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关于雷达方面的知识！ EFFECTIVENESS OF EXTRACTING WATER SURFACE SLOPES FROM LIDAR DATA WITHIN THE ACTIVE CHANNEL: SANDY RIVER, OREGON, USA by JOHN THOMAS ENGLISH A THESIS Presented to the Department of Geography and the Graduate School of the University of Oregon in partial fulfillment of the requirements for the degree of Master of Science March 2009 11 "Effectiveness of Extracting Water Surface Slopes from LiDAR Data within the Active Channel: Sandy River, Oregon, USA," a thesis prepared by John Thomas English in partial fulfillment of the requirements for the Master of Science degree in the Department of Geography. This thesis has been approved and accepted by: Date Committee in Charge: W. Andrew Marcus, Chair Patricia F. McDowell Accepted by: Dean of the Graduate School © 2009 John Thomas English 111 IV An Abstract of the Thesis of John Thomas English in the Department of Geography for the degree of to be taken Master of Science March 2009 Title: EFFECTIVENESS OF EXTRACTING WATER SURFACE SLOPES FROM LIDAR DATA WITHIN THE ACTIVE CHANNEL: SANDY RIVER, OREGON, USA Approved: _ W. Andrew Marcus This paper examines the capability ofLiDAR data to accurately map river water surface slopes in three reaches of the Sandy River, Oregon, USA. LiDAR data were compared with field measurements to evaluate accuracies and determine how water surface roughness and point density affect LiDAR measurements. Results show that LiDAR derived water surface slopes were accurate to within 0.0047,0.0025, and 0.0014 slope, with adjusted R2 values of 0.35, 0.47, and 0.76 for horizontal intervals of 5, 10, and 20m, respectively. Additionally, results show LiDAR provides greater data density where water surfaces are broken. This study provides conclusive evidence supporting use ofLiDAR to measure water surface slopes of channels with accuracies similar to field based approaches. CURRICULUM VITAE NAME OF AUTHOR: John Thomas English PLACE OF BIRTH: Eugene, Oregon DATE OF BIRTH: January 1st, 1980 GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: University of Oregon, Eugene, Oregon Southern Oregon University, Ashland, Oregon DEGREES AWARDED: Master of Science, Geography, March 2009, University of Oregon Bachelor of Science, Geography, 2001, Southern Oregon University AREAS OF SPECIAL INTEREST: Fluvial Geomorphology Remote Sensing PROFESSIONAL EXPERIENCE: LiDAR Database Coordinator, Oregon Department of Geology & Mineral Industries, June 2008 - present. LiDAR & Remote Sensing Specialist, Sky Research Inc., 2003 - 2008 GRANTS, AWARDS AND HONORS: Gamma Theta Upsilon Geographic Society Member, 2006 Gradutate Teaching Fellowship, Social Science Instructional Laboratory, 20062007 v VI ACKNOWLEDGMENTS I wish to express special thanks to Professors W.A. Marcus and Patricia McDowell for their assistance in the preparation of this manuscript. In addition, special thanks are due to Mr. Paul Blanton who assisted with field data collection for this project. I also thank the members ofmy family who have been encouraging and supportive during the entirety of my graduate schooling. I wish to thank my parents Thomas and Nancy English for always being proud of me. Special thanks to my son Finn for always making me smile. Lastly, special thanks to my wife Kathryn for her unwavering support, love, and encouragement. Dedicated to my mother Bonita Claire English (1950-2004). Vll V111 TABLE OF CONTENTS Chapter Page I. INTRODUCTION 1 II. BACKGROlTND 5 Water Surface Slope 5 LiDAR Measurements of Active Channel Features 7 III. STUDY AREA 10 IV. METHODS 22 Overview 22 LiDAR Data and Image Acquisition 23 Field Data Acquisition 24 LiDAR Processing 25 Calculation of Water Surface Slopes 27 Evaluating LiDAR Slope Accuracies and Controls 33 V. RESULTS 35 Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 35 Slope Comparisons 41 Surface Roughness Analysis 46 VI. DiSCUSSiON 51 VII. CONCLUSION 57 APPENDIX: ARCGIS VBA SCRIPT CODE 58 REFERENCES 106 IX LIST OF FIGURES Figure Page 1. Return Factor vs. LiDAR Scan Angle 2 2. Angle of Incidence 3 3. Wave Action Relationship to LiDAR Echo 3 4. Site Map 11 5. Annual Hydrograph of Sandy River 13 6. Oregon GAP Vegetation within Study Area 15 7. Photo of Himalayan Blackberry on Sandy River 16 8. Reach 1 Site Area Map with photo 18 9. Reach 2 Site Area Map 20 10. Reach 3 Site Area Map 21 11. LiDAR Point Filtering Processing Step 26 12. Field DEM Interpolated using Kriging 29 13. Reach 1 LiDAR Cross Sections and Sample Point Location 31 14. Differences Between LiDAR and Field Based Elevations 37 15. Regression ofLiDAR and Field Cross section Elevations 38 16. Comparison of LiDAR and Field Longitudinal Profiles (5, 10,20 meters) 40 17. Regression ofField and LiDAR Based Slopes (5, 10,20 meters) 42 18. Differences Between LiDAR and Field Based Slopes (5, 10,20 meters) 44 19. Relationship of Water Surfaces to LiDAR Point Density 47 20. Marmot Dam: Orthophotographyand Colorized Slope Model 50 21. LiDAR Point Density versus Interpolation 53 LIST OF TABLES T~k p~ 1. Reported Accuracies of 2006 and 2007 LiDAR 24 2. Results of LiDAR and Field Elevation Comparison 38 3. Results ofLiDAR and Field Slope Comparison (5, 10,20 meters) 45 4. Results of Reach 1 Slope Comparison 46 5. Water Surface Roughness Results for Reach 1,2, and 3 48 6. Results of Reach 1 Water Surface Roughness Comparison 49 7. Subset of Reach 3 Water Surface Roughness Analysis Near Marmot Dam 50 x 1 CHAPTER I INTRODUCTION LiDAR (Light Detection and Ranging) has become a common tool for mapping and documenting floodplain environments by supplying individual point elevations and accurate Digital Terrain Models (DTM) (Bowen & Waltermire, 2002; Gilvear et aI., 2004; Glenn et aI., 2005; Magid et aI., 2005; Thoma, 2005; Smith et aI., 2006; Gangodagamage et aI., 2007). Active channel characteristics that have been extracted using LiDAR include bank profiles, longitudinal profiles (Magid et aI., 2005; Cavalli et aI., 2007) and transverse profiles of gullies under forest canopies (James et aI., 2007). To date, however, no one has tested if LiDAR returns from water surfaces can be used to measure local water surface slopes within the active channel. Much of the reason that researchers have not attempted to measure water surface slopes with LiDAR is because most LiDAR pulses are absorbed or not returned from the water surface. However, where the angle of incidence is close to nadir (i.e. the LiDAR pulse is fired near perpendicular to water surface plane), light is reflected and provides elevations off the water surface (Figure 1, Maslov et aI., 2000). Where LiDAR pulses glance the water surface at angles of incidence greater than 53 degrees, a LiDAR pulse is 2 more often lost to refraction (Figure 2) (Jenkins, 1957). In broken water surface conditions the water surface plane is angled, which produces perpendicular angles of incidence allowing for greater chance of return (Maslov et al. 2000). Su et al. (2007) documented this concept by examining LiDAR returns off disturbed surfaces in a controlled lab setting (Figure 3). LiDAR returns off the water surface potentially provide accurate surface elevations that can be used to calculate surface slopes. 1.0 08 ~ 0.6 o t5 ~ E .2 ~ 04 02 00 000 __d=2° d=10 ° --d=200 --d=300 d=40o d=50o I I 2000 4000 60.00 sensing angle, degree I 8000 Figure 1. Return Factor vs. LiDAR Scan Angle. Figure shows relationship between water surface return and scan angle. Return Factor versus sensing angle at different levels of the waving d (d = scan angle). Figure shows the relationship of scan angle of LiDAR to return from a water surface. Return factor is greatest at low scan angles relative to the nadir region of scan. (Maslov, D. V. et. al. (2000). A Shore-based LiDAR for Coastal Seawater Monitoring. Proceedings ofEARSeL-SIGWorkshop, Figure 1, pg. 47). 