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Units改成1/1000 Centimeter 时,标题Header的Height属性怎么不是按厘米单位换算的
liubocy
2008-07-19 07:39:12
在我机器上pb65是按厘米来换算的,
pb8,pb9的值不知道怎么算来的。
我的Header的高度是1厘米的时候,它的Height的值大概是1000。
而在Header区里的对象的x、y、高、宽都是厘米的值
是没打补丁的问题吗,如果是去哪下载?
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Units改成1/1000 Centimeter 时,标题Header的Height属性怎么不是按厘米单位换算的
在我机器上pb65是按厘米来换算的, pb8,pb9的值不知道怎么算来的。 我的Header的高度是1厘米的时候,它的Height的值大概是1000。 而在Header区里的对象的x、y、高、宽都是厘米的值 是没打补丁的问题吗,如果是去哪下载?
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annymu
2008-07-19
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[Quote=引用 1 楼 SummerHeart 的回复:]
1厘米 不就等于1000 毫米吗,即(1/1000 Centimeter)
[/Quote]
1厘米通常等于10毫米
至于楼主的问题,没太看明白,而且建议先打补丁
编程夜猫
2008-07-19
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1厘米 不就等于1000 毫米吗,即(1/1000 Centimeter)
SAP物理
单位
.csv
编码 符号 中文注释
换算
单位
编号
换算
数量 英文注释 22 dl/g 分升/克 A39 10 Decili
ter
per gram 23 g/cm³ 克/立方
厘米
KMQ
1000
Gram per cubic
cen
time
ter
25 g/cm² 克/平方
厘米
28 10 Gram per square
cen
time
ter
28 kg/m² 千克/平方米 28 1 Kilogram per square me
ter
2A rad/s 弧度/秒 2A 0.159155 Radian per second 2B rad/s² 弧度/平方秒 2B 0.159155 Radian per second squared 2C R 伦琴 CKG 0.000258 Roentgen 2I BtuIT/h 英热
单位
/小
时
WTT 0.2930711 British thermal unit per hour 2J cm³/s 立方
厘米
/秒 MQS 0.000001 Cubic
cen
time
ter
per second 2K ft³/h 立方英尺/小
时
MQS 7.87E-06 Cubic f
height
-conver
ter
.github.io:一个适合您的简单的高度转换器项目。 您可以将高度转换为
厘米
,英寸,英尺和米。:winking_face:
嗨,我是Sagar Sharma 这是我的新项目,称为高度转换器。 点击链接查看结果 文字编辑器二手 使用的语言 四个不同的
单位
是:-
厘米
英寸 脚 仪表 要求 Express.js 如何快递? 首先使用npm命令$ npm init制作一个package.json文件 然后键入第二个命令来获得快递$ npm i express 现在您可以检查对package.json的依赖性 使用命令行克隆此存储库 打开Git Bash 。 将当前工作目录更改为要克隆目录的位置。 输入git clone https://github.com/Sagar-Sharma-7/
height
-conver
ter
.github.io.git 按En
ter
键创建此存储库的克隆。 如何到达我?
浅谈CSS中的尺寸
单位
浏览器的兼容性越来越好,移动端基本是清一色的webkit,经常会用到css的不同尺寸/长度
单位
,这里做个整理。 概览 绝对
单位
px: Pixel 像素 pt: Points 磅 pc: Picas 派卡 in: Inches 英寸 mm: Millime
ter
毫米 cm:
Cen
time
ter
厘米
q: Quar
ter
millime
ter
s 1/4毫米 相对
单位
%: 百分比 em: Element me
ter
根据文档字体计算尺寸 rem: Root element me
ter
根据根文档( body/h
RulerControl:RulerControl是可拖动的,可调整大小的UIControl,可提供真实世界的长度测量
标尺控制 RulerControl是可拖动的,可调整大小的UIControl ,可提供真实世界的长度测量。 它支持各种长度
单位
,但最适合以
厘米
或英寸为
单位
的测量。 如何使用 实际
单位
中的测量 使用设备ppi设置plane : let device = Device () // Open source DeviceKit provides ppi if let ppi = device. pointsPerInch { let plane = Plane ( pointsPerUnit : ppi, unit : . inch ) rulerControl. plane = plane rulerControl. baseUnit = .
