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models for perceived
rough
nes
s of three-dimensional
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textures
mathematical models for perceived
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s of three-dimensional
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A new method for fracture based on
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一种基于表面粗糙度的分析微成型过程断裂的方法,喻海良,,随着微机电系统(MEMS)技术和小型化的产品的发展,越来越多的研究人员开始关注微成形技术,以适应市场扩大的需要。本文提出了一�
雷达技术知识
关于雷达方面的知识! EFFECTIVE
NES
S 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 "Effective
nes
s 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: EFFECTIVE
NES
S OF EXTRACTING WATER
SURFACE
SLOPES FROM LIDAR DATA WITHIN THE ACTIVE CHANNEL: SANDY RIVER, OREGON, USA Approved: _ W. Andrew Marcus This paper exa
min
es 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 ev
alu
ate accuracies and deter
min
e how water
surface
rough
nes
s 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 v
alu
es 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
surface
s are broken. This
study
provides conclusive evidence supporting use ofLiDAR to measure water
surface
slopes of channels with accuracies si
mil
ar to field based approaches. CURRICUL
UM
VITAE NAME OF AUTHOR: John Thomas English PLACE OF BIRTH: Eugene, Oregon DATE OF BIRTH: January 1st, 1980 GRADUATE AND UNDERGRADUATE SCHOOLS ATT
END
ED: 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 &
Min
eral 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 fa
mil
y who have been encouraging and supportive during the entirety of my graduate schoo
lin
g. 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 s
mil
e. 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 Ev
alu
ating 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
Rough
nes
s Analysis 46 VI. DiSCUSSiON 51 VII. CONCLUSION 57 APP
END
IX: 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
Surface
s 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
Rough
nes
s Results for Reach 1,2, and 3 48 6. Results of Reach 1 Water
Surface
Rough
nes
s Comparison 49 7. Subset of Reach 3 Water
Surface
Rough
nes
s Analysis Near Marmot Dam 50 x 1 CHAPTER I INTRODUCTION LiDAR (Light Detection and Ranging) has become a common tool for mapping and doc
um
enting 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
surface
s 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 perp
end
icular 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 perp
end
icular angles of incidence allowing for greater chance of return (Maslov et al. 2000). Su et al. (2007) doc
um
ented this concept by exa
min
ing LiDAR returns off disturbed
surface
s 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
surface
s, and the relationship of wave action to capability of echo. From Su (2007) figure 5, p.844 . This
study
exa
min
es 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
rough
nes
s 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 mode
lin
g 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
lin
e 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 th
rough
out a reach. This is a time consu
min
g process, especially if one wanted to doc
um
ent 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
lin
es, water marks, secondary data sources such as contours from topographic maps, or hydraulic mode
lin
g to back calculate the water depth (USACE, 1993; Western et aI., 1997). Variable quality of data and mode
lin
g errors can lead to inaccuracies using these methods. The use of strand
lin
es 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
surface
s in several significant ways. LiDAR measurements eli
min
ate 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 dep
end
ing on the nature of
surface
rough
nes
s, with broken water
surface
s increasing the likelihood of measurements (Figure 3). In addition, most terrestrial LiDAR surveys collect data by flying multiple overlapping flight
lin
es, thus increasing the n
um
ber of returns in off nadir overlapping areas and the potential for returns from water
surface
s. 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 ev
alu
ating the utility of LiDAR in the active channel environment have doc
um
ented the effective
nes
s of using LiDAR DTMs to extract bank profiles. Magid et al. (2005) exa
min
ed 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
rough
nes
s within the channel to instream habitats. Bowen and Waltermire (2002) found that LiDAR elevations within the floodplain were less accurate than advertised by v
end
ors 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
surface
s met v
end
or 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. Sch
um
ann 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 mode
lin
g, 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
surface
s. Green LiDAR also has been used to exa
min
e 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 shore
lin
es. Wang and Philpot (2007) assessed attenuation parameters for measuring bathymetry in near shore shallow water, concluding that quality bathymetric models can be achieved th
rough
a n
um
ber 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 Col
um
bia 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 maxim
um
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 do
min
ated 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 th
rough
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 p
alu
strine 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, p
alu
strine 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, do
min
ates the western banks (Figure 7). The presence of Himalayan blackberry is significant because LiDAR has trouble penetrating th
rough
the dense clusters of vi
nes
. 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 !'