3 reflected\\ :.;/ incident 1 I 1 . '\ I lAIR \ •••••••• ••••••••••••• •••••• ••••••••••••••••••••• • •• eo ••••••••••• o •••••••••••• _0 •••••••••• 0 ••• .•.•.•.•.•.•00 ,••••• ' 0•••• 0 ••••••••••• 0 ••I' .•.•.•.•.•.,................. .".0 ••••••••••••• , •••••••••••• , ••••••••••0••••. .....................................~ . ••••••••••••••••••••••••••••••••••••• • •••••••••••••••••••••••••• 0 •••••••••••••••••••• 0 ••••• 0 •• ~~~)}))}))})))))))))\..)}))?()))))))))))))))))j((~j< Figure 2. Angle of Incidence. Figure displays concept of reflection and refraction of light according to angle of incidence. The intensity of light is greater as the angle of incidence approaches nadir. (Jenkins, F.A., White, RE. "Fundamentals of Optics". McGraw-Hili, 1957, Chapter 25) 09 08 0.7 0.6 0.5 0.4 0.3 0.2 0.1 r - 0.\ O,j/6Y3- -500 17.5 35 52.5 70 horizonral scanning dislancC(lllm) 0.9 0.8 0.7 06 0.5 0.4 0.3 0.2 0.1 a b Figure 3. Wave Action Relationship to LiDAR Echo. "LiDAR measurements of wake profiles generated by propeller at 6000 rpm (a) and 8000 rpm (b). Su's work definitively showed LiDAR's ability to measure water surfaces, and the relationship of wave action to capability of echo. From Su (2007) figure 5, p.844 . This study examines whether LiDAR can accurately measure water surface elevations and slopes. In order to address this topic, I assess the vertical accuracy of LiDAR and the effects of water surface roughness on LiDAR within the active channel. Findings shed light on the utility of LiDAR for measuring water surface slopes in different stream environments and methodological constraints to using LiDAR for this purpose. 4 5 CHAPTER II BACKGROlJND Water Surface Slope Water surface slope is a significant component to many equations for modeling hydraulics, sediment transport, and fluvial geomorphic processes (Knighton, 1999, Sing & Zang, in press). Traditional methods for measuring water surface slope include both direct and indirect methods. Direct water surface slope measurements typically use a device such as a total station or theodolite in combination with a stadia rod or drop line to measure water surface elevations (Harrelson, et ai., 1994, Western et ai., 1997). Inaccuracies in measurements stem from surface turbulence that makes it difficult to precisely locate the water surface, especially in fast water where flows pile up against the measuring device (Halwas, 2002). Direct survey methods often require a field team to occupy several known points throughout a reach. This is a time consuming process, especially if one wanted to document water surface slope along large portions of a river. This method can be dangerous in deep or fast water. 6 Indirect methods of water surface slope measurement consist of acquiring approximate water surface elevations using strand lines, water marks, secondary data sources such as contours from topographic maps, or hydraulic modeling to back calculate the water depth (USACE, 1993; Western et aI., 1997). Variable quality of data and modeling errors can lead to inaccuracies using these methods. The use of strand lines and water marks may not necessarily represent the peak flows or the water surface. Contours may be calculated or interpolated from survey points taken outside the channel area. The most commonly used hydraulic models are based on reconstruction of I-dimensional flow within the channel and do not account for channel variability between cross section locations. LiDAR water surface returns have a great deal of promise for improving measurement of water surfaces in several significant ways. LiDAR measurements eliminate hazards associated with surveyors being in the water. LiDAR also captures an immense amount of elevation data over a very short period of time, with hundreds of thousands of pulses collected within a few seconds for a single swath. Within this mass of pulses, hundreds or thousands of measurements off the water's surface may be collected depending on the nature of surface roughness, with broken water surfaces increasing the likelihood of measurements (Figure 3). In addition, most terrestrial LiDAR surveys collect data by flying multiple overlapping flight lines, thus increasing the number of returns in off nadir overlapping areas and the potential for returns from water surfaces. 7 The accuracy of high quality LiDAR measurements is comparable to field techniques. The relative variability of quality LiDAR vertical measurements typically ranges between 0.03-0.05 meters (Leica, 2007), where relative variability is the total range of vertical error within an individual scan on surface of consistent elevation. Lastly, LiDAR has the ability to collect water surface elevations over large stretches of river within a single flight of a few hours. LiDAR Measurements of Active Channel Features Recent studies evaluating the utility of LiDAR in the active channel environment have documented the effectiveness of using LiDAR DTMs to extract bank profiles. Magid et al. (2005) examined long term changes of longitudinal profiles along the Colorado River in the Grand Canyon. The study used historical survey data from 1923 and differenced topographic elevations with LiDAR data flown in 2000. LiDAR with three meter spot spacing was used to estimate water surface profiles based on the LiDAR elevations nearest to the known channel. Cavalli et al. (2007) extracted longitudinal profiles of the exposed bed of the Rio Cordon, Italy using 0.5 meter LiDAR DEM cells. This study successfully attributed LiDAR DEM roughness within the channel to instream habitats. Bowen and Waltermire (2002) found that LiDAR elevations within the floodplain were less accurate than advertised by vendors and sensor manufacturers. Dense vegetation within the riparian area prevented LiDAR pulses from reaching the 8 ground surface resulting in accuracies ranging 1-2 meters. Accuracies within unvegetated areas and flat surfaces met vendor specifications (l5-20cm). James et al. (2007) used LiDAR at 3 meter spot spacing to map transverse profiles of gullies under forest canopies. Results from this study showed that gully morphologies were underestimated by LiDAR data, possibly due to low density point spacing and biased filtering of the bare earth model. Today, point densities of 4-8 points/m2 are common and would likely alleviate some of the troubles found in this study. Additional studies have used LiDAR to extract geomorphic data from channel areas. Schumann et al. (2008) compared a variety of remotely sensed elevation models for floodplain mapping. The study used 2 meter LiDAR DEMs as topographic base data for floodplain modeling, and found that modeled flood stages based on the LiDAR DEM were accurate to within 0.35m. Ruesser and Bierman (2007) used high resolution LiDAR data to calculate erosion fluxes between strath terraces based on elevation. Gangodagamage et al. (2007) used LiDAR to extract river corridor width series, which help to quantify processes involved in valley formation. This study used a fixed water surface elevation and did not attempt to demonstrate the accuracy of LiDAR derived water surfaces. Green LiDAR also has been used to examine riverine environments. Green LiDAR functions much like terrestrial LiDAR (which uses an infrared laser) except that green LiDAR systems use green light that has the ability to penetrate the water surface and measure the elevation of the channel bed. Green LiDAR is far less common than terrestrial LiDAR and the majority of studies have been centered on studies of ocean shorelines. Wang and Philpot (2007) assessed attenuation parameters for measuring bathymetry in near shore shallow water, concluding that quality bathymetric models can be achieved through a number of post-processing steps. Hilldale and Raft (2007) assessed the accuracy and precision of bathymetric LiDAR and concluded that although the resulting models were informative, bathymetric LiDAR was less precise than traditional survey methods. In general, it is often difficult to assess the accuracy of bathymetric LiDAR given issues related to access of the channel bed at time of flight. 