cen
time
ter
} rulerControl. addTarget ( self , action : #selecto
雷达技术知识
关于雷达方面的知识! EFFECTIVENESS OF EXTRACTING WA
TER
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 Mas
ter
of Science March 2009 11 "Effectiveness of Extracting Wa
ter
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 Mas
ter
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 Mas
ter
of Science March 2009 Title: EFFECTIVENESS OF EXTRACTING WA
TER
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 wa
ter
surface slopes in three reaches of the Sandy River, Oregon, USA. LiDAR data were compared with field measurements to evaluate accuracies and de
ter
mine how wa
ter
surface roughness and point density affect LiDAR measurements. Results show that LiDAR derived wa
ter
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 in
ter
vals of 5, 10, and 20m, respectively. Additionally, results show LiDAR provides grea
ter
data density where wa
ter
surfaces are broken. This study provides conclusive evidence supporting use ofLiDAR to measure wa
ter
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: Mas
ter
of Science, Geography, March 2009, University of Oregon Bachelor of Science, Geography, 2001, Southern Oregon University AREAS OF SPECIAL IN
TER
EST: 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 Chap
ter
Page I. INTRODUCTION 1 II. BACKGROlTND 5 Wa
ter
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 Wa
ter
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 Fil
ter
ing Processing Step 26 12. Field DEM In
ter
polated 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 me
ter
s) 40 17. Regression ofField and LiDAR Based Slopes (5, 10,20 me
ter
s) 42 18. Differences Between LiDAR and Field Based Slopes (5, 10,20 me
ter
s) 44 19. Relationship of Wa
ter
Surfaces to LiDAR Point Density 47 20. Marmot Dam: Orthophotographyand Colorized Slope Model 50 21. LiDAR Point Density versus In
ter
polation 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 me
ter
s) 45 4. Results of Reach 1 Slope Comparison 46 5. Wa
ter
Surface Roughness Results for Reach 1,2, and 3 48 6. Results of Reach 1 Wa
ter
Surface Roughness Comparison 49 7. Subset of Reach 3 Wa
ter
Surface Roughness Analysis Near Marmot Dam 50 x 1 CHAP
TER
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
Ter
rain Models (DTM) (Bowen & Wal
ter
mire, 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 charac
ter
istics 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 wa
ter
surfaces can be used to measure local wa
ter
surface slopes within the active channel. Much of the reason that researchers have not attempted to measure wa
ter
surface slopes with LiDAR is because most LiDAR pulses are absorbed or not returned from the wa
ter
surface. However, where the angle of incidence is close to nadir (i.e. the LiDAR pulse is fired near perpendicular to wa
ter
surface plane), light is reflected and provides elevations off the wa
ter
surface (Figure 1, Maslov et aI., 2000). Where LiDAR pulses glance the wa
ter
surface at angles of incidence grea
ter
than 53 degrees, a LiDAR pulse is 2 more often lost to refraction (Figure 2) (Jenkins, 1957). In broken wa
ter
surface conditions the wa
ter
surface plane is angled, which produces perpendicular angles of incidence allowing for grea
ter
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 wa
ter
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 wa
ter
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 wa
ter
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 Seawa
ter
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 grea
ter
as the angle of incidence approaches nadir. (Jenkins, F.A., White, RE. "Fundamentals of Optics". McGraw-Hili, 1957, Chap
ter
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 wa
ter
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 wa
ter
surface elevations and slopes. In order to address this topic, I assess the vertical accuracy of LiDAR and the effects of wa
ter
surface roughness on LiDAR within the active channel. Findings shed light on the utility of LiDAR for measuring wa
ter
surface slopes in different stream environments and methodological constraints to using LiDAR for this purpose. 4 5 CHAP
TER
II BACKGROlJND Wa
ter
Surface Slope Wa
ter
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 wa
ter
surface slope include both direct and indirect methods. Direct wa
ter
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 wa
ter
surface elevations (Harrelson, et ai., 1994, Wes
ter
n et ai., 1997). Inaccuracies in measurements stem from surface turbulence that makes it difficult to precisely locate the wa
ter
surface, especially in fast wa
ter
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 wa
ter
surface slope along large portions of a river. This method can be dangerous in deep or fast wa
ter
. 6 Indirect methods of wa
ter
surface slope measurement consist of acquiring approximate wa
ter
surface elevations using strand lines, wa
ter
marks, secondary data sources such as contours from topographic maps, or hydraulic modeling to back calculate the wa
ter
depth (USACE, 1993; Wes
ter
n et aI., 1997). Variable quality of data and modeling errors can lead to inaccuracies using these methods. The use of strand lines and wa