alustrine Forest Red Alder-Big Leaf Maple Forest Urban oo o o~ 545000 550000 555000 560000 565000 570000 Figure 6. Oregon GAP Vegetation within
Study
Area. 1999 Oregon GAP Analysis data for Sandy River area. Map shows how the Sandy River area is do
min
ated by Douglas fir forest with areas of p
alu
strine forest and agricultural lands (Oregon Natural Heritage Program, 1999). 16 Figure 7. Photo of Himalayan Blackberry on Sandy River. Himalayan blackberry near mouth of the Sandy River March, 25th 2007. Photo by John English. This
study
focuses on three reaches of channel that represent a range of water
surface
conditions along the river. Reach 1 is a I80-m long pool-riffle reach located 3.7 river kilometers upstream from the mouth, and is where we collected field data shortly after the 2007 LiDAR flight (Figure 8a). The bed is sandy in this reach and can change dramatically during high flows. The bank full width of Reach 1 is approximately 108 meters at its widest point. At the downstream
end
of the riffle, the channel is constricted 17 by riprap placed along the banks as the river flows under a bridge. Vegetation comprises deciduous and conifer trees such as Douglas fir, hemlock, and cottonwoods. Blackberry is present along the channel, but is not so dense that it obscures the active channel boundary. 18 b. Figure 8. Reach 1 Site Area Map with Photo. Reach 1 site area. Top figure (a) shows approximate width at bank full and length of field data collections. Yellow circles represent points along stream margins where water
surface
elevations were surveyed. Bottom photo (b) looks downstream from total station location. 19 Reach 2 (Figure 9) is located approximately 23.5 kIn upstream from the mouth of the Sandy River and is 1,815 meters in length. The widest portion of channel at approximate bank full is 116m. The channel consists of a large meander with sinuosity of 1.38 and consists of six riffles and five pools spaced at regular intervals. The substrate consists of sands with small boulders and large cobbles do
min
ating riffle areas. Cobbles and boulders have likely been introduced to the channel as a result of mass wasting. Douglas fir do
min
ates along banks. 20 oJ> 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
de
lin
eations 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 do
min
ate the channel bed with some boulders likely present from mass wasting along valley walls. As with Reach 2, Douglas fir do
min
ates 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 v
alu
es. 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 exa
min
ation 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 ev
alu
ating 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
lin
e 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
surface
s 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 soo
nes
t 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
surface
s 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
lin
early 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
lin
ear 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 ev
alu
ate 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
lin
ear 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
lin
e 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
Rough
nes
s De
lin
eation 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
de
lin
eations. 37 cross section and 444 sample points lie within Reach 1. 32 Cross sections were extracted using a custom ArcObjects VBA script (App
end
ix 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 v
alu
e 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
rough
nes
s. 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 Ev
alu
ating 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 c
um
ulative frequency plot after transfor
min
g them into absolute v
alu
es. Descriptive statistics were calculated to exa
min
e the range,
min
im
um
, maxim
um
, and mean offset between data sets. Finally LiDAR and field-based v
alu
es were compared using regression analysis. This
study
also exa
min
ed the effects of water
surface
rough
nes
s 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, v
alu
es for water
surface
type, elevation, slope, and point density were extracted within each reach. Point sample data were transferred to tabular format, and average v
alu
es were generated for each cross section. These tables were used to calculate 34 descriptive statistics associated with water
surface
s such as elevation variance, average slope variance, average point density, and average slope. It is ass
um
ed in this
study
that smooth water
surface
s are associated with pools and thus ought to have relatively low slopes. Conversely
rough
water
surface
s are ass
um
ed 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 deter
min