9 10 CHAPTER III STUDY AREA The study area is the Sandy River, Oregon, which flows from the western slopes ofMount Hood northwest to the Columbia River (Figure 4). Recent LiDAR data and aerial photography capture the variety of water surface characteristics in the Sandy River, which range from shooting flow to wide pool-riffle formations. The recent removal of the large run-of-river Marmot Dam upstream of the analysis sites has also generated interest in the river's hydraulics and geomorphology. 11 545000 ,·......,c' 550000 556000 560000 Washington, I 565000 -. Portland Sandy River .Eugene Oregon 570000 ooo '~" ooo ~ ooo~ • Gresham (""IIIII/hill /flIt'r Oregon Clack. fna County Marmot Dam IHillshaded area represents 2006 LiDAR extent. Ol1hophotography was collected only along the Sandy River channel within the LiDAR extent. 10 KiiomElt:IS t---+---+-~I--+--+----t-+--+---+----jl 545000 550000 555000 560000 565000 570000 Figure 4. Site Map. Site area map showing location of analysis reaches within the 2006 and 2007 LiDAR coverage areas. Olihophotography was also collected for the 2006 study, but was collected only along the Sandy River channel. 12 Floodplain longitudinal slopes along the Sandy River average 0.02 and reach a maximum of 0.04. The Sandy River has closely spaced pool-riffles and rapids in the upper reaches, transitioning to longer sequenced pool-riffle morphology in the middle and lower reaches. The Sandy River bed is dominated by sand. Cobbles and small boulders are present mostly in areas of riffles and rapids. Much of the channel is incised with steep slopes along the channel boundaries. The flow regime is typical of Pacific Northwest streams, with peak flows in the winter months ofNovember through February and in late spring with snowmelt runoff (Figure 5). Low flows occur between late September and early October. The average peak annual flow at the Sandy River station below Bull Run River (USGS 14142500) is 106cms. Average annual low flow for the same gauge is 13.9cms. 13 USGS 14142500 SRNDY RIVER BL~ BULL RUN RIVER, NR BULL RUN, OR 200 k.===_~~~=~~~=.......==",,=~-........==~ ~....J Jan 01Feb Ollar 01Rpr O:t1ay 01Jun 01Jul 01Rug OJSep 010ct 01Nov O:IJec 01 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 \ 11 ~I\\ ,1\ 1\ j\ 1"J'fn I\. I, ) \ , ,;' ) I I" 'I'•., I I' I' ] 30000 ~~-~----~-------------~-------, o ~ 20000 ~ 8'-. 10000 ~ Ql Ql ~ U '001 ~ ::::J U, Ql to 1000 to .= u Co? '001 Cl )- .....J. a: Cl Hedian daily statistic <59 years) Daily nean discharge --- Estinated daily nean discharge Period of approved data Period of provisional data Figure 5, Annual Hydrograph of Sandy River. US Geological Survey gaging station annual hydrograph of Sandy River, Oregon at Bull Run River. Data from http://waterdata.usgs.gov/or/nwis/annual/ Vegetation is mostly a mixture of Douglas fir and western red hemlock (Figure 6). Other vegetation includes palustrine forest found in the upper portions of the study area, and agricultural lands found in the middle and lower portions. Douglas fir and western red hemlock make up 87% of vegetated areas, palustrine forest 5%, and agricultural lands 5%, the remaining 3% is open water associated with the channel and reservoirs (Oregon GAP Analysis Program, 2002). The city of Troutdale, OR abuts the lower reaches of the Sandy River. Along this stretch of river Himalayan blackberry, an invasive species, dominates the western banks (Figure 7). The presence of Himalayan blackberry is significant because LiDAR has trouble penetrating through the dense clusters of vines. When this blackberry is close to the water's edge it is difficult to accurately define the channel boundary. 14 15 545000 550000 555000 560000 565000 570000 Reach 3 10 !' 0° 200 MetersO 0 ~~~~~~I O~~~OOO~ Figure 9. Reach 2 Site Area Map. Site map of Reach 2. Reach 2 contains 359 cross sections derived from LiDAR and 3,456 sample points. Inset map shows cross section sample locations derived from LiDAR and smooth/rough water surface delineations used in analysis. 21 Reach 3 is located 40.7km upstream from the mouth of the Sandy and is 2,815 meters in length (Figure 10). The widest portion of this section at approximate banle full is 88 meters. The upstream extent of the channel includes the supercritical flow of Marmot Dam. The channel is incised and relatively straight with a sinuosity of 1.08. Fine sands dominate the channel bed with some boulders likely present from mass wasting along valley walls. As with Reach 2, Douglas fir dominates bank vegetation along. 200 40) Inset mAp displays UDAR point I densily alol1g willl cross seellon Sanlpleing dala LiDAR cross section SAmple locations were used to eX1mcl poinl density values. 503 fOC I 000 '.1..Hrs 1-.,...--,.-+--=1..,=-,---4I--+-1---11 . Reach 3 Figure 10. Reach 3 Site Area Map. Site map of Reach 3. Inset map shows point LiDAR water surface points. Reach 3 contains 550 cross sections and 3,348 sample points. Visual examination of this map allows one to see how point density varies within the active channel. 22 CHAPTER IV METHODS Overview LiDAR data and orthophotography were collected in 2006 and additional LiDAR data were collected over the same area in 2007. Field measurements were obtained five days after the 2007 LiDAR flight in order to compare field measurements of water surface slope to LiDAR-based measurements. Time of flight field measurements of water surface elevations were not obtained for the 2006 flight, but the coincident collection of LiDAR data and orthophotos provide a basis for evaluating variability of LiDAR-based slopes over different channel types as identified from aerial photos. Following sections provide more detail regarding these methods. 23 LiDAR Data and Image Acquisition All LiDAR data were collected using a Leica ALS50 Phase II LiDAR system mounted on a Cessna Caravan C208 (see Table 1 for LiDAR acquisition specifications). The 2006 LiDAR data were collected October 2211d and encompassed 13,780 hectares of high resolution (2':4 points/m2 ) LiDAR data from the mouth of the Sandy River to Marmot Dam. Fifteen centimeter ground resolution orthophotography was collected September 26th , 2006 along the riparian corridor of the Sandy River from its mouth to just above the former site ofMarmot dam (Figure 4). The 2007 LiDAR were collected on October 8th and covered the same extent as the 2006 flight, but did not include orthophotography. Data included filtered XYZ ASCII point data, LiDAR DEMs as ESRI formatted grids at 0.5 meter cell size. Data were collected at 2':8 points per m2 providing a data set with significantly higher point density than the 2006 LiDAR data. The 2006 LiDAR data were collected in one continuous flight. 2006 orthophotography was collected using an RC30 camera system. Data were delivered in RGB geoTIFF format. LiDAR data were calibrated by the contractor to correct for IMU position errors (pitch, roll, heading, and mirror scale). Quality control points were collected along roads and other permanent flat features for absolute vertical correction of data. Horizontal accuracy ofLiDAR data is governed by flying height above ground with horizontal accuracy being equal to 1I3300th of flight altitude (meters) (Leica, 2007). 24 Table 1. Reported Accuracies of 2006 and 2007 LiDAR. Reported Accuracies and conditions for 2006 and 2007 LiDAR data. (Watershed Sciences PGE LiDAR Delivery Report, 2006, Watershed Sciences DOGAMI LiDAR Delivery Report, 2007). Relative Accuracy is a measure of flight line offsets resulting from sensor calibration. 