ter
marks may not necessarily represent the peak flows or the wa
ter
surface. Contours may be calculated or in
ter
polated 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 wa
ter
surface returns have a great deal of promise for improving measurement of wa
ter
surfaces in several significant ways. LiDAR measurements eliminate hazards associated with surveyors being in the wa
ter
. 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 wa
ter
's surface may be collected depending on the nature of surface roughness, with broken wa
ter
surfaces increasing the likelihood of measurements (Figure 3). In addition, most
ter
restrial 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 wa
ter
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 me
ter
s (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 wa
ter
surface elevations over large stretches of river within a single flight of a few hours. LiDAR Measurements of Active Channel Features Re
cen
t 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
ter
m 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 me
ter
spot spacing was used to estimate wa
ter
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 me
ter
LiDAR DEM cells. This study successfully attributed LiDAR DEM roughness within the channel to instream habitats. Bowen and Wal
ter
mire (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 me
ter
s. Accuracies within unvegetated areas and flat surfaces met vendor specifications (l5-20cm). James et al. (2007) used LiDAR at 3 me
ter
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 fil
ter
ing 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 me
ter
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
ter
races 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 wa
ter
surface elevation and did not attempt to demonstrate the accuracy of LiDAR derived wa
ter
surfaces. Green LiDAR also has been used to examine riverine environments. Green LiDAR functions much like
ter
restrial LiDAR (which uses an infrared laser) except that green LiDAR systems use green light that has the ability to penetrate the wa
ter
surface and measure the elevation of the channel bed. Green LiDAR is far less common than
ter
restrial LiDAR and the majority of studies have been
cen
ter
ed on studies of ocean shorelines. Wang and Philpot (2007) assessed attenuation parame
ter
s for measuring bathymetry in near shore shallow wa
ter
, 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 CHAP
TER
III STUDY AREA The study area is the Sandy River, Oregon, which flows from the wes
ter
n slopes ofMount Hood northwest to the Columbia River (Figure 4). Re
cen
t LiDAR data and aerial photography capture the variety of wa
ter
surface charac
ter
istics in the Sandy River, which range from shooting flow to wide pool-riffle formations. The re
cen
t removal of the large run-of-river Marmot Dam upstream of the analysis sites has also generated in
ter
est 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 win
ter
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'-.
1000
0 ~ 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://wa
ter
data.usgs.gov/or/nwis/annual/ Vegetation is mostly a mixture of Douglas fir and wes
ter
n 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 wes
ter
n red hemlock make up 87% of vegetated areas, palustrine forest 5%, and agricultural lands 5%, the remaining 3% is open wa
ter
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 wes
ter
n banks (Figure 7). The presence of Himalayan blackberry is significant because LiDAR has trouble penetrating through the dense clus
ter
s of vines. When this blackberry is close to the wa
ter
's edge it is difficult to accurately define the channel boundary. 14 15 545000 550000 555000 560000 565000 570000 Reach 3 10 !'
ter Palustrine 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 dominated by Douglas fir forest with areas of palustrine 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 wa
ter
surface conditions along the river. Reach 1 is a I80-m long pool-riffle reach located 3.7 river kilome
ter
s upstream from the mouth, and is where we collected field data shortly af
ter
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 me
ter
s 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 wa
ter
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 me
ter
s 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 in
ter
vals. The substrate consists of sands with small boulders and large cobbles dominating riffle areas. Cobbles and boulders have likely been introduced to the channel as a result of mass wasting. Douglas fir dominates along banks. 20 oJ> 0° 200 Me
ter
sO 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 wa
ter
surface delineations used in analysis. 21 Reach 3 is located 40.7km upstream from the mouth of the Sandy and is 2,815 me
ter
s in length (Figure 10). The widest portion of this section at approximate banle full is 88 me
ter
s. 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 wa
ter
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 CHAP
TER
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 af
ter
the 2007 LiDAR flight in order to compare field measurements of wa
ter
surface slope to LiDAR-based measurements.