ed 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 exa
min
es the relationship between LiDAR measured water
surface
slopes, point density, and water
surface
rough
nes
s. Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 The difference between water
surface
elevations from LiDAR affects the n
um
erator within the rise over run equation, which in t
um
affects slope. This elevation analysis ev
alu
ation quantifies differences between field and LiDAR data. LiDAR-based cross section elevations were differenced from field-based cross section elevations. Difference v
alu
es were exa
min
ed th
rough
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
Surface
s 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. V
alu
es on x axis represent
min
im
um
difference within range. For example, the 0.01 category includes v
alu
es 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 v
alu
es 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 Skew
nes
s -0.484 Range of difference 0.093
Min
im
um
difference 0.002 Absolute maxim
um
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
Surface Elevation (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
surface
s shows a clear relationship in overall shape (Figure 16), capturing si
mil
ar tr
end
s in longitudinal profiles. Figure 16 shows field and LiDAR profiles become more si
mil
ar 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 v
alu
es 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>< SIl"> SIll\- ~<::J <;:><::J <;:><::J SIl" ~ SIl" SIll\- SIl"> SIll>< ~/l, r;:,<::J ~'::; ~'::; ~'::; ~'::; ~o Slope Difference (Field-LiDAR) A Differences of Slope at 10m Between Field and L1DAR 7 6 ~ 5 lii 4 :J 0" 3 ~ u. 2 1 o +---+--~--;..J SIll>< ~<::J Slope Difference (Field-LiDAR) B Differences of Slope at 20m Between Field and LiDAR 4 ~~I\- ~~" ~ ~~" ~~I\- ~~"> ~~I>< o"/l, <;:>.~. ~.~.~.~. ~ Slope Difference (Field-LiDAR) o +---+--+--+--t- SIll>< SIl"> <;:><::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 v
alu
es 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. Maxim
um
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 v
alu
es (Field
min
us LiDAR). Slope v
alu
es 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
Min
im
um
difference 0.0000 0.0000 0.0001 Maxim
um
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 v
alu
es. Slope v
alu
es have dimensionless units stem
min
g 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
Rough
nes
s Analysis Water
surface
condition was characterized as smooth or
rough
based on 2006 aerial photography (Figure 19).
Surface
rough
nes
s was exa
min
ed 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
Surface
s to LiDAR Point Density. 2006 aerial photos were used to de
lin
eate
rough
and smooth water
surface
s. 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
rough
nes
s 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
Rough
nes
s 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 v
alu
es for each Reach. Slope v
alu
es have dimensionless units from rise over run equation derived from ESRI generated slope grid. Point density v
alu
es 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
surface
s have greater slopes than smooth water
surface
s. 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
Rough
nes
s Comparison. Reach 1 water
surface
rough
nes
s slope analysis. Reach 1 was divided into smooth and
rough
water
surface
s based upon visual characteristics present in aerial photography. Slopes were calculated for each area and compared with field data to exa
min
e 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
surface
s. The super critical flow at the dam yielded a slope of - 0.896 (Table 7). The run below the dam contained low slope v
alu
es 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 V
alu
e 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
Rough
nes
s Analysis Near Marmot Dam. Subset of Reach 3 immediately surrounding Marmot Dam
rough
nes
s analysis containing v
alu
es for Mannot Dam. The
rough
nes
s 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 v
end
or. The second source can be associated with the accuracy of field based measurements which are si
mil
ar 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 th
rough
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
lin
es of interpolation associated with smooth water
surface
s (right image). Smooth water
surface
s t
end
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
lin
es of interpolation. The comparability of LiDAR and field-based slopes showed a significant tr
end
with increasing downstream distances between cross sections. Adjusted R2 v
alu
es 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
rough
nes
s found that
rough
water
surface
s had significantly higher point densities than smooth water
surface
s.
