of these cross-sections (11a and 11b) are in the same location. The left side of the river is similar in both figure 11a and b. Figure 11a, the high-resolution DEM cross-section, shows a gradual slope which flattens out around 200 m (656.2 ft) at the edge of the cross-section. This is the same as the TMACOG cross-section (Figure 9b). However, elevation values on the right side of the river are different, but within about 3 m (9.8 ft). This is due to the elevation values from the DEM being higher in that area than the elevations surveyed when the TMACOG cross-sections were created. These errors in the DEM values can be attributed to noise, surface objects, and interpolation errors discussed earlier. Even if the shape of the channel is different, the river bed elevations on the DEM were the same across the width of the channel, creating a rectangular channel (Figure 11a). In actuality, these elevations would be slightly higher or lower than each other creating a non-rectangular channel (Figure 11b). Since a mask was used on the DEM to replace the elevation values in the river bed, each pixel under the mask was the same. In order to have different values across the river channel, elevation data would need to be replaced for each individual pixel in the channel. Replacing values for each individual pixel would be very time consuming and hard to

40 35 accomplish with GIS software. However, cross-section data can be edited in HEC-RAS after being created in GeoRAS. This allows for the correction of cross-sections. If true surface values are known, overestimations of elevation data on the created cross-sections can be corrected using the available surveyed cross-section. Without that data incorrect elevation values would be harder to identify.

41 36 (a) (b) Figure 11. a) Cross-section created from the high resolution DEM. b) Cross-section provided by TMACOG. Note that the x axis scale in (b) does not match the one in (a). It is important to note why the elevation values of the left side of the North Branch of the Portage River in figure 11a are more comparable to the left bank of the river of figure 11b than the right bank of the river in both figures. Since the TMACOG

42 37 cross-sections were created from surveyed elevation data, they are very precise. Any differences from the TMACOG cross-sections seen in the high-resolution DEM crosssections would have to be from incorrect surface elevations in the DEM. Interpolation was used to reduce the errors caused by noise, but a larger area was interpolated on the left bank than on the right bank. This was done because it was determined that the left bank had a larger area of vegetation and buildings that needed to be taken out of the image. These values needed to be removed because vegetation and buildings cover part of the aerial photograph of the river, preventing the photogrammetry software from obtaining correct elevations. Also, this could be due to a higher amount of noise on the right bank side of the river. Despite these differences, flood profile elevations, using both cross-sections, for the 100 year flood were the same. This means that the shape of the high-resolution DEM cross-sections and the TMACOG cross-sections are similar enough to produce the same flood profile elevations for the 100-year flood in a hydraulic model. Inundation Maps The inundation map produced from the high resolution DEM cross-sections shows a larger area of flooding than the inundation map produced with the cross sections from TMACOG. All flow and bridge data were kept the same in both cases. However, the high resolution DEM cross-sections were longer than the cross-sections in TMACOG s data. The bounding polygon, where flooding areas are calculated, is controlled by the length of the cross-sections. GeoRAS does not calculate flooding outside the bounding polygon. Extending the cross-sections makes the polygon larger and allows for a bigger floodplain to be studied. Figure 12a and b show the extent of the bounding polygons. In some areas, for example, near the Water Street Bridge, the

43 38 TMACOG s data show flooding within the entire bounding polygon (Figure 13). However, when the cross-sections were extended into that area, increasing the size of the bounding polygon, GeoRAS extended the flooding in that area (Figure 14). In this section of the study area, the flooding extent was increased from 10 m (32.8 ft) to 60 m (196.9 ft). Furthermore, flooding in this section occurred mostly on the northwest side of the North Branch due to the amount of noise removed and new values interpolated in this area. Interpolation on the southeast side of the river was done, but to a smaller area. The Middle branch of the Portage River joins the North Branch at the Village of Pemberville, but for reasons discussed in the Material and Methods section, it was left out of this study.

44 39 (a) (b) Figure 12. a) Bounding polygon resulting from the cross-sections of the TMACOG data. b) Bounding polygon resulting from the high resolution DEM cross-sections. The lines in the middle represent the banks of the river channel.