Time
of flight field measurements of wa
ter
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
cen
time
ter
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 fil
ter
ed XYZ ASCII point data, LiDAR DEMs as ESRI formatted grids at 0.5 me
ter
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 (me
ter
s) (Leica, 2007). 24 Table 1. Reported Accuracies of 2006 and 2007 LiDAR. Reported Accuracies and conditions for 2006 and 2007 LiDAR data. (Wa
ter
shed Sciences PGE LiDAR Delivery Report, 2006, Wa
ter
shed 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 me
ter
s (AGL) 1100
1000
Absolute Vertical Accuracy in me
ter
s 0.063 0.034 Relative Accuracy in me
ter
s (calibration) 0.058 0.054 Horizontal Accuracy (l/3300th * AGL) me
ter
s 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) af
ter
the 2007 flight to collect ground truth data within the Reach 1. The initial aim was to survey wa
ter
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 me
ter
s 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 in
ter
vals of approximately 1 to 2 me
ter
s. Thirteen additional measurements were collected along the east bank at approximately ten me
ter
in
ter
vals. Depth measurements were added to bed elevations to derive wa
ter
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 wa
ter
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 Fil
ter
ing 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 wa
ter
surface. All bars and overhanging vegetation have been removed as well. 27 Wa
ter
points were classified using the ground classification algorithm in
Ter
rascan© (Soininen, 2005) to separate wa
ter
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 wa
ter
. Points classified as wa
ter
were output as comma delimited x,y,z ASCII text files (XYZ), then converted to a 0.5 me
ter
linearly in
ter
polated ESRI formatted grid using ESRI geoprocessing model script. Calculation of Wa
ter
Surface Slopes Wa
ter
surface slopes were calculated using the rise over run dimensionless slope equation where the rise is the vertical difference between upstream and downstream wa
ter
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 wa
ter
surface slopes. We used linear in
ter
polation 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 in
ter
polate field 28 measurements to create a wa
ter
surface for the entire stream. The field data-based DEM was created using kriging in
ter
polation within ArcGIS Desktop Spatial Analyst (Figure 12). No quantitative analysis was performed to evaluate the in
ter
polation method of the field-based wa
ter
surface. The kriging in
ter
polation was chosen because it producex the smoothest wa
ter
surface based on visual inspection when compared to linear and natural neighbor in
ter
polations, which generated irregular fluctuations that were unrealistic for a wa
ter
surface. The kriged surface provided a wa
ter
surface elevation model for comparative analysis with LiDAR. 29 Figure 12. Field DEM In
ter
polated using Kriging. Field DEM in
ter
polated from field survey points using kriging method found in ArcGIS Spatial Analyst. DEM has been hiIlshaded to show surface charac
ter
istics. The very small differences in wa
ter
surface elevations generate only slight variations in the hillshadeing. To compare LiDAR and field-based wa
ter
surface slopes, wa
ter
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 wa
ter
surface slopes that are more representative of the entire channel. The Sm in
ter
val spacing was considered to be a sufficient for fine resolution slope extraction. Because cross section
cen
ter
points were used to calculate the longitudinal distance and because the stream was sinuous, the projection of the cross sections from the
cen
ter
line to the banks led to stream side distances between cross sections that differed from Sm. 30 31 Smooth 125 Me
ter
s 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 wa
ter
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 me
ter
in
ter
vals creating 444 cross section sample locations (Figure 13). Cross section averages were calculated using field-based and LiDAR-based elevation wa
ter
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 charac
ter
ize how LiDAR-based elevations, slopes and point densities in
ter
act with varying wa
ter
surface roughness. Within Reach 2, 359 cross sections were drawn and elevations were sampled every five me
ter
s 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 in
ter
vals of five, ten and twenty me
ter
s 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 af
ter
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 wa
ter
surface roughness on LiDAR elevation measurements, LiDAR point density, and LiDAR derived wa
ter
surface slopes. Each reach was divided into smooth and rough sections based on visual analysis of the orthophoto data. One-me
ter
resolution slope ras
ter
s were created from the LiDAR wa
ter
surface grids using ArcGIS Spatial Analyst. One me
ter
resolution point density grids were created from LiDAR point data (ArcGIS Spatial Analyst). Using the cross section sample points, values for wa
ter
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 wa
ter
surfaces such as elevation variance, average slope variance, average point density, and average slope. It is assumed in this study that smooth wa
ter
surfaces are associated with pools and thus ought to have relatively low slopes. Conversely rough wa
ter
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 wa
ter
surface conditions as de
ter
mined from the aerial photos. 35 CHAP
TER