Rough
water
surface
s averaged at least 1 point/m2 , while smooth water
surface
s 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 tr
end
s relating water
surface
rough
nes
s and slope.
Rough
water
surface
s for all three analysis reaches averaged larger average slope v
alu
es than smooth water
surface
s. This is because
rough
water
surface
s 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
surface
s are commonly associated with pools or glides, which would be areas of lower slope. Future research should exa
min
e 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 arg
um
ent 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 pi
lin
g 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 instr
um
entation and v
end
or capabilities. LiDAR must be corrected for calibrations and GPS drift to create a reliable data set, and not all LiDAR v
end
ors 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 exa
min
ed 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 exa
min
e 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: APP
END
IX ARCGIS VBA SCRIPT CODE 58 Public g---.pStrmLayer As ILayer ' stream center
lin
e layer selected by user (for step 1) Public g_StrearnLength As Double ' stream center
lin
e length (for step 1) Public g_InputDistance As Integer 'As Double 'distance entered by user (for step 1) Public g_N
um
Segments As Integer I n
um
ber of sample points entered by user (for step 1) Public gyPointLayer As ILayer I point layer created from stream center
lin
e (for step 1) Public g]ntShpF1Name As String I point layer pathname (for step 1) Public gyMouseCursor As IMouseCursor 'mouse cursor Public g_
Lin
earConverson As Double I
lin
ear 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 'maxim
um
search distance (for step 4) Public L N
um
Directions As Integer I n
um
ber of directions to search in (for step 4) Public g_SampleDistance As Double 'sample distance (for step 5) Public g_SampleN
um
ber As Double ' total sample points (for step 5) Public g_VegBeginPoint As Boolean I where to start the c
alu
caltion (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 center
lin
e 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 Center
lin
e 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(ThisDoc
um
ent.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(pPoly
lin
e As IPoly
lin
e, dAlong As Double) As Double Dim pi As Double pi = 4 * Atn(l) Dim dAngle As Double Dim p
Lin
e As I
Lin
e Set p
Lin
e = New
Lin
e pPoly
lin
e.QueryTangent esriNoExtension, dAlong, False, 1, p
Lin
e , convert from radians to degrees dAngle = (180 * p
Lin
e.Angle) / pi I adjust angles , ESRI defi
nes
0 degrees as the positive X-axis, increasing counter-clockwise I Ecology references 0 degrees as North, increasing clockwise If dAngle <= 90 Then GetAngle = 90 - dAngle Else GetAngle = 360 - (dAngle - 90)
End
If
End
Function Function FeatureExists(strFeatureFileName As String) As Boolean On Error GoTo ErrHandler: Dim pWSF As IWorkspaceFactory Set pWSF = New ShapefileWorkspaceFactory Dim pFeatWS As IFeatureWorksiJace Dim pFeatDS As IFeatureClass Dim strWorkspace As String Dim strFeatDS As String strWorkspace = SplitWorkspaceName(strFeatureFileName) & "\" strFeatDS = SplitFileName(strFeatureFileName) If PWSF.IsWorkspace(strWorkspace) Then Set pFeatWS = pWSF.OpenFromFile(strWorkspace, 0) Set pFeatDS = pFeatWS.OpenFeatureClass(strFeatDS)
End
If 60 FeatureExists = (Not pFeatDS Is Nothing) Set pWSF =Nothing Set pFeatWS = Nothing Set pFeatDS = Nothing Exit Function ErrHandler: FeatureExists = False
End
Function 'Returns a Workspace given for example C: \temp\dataset returns C:\temp Function SplitWorkspaceName(sWholeName As String) As String On Error GoTo ERH Dim pos As Integer pos = InStrRev(sWholeName, "\") If pos > 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 ISimple
Lin
eDecorationElement Set MakeDecoElement
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