2006 LiDAR 2007 LiDAR Flying height above ground level meters (AGL) 1100 1000 Absolute Vertical Accuracy in meters 0.063 0.034 Relative Accuracy in meters (calibration) 0.058 0.054 Horizontal Accuracy (l/3300th * AGL) meters 0.37 0.33 Discharge @ time of flight (cms) 13.05 20.8 - 21.8 LiDAR data collection over the Reach 1 field survey location was obtained in a single flight on October 8, 2007 between 1:30 and 6:00 pm. During the LiDAR flight, ground quality control data were collected along roads and other permanent flat surfaces within the collection area. These data were used to adjust for absolute vertical accuracy. Field Data Acquisition A river survey crew was dispatched at the soonest possible date (October 13, 2007) after the 2007 flight to collect ground truth data within the Reach 1. The initial aim was to survey water surface elevations at cross sections of the channel, but the survey was limited to near shore measurements due to high velocity conditions. We collected 187 measurements of bed elevation and depth one to fifteen meters from banks along both sides of the channel (Figure 8a) using standard total station longitudinal profile 25 survey methods (Harrelson, 1994). Seventy-six and 98 measurements were collected along the east and west banks, respectively, at intervals of approximately 1 to 2 meters. Thirteen additional measurements were collected along the east bank at approximately ten meter intervals. Depth measurements were added to bed elevations to derive water surface elevations. Discharge during the survey ranged between 22.5 and 22.7 cms during the survey of the east bank and remained steady at 22.5 cms during the survey of the west bank (USGS station 14142500). LiDAR Processing The goal ofLiDAR processing for this project was to classify LiDAR point data within the active channel as water and output this subset data for further analysis. The LiDAR imagery was first clipped to the active channel using a boundary digitized from the 2006 high resolution orthophotography. LiDAR point data were then reclassified to remove bars, banks, and overhanging vegetation (Figure 11). 26 Figure 11. LiDAR Point Filtering Processing Step. LiDAR processing steps. Top image shows entire LiDAR point cloud clipped to active channel boundary. Lower image shows the final processed LiDAR points representing only those points that reflect off the water surface. All bars and overhanging vegetation have been removed as well. 27 Water points were classified using the ground classification algorithm in Terrascan© (Soininen, 2005) to separate water surface returns from those off of vegetation or other surfaces elevated above the ground. The classification routine uses a proprietary mathematical model to accomplish this task. Once the ground classification was finished, classified points were visually inspected to add or remove false positives and remove in-channel features such as bar islands. A total of 11,593 of 1,854,219 LiDAR points were classified as water. Points classified as water were output as comma delimited x,y,z ASCII text files (XYZ), then converted to a 0.5 meter linearly interpolated ESRI formatted grid using ESRI geoprocessing model script. Calculation of Water Surface Slopes Water surface slopes were calculated using the rise over run dimensionless slope equation where the rise is the vertical difference between upstream and downstream water surface elevations and run is the longitudinal distance between elevation locations. LiDAR data is typically used in grid format. For this reason grid data were used for calculation of water surface slopes. We used linear interpolation to grid the LiDAR point data as this is the standard method used by the LiDAR contractor. In order to compare the LiDAR and field data it was also necessary to interpolate field 28 measurements to create a water surface for the entire stream. The field data-based DEM was created using kriging interpolation within ArcGIS Desktop Spatial Analyst (Figure 12). No quantitative analysis was performed to evaluate the interpolation method of the field-based water surface. The kriging interpolation was chosen because it producex the smoothest water surface based on visual inspection when compared to linear and natural neighbor interpolations, which generated irregular fluctuations that were unrealistic for a water surface. The kriged surface provided a water surface elevation model for comparative analysis with LiDAR. 29 Figure 12. Field DEM Interpolated using Kriging. Field DEM interpolated from field survey points using kriging method found in ArcGIS Spatial Analyst. DEM has been hiIlshaded to show surface characteristics. The very small differences in water surface elevations generate only slight variations in the hillshadeing. To compare LiDAR and field-based water surface slopes, water surface elevations from the LiDAR and field-based DEMS were extracted at the same locations along Reach I. To accomplish this, 37 cross sections were manually constructed at approximately Sm spacings (Figure 13). Cross sections comparisons were used rather than point-to-point comparisons between streamside field and LiDAR data points because the cross sections provide water surface slopes that are more representative of the entire channel. The Sm interval spacing was considered to be a sufficient for fine resolution slope extraction. Because cross section center points were used to calculate the longitudinal distance and because the stream was sinuous, the projection of the cross sections from the center line to the banks led to stream side distances between cross sections that differed from Sm. 30 31 Smooth 125 Meters I 100 I 75 I 50 I 25 I Cross Sections Cross Section Data Roughness Delineation Cross Section Sample Locations _ Rough oI ~ each 1 Figure 13. Reach 1 LiDAR Cross Sections and Sample Point Locations. Reach I LiDAR-derived cross section sample locations and areas of smooth and rough water surface delineations. 37 cross section and 444 sample points lie within Reach 1. 32 Cross sections were extracted using a custom ArcObjects VBA script (Appendix A). This script extracted 1 cell nearest neighbor elevations along the transverse cross sections at 5 meter intervals creating 444 cross section sample locations (Figure 13). Cross section averages were calculated using field-based and LiDAR-based elevation water surface grids. The average cross sectional elevation value for field and LiDAR data were then exported to Excel files, merged with longitudinal distance between cross section, and used to calculate field survey-based and LiDAR-based slopes between cross sections. Reaches 2 and 3, for which only LiDAR data were available, were sampled using the same cross sectional approach used in Reach 1. The data extracted from these reaches were used to characterize how LiDAR-based elevations, slopes and point densities interact with varying water surface roughness. Within Reach 2, 359 cross sections were drawn and elevations were sampled every five meters along each cross section creating 3,456 cross section sample locations (Figure 9). Reach 3 contained 550 cross sections and 3,348 cross section sample locations (Figure 10). Slopes were calculated between each cross section. 