45 Figure 13. Area around Water Street Bridge with TMACOG s data. The yellow area is the extent of the flooding within bounding polygon (light blue). Flooding cannot surpass the bounding polygon 40

46 41 Figure 14. Extent of flooding after the bounding polygon was extended by creating new, longer cross-sections. Due to the lack of intermediate cross-sections and the location of surveyed crosssections in the data provided by TMACOG, the bounding polygon created for this study does not follow the true shape of the channel. Only 27 cross-sections were used, while the high-resolution cross-section study utilized 34, plus six bridge cross-sections from the TMACOG data. This allowed the high resolution DEM cross-sections study to better follow the true shape of the river, and GeoRAS to calculate inundation in areas which were not covered by cross-sections in the TMACOG study.

47 42 CONCLUSION This study presents the qualitative comparison of two 100 year flood extents, one created with a high-resolution DEM, the other with data from TMACOG surveyed crosssections. Also, a sample cross-section was compared. The differences are seen graphically rather than quantitatively. Accurate, surveyed data are needed to make a quantitative comparison of the hydraulic data, but there is no record available of the areal extent of the 100-year flood in the Village of Pemberville. High water marks (Appendix A) are available for a storm of unknown return period, but only two of the 13 points are on the North Branch of the Portage River and only on bridges, preventing an areal extent comparison because bridges restrict flow. The most accurate data available are the TMACOG cross-section elevation data. All the results of the hydraulic analysis using the high-resolution DEM were compared to the TMACOG data. In order to quantitatively compare the flood profiles (elevation) with the high-water marks (Appendix A), more points along the North Branch of the Portage River are needed. This study demonstrates that hydraulic modeling using high-resolution DEMs, created with NAPP aerial photography, is possible because the elevation data can be used to produce cross-sections. Flood extents and elevations for a 100-year flood were similar in studies conducted with surveyed cross-sections (TMACOG data) and cross-sections created from the high resolution DEM. Flood extents calculated with the high-resolution DEM cross-sections flooded eight percent more area than the TMACOG data (43,404 m 2, 467,197 ft 2 ). To produce a larger floodplain, the cross-sections from the high resolution DEM were longer, creating a bigger bounding polygon in GeoRAS. The distance and length of cross-sections control the size of the bounding polygon because it is created by

48 43 connecting the edges of the cross-sections. Different lengths and large distances between cross-sections can cause large changes in the width of the bounding polygon, not accurately representing the floodplain, and limiting the amount of inundation calculated by GeoRAS. Using more cross-sections, closer together and of similar length, reduces large changes in the width of the bounding polygon, creating more accurate flood extents. Both datasets produced inundation maps comparable to the current FIRM for the Village of Pemberville. The datasets produced flood profile elevations of 196 m (643 ft) along the North Branch of the Portage River in the Village of Pemberville, which is the same as on the FIRM (figure 9). Conducting hydraulic analyses with data derived from high-resolution DEMs is faster and cheaper than surveying data in the field. However, data derived from highresolution DEMs must be compared to surveyed data in order to test its accuracy. This is because physically surveying elevation of a surface is more accurate than obtaining elevation date using photogrammetry software. In this study, all the data created from the high-resolution DEM were compared to the surveyed TMACOG data. Furthermore, without TMACOG s surveyed data, the created high-resolution DEM would not have been able to produce accurate cross-sections because the elevation data of the river channel in the surveyed data were used to replace values in the DEM.

49 44 FUTURE WORK Any future studies conducted with this data should collect more ground control points in the overlapping regions of the aerial photographs and use a method of removing all the noise from the entire image. More ground control points throughout the study area than the ones used in this study would allow a more accurate DEM to be produced. This can be accomplished by using a GPS. Having more ground control points would allow the photogrammetry software to better fit a plane through the points, increasing the accuracy of the DEM. In the case when two high-resolution DEMs are joined, programs can be used to fit the same plane in both DEMs eliminating the problems at the junction of the two high-resolution DEMs. Once a high resolution DEM is produced, GIS and remote sensing software can be used to apply filters and conduct image enhancement techniques to reduce the noise in the DEM and improve its quality. The majority of aerial photographs are taken in the late fall, but the ones for this study still have leaves that obstruct the river and land surface. Using aerial photographs taken when leaves are off trees and agricultural fields are cleared of crops would help to reduce noise and more accurately measure elevation values under vegetation. Elevation values calculated for these areas will then be values of the surface instead of vegetation, increasing the accuracy of the DEM. With a more accurate DEM, cross-sections created from the DEM would also be more accurate. This would then make hydraulic studies in the area more accurate as well. More cross-sections, closer together and similar in length, should be used to represent the floodplain and produce realistic flood extents.