V RESULTS Results of this study encompass three analyses. Elevation analysis describes the statistical difference between LiDAR and field-based wa
ter
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, wa
ter
surface analysis examines the relationship between LiDAR measured wa
ter
surface slopes, point density, and wa
ter
surface roughness. Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 The difference between wa
ter
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
ter
ms of absolute elevations relative to sea level, the majority of LiDAR-based wa
ter
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 wa
ter
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 wa
ter
surface measurements are comparable. 37 Distribution of Elevation Differences Between Field and LiDAR Wa
ter
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 me
ter
s. 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 Wa
ter
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
ter 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 wa
ter
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
ter
ms 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 me
ter
Longitudinal Profile Comparison 20 40 60 80 100 120 140 160 180 5.75 .s 5.70 ~" _ • •• • :. 5
ter Field Profile II:: 5.65 ...- .=....:....:l..,... H.T• tI.:!..~.....~.Io-,•..-..;.....-.------j. 5rre
ter
L,DARprof,lel- ..0.._. 5.60 .. • •• ~ 5.55 -1------------ .~•.~•.-.-.-------- ~ 5.50 +---------------"''-.--'~~ ~.. ,~ "yT1I:!'-'--- W 5.45 -1---------------.-::..---'.1-.........-- ...:I:C"IL'J-"---- 5.40 -t------,----,---------,----,------,----,---------,----,---------, o Longitudinal Distance Down Stream (m) A 10 me
ter
Longitudinal Profile Comparison 5.75 5.70 . • [,.10 rreler Field Profile! I: I 5.65 • • , . • • • 10 rre
ter
LiDAR Profile • • • I:: 5.60 • • 0 :;:; • • >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 me
ter
Longitudinal Profile Comparison 5.75 5.70 • ,. 20
ter Field Profile .s 5.65 • . • • • 20 rreler LiDARProfile • I:: 5.60 • 0 :;:; >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 me
ter
s). Longitudinal profiles of a) 5 me
ter
, b) 10 me
ter
, and c) 20 me
ter
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 in
ter
vals to test the sensitivity of the LiDAR's in
ter
nal relative accuracy. Differences in Sm LiDAR and field-based slopes derived from cross sections reveal substantial scat
ter
(Figure l7a), although they clearly covary. Ten me
ter
in
ter
val 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 me
ter
Slope Comparison 0.004 y = 0.63x - 0.001 R2 = 0.51 -0.008 -0.008 Field Slope (Rise/Run) 20 me
ter
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 me
ter
s). Scat
ter
plots showing comparisons between slope values calculated at distance in
ter
vals of a) 5 me
ter
s, b) 10 me
ter
s, and c) 20 me
ter
s. 43 Figure 18 shows how the range of differences between LiDAR and field-based wa
ter
surface slopes decrease as longitudinal distance increases. Five me
ter
slope differences ranged between -0.004 and 0.004 (Figure 18a). Ten me
ter
slope differences ranged between -0.002 and 0.003 (Figure 18b). Twenty me
ter
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 me
ter
s). Histogram charts showing difference values between field and LiDAR derived slopes at a) 5 me
ter
slope distances, b) 10 me
ter
slope distances, and c) 20 me
ter
slope distances. 45 The mean difference between slopes decreases from 0.0017 to 0.0007 as slope distance in
ter
val 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 in
ter
val (Table 3). Table 3. Results of LiDAR and Field Slope Comparison (5, 10,20 me
ter
s). 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 In
ter
val 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 Wa
ter
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 me
ter
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 Wa
ter
surface condition was charac
ter
ized 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 wa
ter
surface turbulence. Table 5 shows statistics with relation to wa
ter
surface condition for all three reaches. 47 Figure 19. Relationship of Wa
ter
Surfaces to LiDAR Point Density. 2006 aerial photos were used to delineate rough and smooth wa
ter
surfaces. Image on left shows a transition between rough wa
ter
surface (seen as white wa
ter
) and smooth wa
ter
surface (seen as upstream pool). Image on right shows LiDAR point density in points per square me
ter
. In all reaches point density, variance of elevations, and wa
ter
surface slopes were significantly higher in rough surface conditions. These results indicate that LiDAR point density is directly related to the roughness of a wa
ter
surface and that is capturing the rough wa
ter
charac
ter
istics one would expect in areas where turbulence generates surface waves. 48 Table 5. Wa
ter
Surface Roughness Results for Reach 1,2, and 3. Wa
ter
surface statistical output for rough and smooth wa
ter
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 me
ter
s. Reach 1 Reach 2 Reach 3 Rou~h wa
ter
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 wa
ter
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 wa
ter
conditions and slopes were calculated using field and LiDAR data sets (Table 6). Again, results showed that rough wa
ter
surfaces have grea
ter
slopes than smooth wa
ter
surfaces. The smooth wa
ter
surface of Reach 1 yielded a larger discrepancy between field and LiDAR derived slopes compared to rough wa
ter
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 Wa
ter
Surface Roughness Comparison. Reach 1 wa
ter