33 Evaluating LiDAR Slope Accuracies and Controls The accuracy of elevation data is the major control on slope accuracy, so a comparative analysis was performed using field survey and LiDAR elevations. First, field-based and LiDAR slopes were calculated at distance intervals of five, ten and twenty meters using average cross section elevations to test the sensitivity of the slopes to vertical inaccuracies in the LiDAR data. The field and LiDAR elevations were differenced using the same points used to create average cross section elevations. Differences were plotted in the form of histogram and cumulative frequency plot after transforming them into absolute values. Descriptive statistics were calculated to examine the range, minimum, maximum, and mean offset between data sets. Finally LiDAR and field-based values were compared using regression analysis. This study also examined the effects of water surface roughness on LiDAR elevation measurements, LiDAR point density, and LiDAR derived water surface slopes. Each reach was divided into smooth and rough sections based on visual analysis of the orthophoto data. One-meter resolution slope rasters were created from the LiDAR water surface grids using ArcGIS Spatial Analyst. One meter resolution point density grids were created from LiDAR point data (ArcGIS Spatial Analyst). Using the cross section sample points, values for water surface type, elevation, slope, and point density were extracted within each reach. Point sample data were transferred to tabular format, and average values were generated for each cross section. These tables were used to calculate 34 descriptive statistics associated with water surfaces such as elevation variance, average slope variance, average point density, and average slope. It is assumed in this study that smooth water surfaces are associated with pools and thus ought to have relatively low slopes. Conversely rough water surfaces are assumed to be representative of riffles and rapids, and thus ought to have relatively steeper slopes. Reach 1 contains field data, so slopes from LiDAR and field data were compared with respect to water surface conditions as determined from the aerial photos. 35 CHAPTER V RESULTS Results of this study encompass three analyses. Elevation analysis describes the statistical difference between LiDAR and field-based water surface elevations for Reach 1. Slope analysis compares LiDAR derived and field-based slopes calculated at 5, 10, and 20m longitudinal distances. These analyses aim to quantify both slope accuracy and slope sensitivity. Lastly, water surface analysis examines the relationship between LiDAR measured water surface slopes, point density, and water surface roughness. Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 The difference between water surface elevations from LiDAR affects the numerator within the rise over run equation, which in tum affects slope. This elevation analysis evaluation quantifies differences between field and LiDAR data. LiDAR-based cross section elevations were differenced from field-based cross section elevations. Difference values were examined through statistical analysis. 36 In terms of absolute elevations relative to sea level, the majority of LiDAR-based water surface elevations were lower than field-based elevations, although the LiDAR elevations were higher in the upper portion ofReach 1. Differences ranged between -0.04 and 0.05m with a mean absolute difference between field and LiDAR elevations of 0.02m (Figure 14 and Table 2). The range of differences is within the expected relative accuracies of LiDAR claimed by the LiDAR provider. Elevations for field and LiDAR data are significantly correlated with an R2 of 0.94 (Figure 15). The negative offset was expected given that discharge at time of LiDAR acquisition was lower than discharge at time of field data acquisition. Discharge during field acquisition ranged between 22.5 and 22.7 cfs, while discharge during LiDAR acquisition was between 20.8 and 21.8cfs. The portion of Reach 1 where LiDAR water surface measurements were higher than field measurements may be related to difference in discharge or change in bed configuration. Overall results showed that LiDAR data and field-based water surface measurements are comparable. 37 Distribution of Elevation Differences Between Field and LiDAR Water Surfaces 10 9 8 7 >. 6 u r:: ell 5 :l C'" ~ 4 u.. 3 2 0+---+ -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 More Elevation Difference, Field - L1DAR (m) Figure 14. Differences Between LiDAR and Field Based Elevations. Elevation difference statistics between cross sections derived from field and LiDAR elevation data. Positive differences indicate that field-based elevations were higher than LiDAR; negative differences indicate LiDAR elevations were higher. Values on x axis represent minimum difference within range. For example, the 0.01 category includes values ranging from 0.01 to 0.0199. y-1.18x-1.03 .... R2 =0.94 ""..,; I •• ./... ./ .- ./ • ./ • ./. /""I ./iI ../. _._~. -? , 38 Table 2. Results of LiDAR and Field Elevation Comparison. Descriptive and regression statistics for absolute difference lField - LiDARI values between cross section elevations. All units in meters. Sample size is 37. Mean 0.028 Median 0.030 Standard Deviation 0.013 Kurtosis -0.640 Skewness -0.484 Range of difference 0.093 Minimum difference 0.002 Absolute maximum difference 0.047 Confidence Level(95.0%) (m) 0.004 Elevation Comparison of Field and LiDAR Water Surface Elevations 5.72 5.70 ~_ 5.68 g 5.66 :0:; I1l 5.64 > iii 5.62 ell 5.60 () ~ 5.58 ~ 5.56 ~ 5.54 1\1 5.52 ~ IX 5.50 ion (m) Figure 15. Regression of LiDAR and Field Cross Section Elevations. Regression of field-based (x) and LiDAR-based (y) cross section elevations. 39 Comparison of longitudinal profiles offield and LiDAR water surfaces shows a clear relationship in overall shape (Figure 16), capturing similar trends in longitudinal profiles. Figure 16 shows field and LiDAR profiles become more similar in shape as distance between cross sections increases. In terms of overall shape, the greatest differences occur in the upper 30 m, where LiDAR-based profiles demonstrate a higher slope than do field-based measurements. Because of the five day lag between LiDAR and field measurements in this mobile bed stream, it is impossible to know the degree to which this difference represents error in measurements or real change in the system. 40 5 meter Longitudinal Profile Comparison 20 40 60 80 100 120 140 160 180 5.75 .s 5.70 ~" _ • •• • :. 5 Cll 5.55 • • ~ • • w 5.50 • • • • • • • • • 5.45 5.40 0 20 40 60 80 100 120 140 160 180 Longitudinal Distance Down Stream (m) B 20 meter Longitudinal Profile Comparison 5.75 5.70 • ,. 20 Cll 5.55 •• Q) W 5.50 •• • , 5.45 . 5.40 0 20 40 60 80 100 120 140 160 180 Longitudinal Distance Down Stream (m) C Figure 16. Comparison of LiDAR and Field Longitudinal Profiles (5, 10, 20 meters). Longitudinal profiles of a) 5 meter, b) 10 meter, and c) 20 meter cross section elevations. 41 Slope Comparisons Slope in this study is calculated as the dimensionless ratio of rise over run. As noted in the Methods section, slopes were calculated over three different horizontal intervals to test the sensitivity of the LiDAR's internal relative accuracy. Differences in Sm LiDAR and field-based slopes derived from cross sections reveal substantial scatter (Figure l7a), although they clearly covary. Ten meter interval slopes show a stronger relationship (Figure 17b), while slopes based on cross sections spaced 20 m apart have the strongest relationship (Figure l7c). The slope associated with regression of field and LiDAR elevation data is not approximately 1 as one might expect. This is because LiDAR elevations are higher than field elevations at the upstream end of the reach, and lower at the downstream end. 42 5m Slope Comparison -c: ~ -0:: Q) (/l ~ ~.01 Q) C. .2 en 0:: « 0 ::i A -c: ~ 0:: --Q) (/l i2 -0.01 Q) C. 0 en 0:: « 0 ::i B 0.004 = 0.58x - 0.001 R2 = 0.38 ~.008 -0.008 Field Slope (Rise/Run) 10 meter Slope Comparison 0.004 y = 0.63x - 0.001 R2 = 0.51 -0.008 -0.008 Field Slope (Rise/Run) 20 meter Slope Comparison • 0.004 0.002 0.004 C :::l -0:: Q) (/l i2 ~.01 -Q) c. o Ci5 0:: « o~ 0.004 =0.66x - 0.001 R2 = 0.80 ~.008 ~.006 -0.008 Field Slope (Rise/Run) 0.002 0.004 C Figure 17. Regression of Field and LiDAR Based Slopes (5,10,20 meters). Scatter plots showing comparisons between slope values calculated at distance intervals of a) 5 meters, b) 10 meters, and c) 20 meters. 