50 45 The high water marks from the January 2005 flood, located with a Trimble GPS receiver (Appendix A), can be used to calibrate a hydraulic model, but only if the size of the flood event (i.e. 10-year, 100-year, etc.) is known. For example, if this flood event was determined to be a 100-year flood, then calculated flood profiles for the 100-year flood would have to be the same elevation as the high water marks to be accurate.

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52 47 Mather P.M. Computer Processing of Remotely-Sensed Images: An Introduction, 3 rd edition. John Willey and Sons, Chichester, West Sussex, England, Moran, M. S., Chen, J. M., McElroy, S., & Peters-Lidard, C. D., Estimating soil moisture at the watershed scale with satellite-based radar and land surface models, Canadian Journal of Remote Sensing, 30(5), , Morrison, R. B. and Cooley, M. E., Assessment of flood damage in Arizona by means of ERTS-1 imagery, Proceedings Symposium on Significant Results Obtained from the Earth Resources Satellite-1, Vol. 1, New Carrollton, Maryland, pp , Oberstadler, R., Calabresi, G., Honsch, H., & Huth, D., Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping; a case study in Germany. Hydrological Processes, 11(10), , Ohio Department of Natural Resources, Division of Water, Map Modernization Business Plan for the State of Ohio 2006 Update, Columbus, Ohio, PCI Geomatics, OrthoEngine User Guide Geomatica 10, PCI Geomatics Enterprises Inc., Ontario, Canada, Frank, Michael, Personal communication, June 6, Rakas, A. About Pemberville. 3 December, Roberts, S.J., Gottgens, J.F., Spongberg, A.L., Evans, J.E. and Levine, N.S., Assessing potential removal of low-head dams in urban settings: An example from the Ottawa River, NW Ohio, Environmental Management, v. 39, p Sipple, S.J., Hamilton, S.K., Melack, J.M., Choudhury, B.J., Determination of inundation area in the Amazon River floodplain using the SMMR 37 GHz polarization difference, Remote Sensing Environ., 48, 70-76, Smith, L. C., Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological Processes, 11, , Toledo Metropolitan Area Council of Governments, Portage River Hydrologic Study Prepared for the Portage River Basin Council, Toledo, Ohio, Townsend, P. A., Walsh, J.S., Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing, Geomorphology, 21, , Trimble, GPS Pathfinder Office: getting Started Guide, Trimble Navigation Limited Mapping & GIS Business Area, Westminster, CO, 2003.

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54 APPENDICES 49

55 APPENDIX A 50

56 51 List of high water marks for a large flood in January 2005 obtained from the Wood County, Ohio Engineers Office. The street locations are from the list obtained from the Engineer s Office. Water elevation and UTM location were obtained with a Trimble GPS. Only marks within the aerial photographs were located with a GPS. Number Street Location UTM Location Elevation of High Water Mark 1 SE corner west abutment Kramer Rd. N. Branch 2.0 below top 2 Linwood rd over N. Branch E. Point NE Wing 7 below top 3 Gypsy Lane SW corner East backwall 1.5 below top 4 Napoleon Road NE corner top step west abutment 9 10 below top 5 Cuckle Creek Rd. SW corner west backwall 3 3 below top 6 Bowling Green Rd. east top east backwall N end at bridge deck 3 3 below top 7 Chamberlain Rd. top south end NW wing N abutment 2 10 below 8 Silverwood Rd. top south end NW wing N abutment 2 6 below 9 Kohring Rd top south end NW wind N abutment 4 6 below N: E: m 10 Water St. in Pemberville top east corner North abutment 2.7 below N: E: m 11 Pemberville Rd. SE corner top step east end north abutment 3 4 below 12 Bradner Rd. west corner north abutment 1 7 below top 13 South River Rd. just east of Wayne N: E: N: E: N: E: m 195.9m 198.6m (Water up to middle of guardrail)

57 APPENDIX B 52

58 USGS NAPP Aerial Photograph: 12373_126 53

59 USGS NAPP Aerial Photograph: 12373_127 54

60 USGS NAPP Aerial Photograph: 12373_128 55