surface roughness slope analysis. Reach 1 was divided into smooth and rough wa
ter
surfaces based upon visual charac
ter
istics 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 wa
ter
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 adja
cen
t run yielded high point densities of grea
ter
than 2 points per square me
ter
. 50 Cross Sections o Cross Section Sample Locations L1DAR derived Slope Model Value Higll 178814133 25 50 75 100 125 150 ~.',e
ter
s 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 Wa
ter
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 CHAP
TER
VI DISCUSSION The elevation analysis portion of this study shows that LiDAR can provide wa
ter
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 grea
ter
lengths of in
ter
polation 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 In
ter
polation in Low Point Density Figure 21. LiDAR Point Density versus In
ter
polation. Side by side image showing long lines of in
ter
polation associated with smooth wa
ter
surfaces (right image). Smooth wa
ter
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 in
ter
polation. 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 wa
ter
surface elevation has less effect on wa
ter
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 wa
ter
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 me
ter
s respectively. Although the discrepancy between field and LiDAR-based slopes is greatest at 5-m in
ter
vals, the overall slopes (Fig 17) and longitudinal profiles (Fig 16) even at this distance generally correspond. The use of a 5m in
ter
val wa
ter
surface slope as a basis for comparison is really a worst case example, as wa
ter
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
ter
ms of spatial extent and resolution of wa
ter
surface slope measurements. Analysis of surface roughness found that rough wa
ter
surfaces had significantly higher point densities than smooth wa
ter
surfaces. Rough wa
ter
surfaces averaged at least 1 point/m2 , while smooth wa
ter
surfaces averaged less than 1 point/2m2 • Longitudinal profiles of Reach 1 indicate the most accurate wa
ter
surface measurements occur in areas of higher point density (Fig. 16). Future applications that attempt to use 55 LiDAR to measure wa
ter
surface slope ought to sample DEM elevations from high point density areas of channel. Wa
ter
surface analysis also showed trends relating wa
ter
surface roughness and slope. Rough wa
ter
surfaces for all three analysis reaches averaged larger average slope values than smooth wa
ter
surfaces. This is because rough wa
ter
surfaces are commonly associated with steps, riffles, and rapids. All three of these habitat types are areas have higher slopes than smooth wa
ter
habitats. Smooth wa
ter
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 charac
ter
ize 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 wa
ter
surface slopes as control for measuring "accuracy". Wa
ter
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 wa
ter
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 wa
ter
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 wa
ter
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 wa
ter
surface elevations because of protruding boulders or dense vegetation that hinders accurate measurements. In some cases vegetation within and adja
cen
t to the channel may in
ter
fere with LiDAR's ability to reach the wa
ter
surface. Researchers should consider flow, channel morphology, and biota when obtaining wa
ter
surface slopes from LiDAR. 57 CHAP
TER
VII CONCLUSION This paper examined the ability of LiDAR data to accurately measure wa
ter
surface slopes. This study has shown that LiDAR data provides sufficiently accurate elevation measurements within the active channel to accurately measure wa
ter
surface slopes. Measurement of wa
ter
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. Wa
ter
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 wa
ter
surface slopes as these attributes influence wa
ter
surface measurement. Further study should examine accuracy of LiDAR derived wa
ter
surface slopes in channel morphologies other than those in this study. Overall, the recognition that LiDAR can accurately measure wa
ter
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
cen
ter
line layer selected by user (for step 1) Public g_StrearnLength As Double ' stream
cen
ter
line length (for step 1) Public g_InputDistance As Integer 'As Double 'distance en
ter
ed by user (for step 1) Public g_NumSegments As Integer I number of sample points en
ter
ed by user (for step 1) Public gyPointLayer As ILayer I point layer created from stream
cen
ter
line (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 IRas
ter
Layer I DEM layer (for steps 3 and 4) Public g_DEMConvert
Units
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
cen
ter
line 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
Cen
ter
line Point Shapefile" pGxdial.RememberLocation = True Dim pShapeFileObj As IGxObject Dim pGxFil
ter
As IGxObjectFil
ter
Set pGxFil
ter
= New GxFil
ter
Shapefiles 'e.g shp Set pGxdial.ObjectFil
ter
= pGxFil
ter
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 coun
ter
-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 = SplitWorkspa
ceN
ame(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 SplitWorkspa
ceN
ame(sWholeName As String) As String On Error GoTo ERH Dim pos As Integer pos = InStrRev(sWholeName, "\") If pos > 0 Then SplitWorkspa
ceN
ame = 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
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