43 Figure 18 shows how the range of differences between LiDAR and field-based water surface slopes decrease as longitudinal distance increases. Five meter slope differences ranged between -0.004 and 0.004 (Figure 18a). Ten meter slope differences ranged between -0.002 and 0.003 (Figure 18b). Twenty meter slope differences ranged between 0 and 0.002 (Figure 18c). 44 Differences of Slope at 5m Between Field and LiDAR 10 » 8 0c Ql 6 :J 0" 4 .Q..l u. 2 0 SIll> SIll\- ~<::J <::J SIll>< ~/l, r;:,< ~ ~~I>< o"/l, .~. ~.~.~.~. ~ Slope Difference (Field-LiDAR) o +---+--+--+--t- SIll> <::J <::J ~ 3 c Ql :J 2 0" ~ U. C Figure 18. Differences Between LiDAR and Field Based Slopes (5, 10,20 meters). Histogram charts showing difference values between field and LiDAR derived slopes at a) 5 meter slope distances, b) 10 meter slope distances, and c) 20 meter slope distances. 45 The mean difference between slopes decreases from 0.0017 to 0.0007 as slope distance interval is increased. Maximum slope difference and standard deviation of offsets decrease from 0.001 to 0.0005 and 0.0047 to 0.0014 respectively. Regression analysis of these data show a significant relationship for all three comparisons, and adjusted R2 increased from 0.357 to 0.763 with slope distance interval (Table 3). Table 3. Results of LiDAR and Field Slope Comparison (5, 10,20 meters). Descriptive and regression statistics for offsets between field and LiDAR derived slope values (Field minus LiDAR). Slope values are dimensionless rise / run. All data is significant at 0.01. Distance Interval 5m 10m 20m Mean 0.0017 0.0012 0.0007 Standard Deviation 0.0010 0.0007 0.0005 Range of Difference 0.0080 0.0047 0.0024 Minimum difference 0.0000 0.0000 0.0001 Maximum difference 0.0047 0.0026 0.0015 Count 36 16 8 Adjusted R squared 0.36 0.47 0.76 Water surface slope for the entire length of Reach 1 (l59.32m) was compared and yielded a difference of 0.0005. This difference is smaller (by 0.0002) than the difference between 20 meter slope (Table 4). Slope was calculated by differencing the most upstream and downstream cross sections and dividing by total length of reach. Differences between LiDAR and field-based slopes may represent real change due to the five day lag between data sets and difference in discharge. 46 Table 4. Results of Reach 1 Slope Comparison. Comparison of slopes calculated using the farthest upstream and downstream cross section elevation values. Slope values have dimensionless units stemming from rise over run. Upper Lower Reach Elevation (m) Elevation (m) Len2th (m) Slope Field 5.652 5.491 159.32 -0.0010 LiDAR 5.697 5.455 159.32 -0.0015 Surface Roughness Analysis Water surface condition was characterized as smooth or rough based on 2006 aerial photography (Figure 19). Surface roughness was examined to understand its effect on LiDAR data within the active channel, as well as LiDAR's ability to potentially capture difference in water surface turbulence. Table 5 shows statistics with relation to water surface condition for all three reaches. 47 Figure 19. Relationship of Water Surfaces to LiDAR Point Density. 2006 aerial photos were used to delineate rough and smooth water surfaces. Image on left shows a transition between rough water surface (seen as white water) and smooth water surface (seen as upstream pool). Image on right shows LiDAR point density in points per square meter. In all reaches point density, variance of elevations, and water surface slopes were significantly higher in rough surface conditions. These results indicate that LiDAR point density is directly related to the roughness of a water surface and that is capturing the rough water characteristics one would expect in areas where turbulence generates surface waves. 48 Table 5. Water Surface Roughness Results for Reach 1,2, and 3. Water surface statistical output for rough and smooth water surface of Reaches 1, 2, and 3. Results within table represent average values for each Reach. Slope values have dimensionless units from rise over run equation derived from ESRI generated slope grid. Point density values based on points/m2 • Elevation variance in meters. Reach 1 Reach 2 Reach 3 Rou~h water No. of Sample Points 153 1981 1968 Avg Slope -0.013 -0.011 -0.007 Point Density (pts/mL ) 1.195 1.002 1.217 Elevation Variance (m) 0.003 0.018 0.041 Smooth water No. of Sample Points 290 1474 1378 Avg Slope 0.0075 -0.0006 -0.0033 Point Density (pts/mL ) 0.149 0.550 0.480 Elevation Variance (m) 0.001 0.0077 0.024 Within Reach 1, cross section elevations were separated into rough and smooth water conditions and slopes were calculated using field and LiDAR data sets (Table 6). Again, results showed that rough water surfaces have greater slopes than smooth water surfaces. The smooth water surface of Reach 1 yielded a larger discrepancy between field and LiDAR derived slopes compared to rough water surface. This is because small differences between LiDAR and field elevations generate larger proportional error in the rise / run equation when total elevation differences between upstream and downstream are small. 49 Table 6. Results of Reach 1 Water Surface Roughness Comparison. Reach 1 water surface roughness slope analysis. Reach 1 was divided into smooth and rough water surfaces based upon visual characteristics present in aerial photography. Slopes were calculated for each area and compared with field data to examine accuracy. Surface Reach Upper Lower Slope Type Lenl!th (m) Elevation (m) Elevation (m) Slope Difference Field Smooth 83.11 5.652 5.642 -0.0001 N/A LiDAR Smooth 83.11 5.697 5.612 -0.0010 0.0009 Field Rough 71.73 5.635 5.491 -0.0020 N/A LiDAR Rough 71.73 5.592 5.455 -0.0019 -0.0001 Prior to collections of the 2007 data, Reach 3 contained the former Marmot Dam that was dismantled on October 19th , 2007 (Figure 20). The areas at and directly below the dam are rough water surfaces. The super critical flow at the dam yielded a slope of - 0.896 (Table 7). The run below the dam contained low slope values of less than -0.002. Both the dam fall and adjacent run yielded high point densities of greater than 2 points per square meter. 50 Cross Sections o Cross Section Sample Locations L1DAR derived Slope Model Value Higll 178814133 25 50 75 100 125 150 ~.',eters I I I I I I La,·, 0003936 Figure 20. Marmot Dam: Orthophotography and Colorized Slope Model. Mannot Dam at far upstream portion of Reach 3. Image on left shows dam site in 2006 orthophotography. Image on right shows the increase in slope associated with the dam. Marmot Dam was removed Oct. 19th , 2007. Table 7. Subset of Reach 3 Water Surface Roughness Analysis Near Marmot Dam. Subset of Reach 3 immediately surrounding Marmot Dam roughness analysis containing values for Mannot Dam. The roughness results fell within expectations showing increases in slope at the dam fall and high point densities at the dam fall and immediate down stream run. Habitat Type Avg Slope Point Density Point Density Variance Dam Fall -0.896 2.284 1.003 Dam Run -0.001 2.085 5.320 51 CHAPTER VI DISCUSSION The elevation analysis portion of this study shows that LiDAR can provide water surface profiles and slopes that are comparable to field-based data. The differences between LiDAR and field based measurements can be attributed to three potential sources. The first is the relative accuracy of the LiDAR data which has been reported between O.05m and O.06m by the vendor. The second source can be associated with the accuracy of field based measurements which are similar to the relative accuracy of the LiDAR (O.03m-O.05m). Lastly, the discharge differed between field data collection and LiDAR collection by O.02cms. It is possible that much of the O.05m difference observed through most of the Reach 1 profile (Figure 16) could be attributed to the difference in discharge and changes in bed configuration, but without further evidence, the degree of difference due to error or real change cannot be identified. Even if one attributes all the difference to error in LiDAR measurements, the overall correspondence ofLiDAR and field measurement (Figure 15 and 16) indicates that LiDAR-based surveys are useful for many hydrologic applications. 52 In the upper portion of the reach, the profiles display LiDAR elevations that are higher than the field data elevations, whereas the reverse is true at the base of the reach. This could be a function of difference in discharge between datasets, change in bed configuration, or an artifact of low point density. Low density of points forces greater lengths of interpolation between LiDAR points leading to a coarse DEM (Figure 21). Overall, the analysis Reach 1 profile indicates that LiDAR was able to match the fieldbased elevation measurements within ±O.05m. 53 Rough & Smooth Wa~t:e:-r~S~u=rf;:a~c:e:s~rz~~J,;~~ Grid Interpolation in Low Point Density Figure 21. LiDAR Point Density versus Interpolation. Side by side image showing long lines of interpolation associated with smooth water surfaces (right image). Smooth water surfaces tend to have low LiDAR point density. The image on the right shows a hillshade ofthe LiDAR DEM. The DEM has been visualized using a 2 standard deviation stretch to highlight long lines of interpolation. The comparability of LiDAR and field-based slopes showed a significant trend with increasing downstream distances between cross sections. Adjusted R2 values increased from 0.36 to 0.76 and the range of difference between field and LiDAR based slopes decreased from 0.0047 to 0.00 14 as longitudinal distance increased from 5 to 20- 54 m. This suggests that the 0.05m of expected variation of LiDAR derived water surface elevation has less effect on water surface slope accuracy as distance between elevation measurements points increases. Likewise, slopes accuracies along rivers with low gradients will improve as the longitudinal distance between elevation points increases. Overall, data has shown that LiDAR can measure water surface slopes with mean difference relative to field measurements of 0.017, 0.012, and 0.007 at horizontal distances of 5, 10, and 20 meters respectively. Although the discrepancy between field and LiDAR-based slopes is greatest at 5-m intervals, the overall slopes (Fig 17) and longitudinal profiles (Fig 16) even at this distance generally correspond. The use of a 5m interval water surface slope as a basis for comparison is really a worst case example, as water surface slopes are usually measured over longer reach scale distances where the discrepancy between LiDAR and field-based measurements is lower. The continuous channel coverage and accuracies derived from LiDAR represent a new level of accuracy and precision in terms of spatial extent and resolution of water surface slope measurements. Analysis of surface roughness found that rough water surfaces had significantly higher point densities than smooth water surfaces. Rough water surfaces averaged at least 1 point/m2 , while smooth water surfaces averaged less than 1 point/2m2 • Longitudinal profiles of Reach 1 indicate the most accurate water surface measurements occur in areas of higher point density (Fig. 16). Future applications that attempt to use 55 LiDAR to measure water surface slope ought to sample DEM elevations from high point density areas of channel. Water surface analysis also showed trends relating water surface roughness and slope. Rough water surfaces for all three analysis reaches averaged larger average slope values than smooth water surfaces. This is because rough water surfaces are commonly associated with steps, riffles, and rapids. All three of these habitat types are areas have higher slopes than smooth water habitats. Smooth water surfaces are commonly associated with pools or glides, which would be areas of lower slope. Future research should examine the potential for using LiDAR to characterize stream habitats based on in-stream point density and slope. This study is not without its limitations. The field area used to test the accuracy of LiDAR is only representative of a small portion of the Sandy River. Comparisons of field and LiDAR data would be improved by having mid-channel field data. One might also question the use of field based water surface slopes as control for measuring "accuracy". Water surface slope is difficult to measure for reasons stated earlier in this paper. One might make the argument that there is no real way to truly measure LiDAR accuracy of water surface slope, and that LiDAR and field based measurements are simply comparable. In this context, LiDAR holds an advantage over field based measurements given its ability to measure large sections of river in a single day. LiDAR has a distinct advantage over traditional methods of measurement in that measurements are returned from the water surface, and consequently not subject to errors 56 associated with variability of surface turbulence piling up against the measuring device. LiDAR can also capture long stretches of channel within a few seconds reducing the influence of changes in discharge. LiDAR data in general does have its limitations. LiDAR data are only as accurate as the instrumentation and vendor capabilities. LiDAR must be corrected for calibrations and GPS drift to create a reliable data set, and not all LiDAR vendors produce the same level of quality. LiDAR data may be more accurate in some river reaches than others. The study reaches of this study contained well defined open channels, which made identifying LiDAR returns off the water surface possible. Both LiDAR data sets were collected at low flows. Flows that are too low or channels that are too narrow may limit ability to extract water surface elevations because of protruding boulders or dense vegetation that hinders accurate measurements. In some cases vegetation within and adjacent to the channel may interfere with LiDAR's ability to reach the water surface. Researchers should consider flow, channel morphology, and biota when obtaining water surface slopes from LiDAR. 57 CHAPTER VII CONCLUSION This paper examined the ability of LiDAR data to accurately measure water surface slopes. This study has shown that LiDAR data provides sufficiently accurate elevation measurements within the active channel to accurately measure water surface slopes. Measurement of water surface slope with LiDAR provides researchers a tool which is both more efficient and cost effective in comparison with traditional field-based survey methods. Additionally, analysis showed that LiDAR point density is significantly higher in rough surface conditions. Water surface elevations should be gathered from high point density areas as low point density may hinder elevation accuracy. Channel morphology, gradient, flow, and biota should be considered when extracting water surface slopes as these attributes influence water surface measurement. Further study should examine accuracy of LiDAR derived water surface slopes in channel morphologies other than those in this study. Overall, the recognition that LiDAR can accurately measure water surface slopes allows researchers an unprecedented ability to study hydraulic processes for large stretches of river. Common: APPENDIX ARCGIS VBA SCRIPT CODE 58 Public g---.pStrmLayer As ILayer ' stream centerline layer selected by user (for step 1) Public g_StrearnLength As Double ' stream centerline length (for step 1) Public g_InputDistance As Integer 'As Double 'distance entered by user (for step 1) Public g_NumSegments As Integer I number of sample points entered by user (for step 1) Public gyPointLayer As ILayer I point layer created from stream centerline (for step 1) Public g]ntShpF1Name As String I point layer pathname (for step 1) Public gyMouseCursor As IMouseCursor 'mouse cursor Public g_LinearConverson As Double I linear conversion factor Public gyDEMLayer As IRasterLayer I DEM layer (for steps 3 and 4) Public g_DEMConvertUnits As Double I DEM vertical units conversion factor (for steps 3 and 4) Public g_MaxSearchDistance As Double 'maximum search distance (for step 4) Public L NumDirections As Integer I number of directions to search in (for step 4) Public g_SampleDistance As Double 'sample distance (for step 5) Public g_SampleNumber As Double ' total sample points (for step 5) Public g_VegBeginPoint As Boolean I where to start the calucaltion (for step 5) Public g_VegCaclMethod As Boolean 'which method for Vegetation Calculation (for step 5) Public gyContribLayer As ILayer ' contributing point layer (for step 6) Public gyReceivLayer As ILayer 'receiving point layer (for step 6) Public gyOutputLayerName As String I output shapefile (for step 6) Function VerifyField(fLayer As ILayer, fldName As String) As Boolean I verify that topo fields are in the stream centerline point layer Dim pFields As IFields Dim pField As IField Dim pFeatLayer As IFeatureLayer Dim pFeatClass As IFeatureClass Set pFeatLayer = fLayer Set pFeatClass = pFeatLayer.FeatureClass Set pFields = pFeatClass.Fields For i = 0 To pFields.FieldCount - 1 Set pField = pFields.Field(i) 'MsgBox pField.Name IfpField.Name = fldName Then VerifyField = True Exit Function End If Next VerifyField = False End Function Function Ca1cPointLatLong(inPnt As IPoint, inLayer As ILayer) As IPoint , in point layer Dim pFLayer As IFeatureLayer Set pFLayer = inLayer , spatial reference environment Dim pInSpatialRef As ISpatialReference Dim pOutSpatialRef As ISpatialReference Dim pGeoTrans As IGeoTransformation Dim pInGeoDataset As IGeoDataset Set pInGeoDataset = pFLayer Dim pSpatRefFact As ISpatialReferenceFactory , get map units of shapefile spatial reference Dim pPCS As IProjectedCoordinateSystem Set pPCS = pInGeoDataset.SpatialReference 'set spatial reference environment Set pSpatRefFact = New SpatialReferenceEnvironment Set pInSpatialRef= pInGeoDataset.SpatialReference 'MsgBox pInSpatialRef.Name Set pOutSpatialRef= pSpatRefFact.CreateGeographicCoordinateSystem(esriSRGeoCS_WGS1984) Set pGeoTrans = pSpatRefFact.CreateGeoTransformation(esriSRGeoTransformation_NADI983_To_WGS1984_1) Dim pOutGeom As IGeometry2 Set Ca1cPointLatLong = New Point Set CalcPointLatLong.SpatialReference = pInSpatialRef Ca1cPointLatLong.PutCoords inPnt.X, inPnt.Y Set pOutGeom = Ca1cPointLatLong pOutGeom.ProjectEx pOutSpatialRef, esriTransformForward, pGeoTrans, 0, 0, ° 'MsgBox inPnt.X &" "& inPnt.Y & vbCrLf& Ca1cPointLatLong.X &" "& Ca1cPointLatLong.Y End Function Sub OpenGxDialogO Dim pGxdial As IGxDialog Set pGxdial = New GxDialog pGxdial.ButtonCaption = "OK" pGxdial.Title = "Create Stream Centerline Point Shapefile" pGxdial.RememberLocation = True Dim pShapeFileObj As IGxObject Dim pGxFilter As IGxObjectFilter Set pGxFilter = New GxFilterShapefiles 'e.g shp Set pGxdial.ObjectFilter = pGxFilter If pGxdial.DoModaISave(ThisDocument.Parent.hWnd) Then Dim pLocation As IGxFile Dim fn As String 59 Set pLocation = pGxdial.FinalLocation fn = pGxdial.Name End If If Not pLocation Is Nothing Then LPntShpFlName = pLocation.Path & "\" & fn frmlB.tbxShpFileName.Text = g]ntShpFlName frmlB.cmdOK.Enabled = True End If End Sub Function GetAngle(pPolyline As IPolyline, dAlong As Double) As Double Dim pi As Double pi = 4 * Atn(l) Dim dAngle As Double Dim pLine As ILine Set pLine = New Line pPolyline.QueryTangent esriNoExtension, dAlong, False, 1, pLine , convert from radians to degrees dAngle = (180 * pLine.Angle) / pi I adjust angles , ESRI defines 0 degrees as the positive X-axis, increasing counter-clockwise I Ecology references 0 degrees as North, increasing clockwise If dAngle 0 Then SplitWorkspaceName = Mid(sWholeName, 1, pos - 1) Else Exit Function End If Exit Function ERH: MsgBox "Workspace Split" & Err.Description End Function 'Returns a filename given for example C:\temp\dataset returns dataset Function SplitFileName(sWholeName As String) As String On Error GoTo ERH Dim pos As Integer Dim sT, sName As String pos = InStrRev(sWholeName, "\") Ifpos > 0 Then sT = Mid(sWholeName, 1, pos - 1) Ifpos = Len(sWholeName) Then Exit Function End If sName = Mid(sWholeName, pos + 1, Len(sWholeName) - Len(sT)) pos = InStr(sName, ".") If pos > 0 Then SplitFileName = Mid(sName, 1, pos - 1) Else SplitFileName = sName End If End If Exit Function ERH: 61 • MsgBox "Workspace Split:" & Err.Description End Function Public Sub BusyMouse(bolBusy As Boolean) 'Subroutine to change mouse cursor If g---'pMouseCursor Is Nothing Then Set g---'pMouseCursor = New MouseCursor End If IfbolBusy Then g---'pMouseCursor.SetCursor 2 Else g---'pMouseCursor.SetCursor 0 End If End Sub Function MakeColor(lRGB As Long) As IRgbColor Set MakeColor =New RgbColor MakeColor.RGB = lRGB End Function Function MakeDecoElement(pMarkerSym As IMarkerSymbol, _ dPos As Double)_ As ISimpleLineDecorationElement Set MakeDecoElement

Learn the skills you need to take advantage of Kali Linux for digital forensics investigations using this comprehensive guide Key Features Master powerful Kali Linux tools for digital investigation and analysis Perform evidence acquisition, preservation, and analysis using various tools within Kali Linux Implement the concept of cryptographic hashing and imaging using Kali Linux Perform memory forensics with Volatility and internet forensics with Xplico. Discover the capabilities of professional forensic tools such as Autopsy and DFF (Digital Forensic Framework) used by law enforcement and military personnel alike Book Description Kali Linux is a Linux-based distribution used mainly for penetration testing and digital forensics. It has a wide range of tools to help in forensics investigations and incident response mechanisms. You will start by understanding the fundamentals of digital forensics and setting up your Kali Linux environment to perform different investigation practices. The book will delve into the realm of operating systems and the various formats for file storage, including secret hiding places unseen by the end user or even the operating system. The book will also teach you to create forensic images of data and maintain integrity using hashing tools. Next, you will also master some advanced topics such as autopsies and acquiring investigation data from the network, operating system memory, and so on. The book introduces you to powerful tools that will take your forensic abilities and investigations to a professional level, catering for all aspects of full digital forensic investigations from hashing to reporting. By the end of this book, you will have had hands-on experience in implementing all the pillars of digital forensics—acquisition, extraction, analysis, and presentation using Kali Linux tools. What you will learn Get to grips with the fundamentals of digital forensics and explore best practices Understand the workings of file systems, storage, and data fundamentals Discover incident response procedures and best practices Use DC3DD and Guymager for acquisition and preservation techniques Recover deleted data with Foremost and Scalpel Find evidence of accessed programs and malicious programs using Volatility. Perform network and internet capture analysis with Xplico Carry out professional digital forensics investigations using the DFF and Autopsy automated forensic suites Who This Book Is For This book is targeted at forensics and digital investigators, security analysts, or any stakeholder interested in learning digital forensics using Kali Linux. Basic knowledge of Kali Linux will be an advantage. Table of Contents Introduction to Digital Forensics Installing Kali Linux Understanding File Systems and Storage Media Incident Response and Data Acquisition Evidence Acquisition and Preservation with DC3DD and Guymager File Recovery and Data Carving with Foremost and Scalpel Live and Memory Forensics with Volatility Autopsy – The Sleuth Kit Network and Internet Capture Analysis with Xplico Collecting, Preserving and Revealing Evidence using DFF

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