This is a cartoon of the standard NIR lidar. Only get 5 partial returns from each laser shot. In effect we re wasting much of the laser

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2 This is a cartoon of the standard NIR lidar. Only get 5 partial returns from each laser shot. In effect we re wasting much of the laser energy\project costs, because we never record much of the energy from each pulse. 2

3 Contrast between full-waveform recording and discrete sampling of that waveform. Some of the fixed-interval discrete samples may be very informative, while others are much less so, depending on where they occur in the waveform. 3

4 This illustrates how water absorbs most of the energy from near-infrared lidars. Over the forest the red dots are the third-returns from somewhere in the middle (vertically) of a forest canopy. The small white areas are individual tree crowns. 4

5 The Experimental Advanced Airborne Research Lidar (EAARL) is a relatively new research instrument with several design features that are attractive for stream bathymetry. EAARL is a full-waveform green lidar with the capability to penetrate water (depth and clarity dependent) Note that new system (2010) will have much improved data density relative to the performance described in this slide. Laser power will also be somewhat greater in new system. 5

6 -Cross environment veg/topo/bathy capabilities -Maps detailed topography in shallow non-marine/marine environments with clear water. -Note the waveform digitizing capabilities and significance 6

7 The very wide dynamic range of aquatic environments makes the unique EAARL design essential with multiple Photo Multiplier Tubes (PMTs) sampling different portions of the returned energy. 7

8 This simplified block diagram describes the wide dynamic range of the EAARL sensor. The Optical receiver front end is comprised of : a telescope, filters, apertures, lenses, etc. and is depicted by the blue box. The dotted lines represent the photon paths through a system of distortion free optical power dividers that splits the signal so 90% goes to PMT-1, 9% to PMT-2, and 1% to PMT-3. The group delay, or propagation time, through the system is carefully controlled through the entire optical and electrical signal path. The PMTs each convert their fraction of the arriving optical energy to electrical impulses which are each delivered to corresponding channels within one of two waveform digitizer banks. When the laser is triggered, a small sample is coupled via fiber optic to PMT-3 resulting is the system transmit pulse. The transmit pulse marks the beginning of the laser distance measurement and also gives the laser power and pulse shape characteristics. 8

9 This is a cartoon of how the EAARL laser interacts in water. The EAARL system sends out a short green wavelength (532nm) laser pulse that has a cm footprint when it begins to interact with the water surface. The entire time history of the outgoing laser pulse is digitized and stored (thus this is a full-waveform laser system). As the pulse interacts with the water surface, portions of the energy are reflected from various layers in the water column. This time history of the reflected energy is digitized by the array of PMTs and the relevant portions of the digitized return are recorded as a waveform. At any particular time, the amplitude of the waveform measures the strength of the return. Inflections in the waveform that comprise sufficient leading and trailing edges generally define a layer in the water column. Depending on the signal strength and the water depth and clarity, the laser will return another peak in the backscatter indicating that the pulse has penetrated through to the bottom. Note also that laser velocity in water is ~75% of air velocity - - so we need to recognize the water surface, otherwise we will under-predict the bed elevation because we assumed air velocity throughout the travel time, when actually it was slower for part of the travel time. 9

10 The elevation of the bed of a channel is normally calculated by measuring the difference in time between the centroid of the peak from the water surface and the centroid of a peak from the bed. 10

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12 Here EAARL bathymetric data for part of a channel are combined with the digital color-infrared camera imagery also gathered in each EAARL mission. Coregistration was only done manually and so isn t exact in this example. 12

13 EAARL can also be flown in a Pilatus PC-6 aircraft that is better equipped for mountain canyon flying. 13

14 The darker green spots represent the pattern of elevation measurements using the existing EAARL system. The olive green spots represent the additional point elevation measurements that will be taken with the improved EAARL. 14

15 The full-waveform data requires custom-written processing code. 15

16 This screen capture shows many of the important elements of the ALPS GUI interface. 16

17 This is an example of the data density from 2004 in Bear Valley Cr., Idaho. The red dots are places where the lidar recorded either the exposed bare earth elevation or the submerged ground elevation (if inside the channel). The point cloud data were then gridded and contoured. The data density is greater in some areas where we happened to get 2 passes of data. We try to fly multiple flight lines with overlapping coverage everywhere to compensate for the somewhat lower pulse frequency of EAARL (it s a brute force way to increase the data density). Most of the contours outside the channel have been removed to make the image less busy. A few off-channel habitat areas were left as noted. 17

18 This is a side-by-side comparison of a survey-grade GPS wading survey and the bathymetric lidar. The GPS surveyors missed a small deep pool at E, N, so in this case the lidar survey is more accurate. The field photo is taken from the position of the flow arrow in the right panel - - looking downstream. 18

19 This slide shows a more detailed accuracy assessment of the same stream reach as in the previous slide. The center map is a difference image produced by subtracting an EAARL-derived DEM from a DEM made by an intensive ground survey. The largest lidar errors are along the banks. The bottom figure is a long profile down the channel (along the dashed line in the central figure). In the profile the black line is from actual ground survey points. The red and green lines are along the same track, but taken from the ground survey and lidar DEMs respectively. For more accuracy information see: McKean, J., Nagel, D., Tonina, D., Bailey, P., Wright, C.W., Bohn, C., Nayegandhi, A., Remote sensing of channels and riparian zones with a narrow-beam aquatic-terrestrial lidar. Remote Sensing, 1, ; doi: /rs

20 Comparison of field surveyed and lidar surveyed hydraulic geometry metrics. 20

21 Stream metrics that seem to be mappable using the EAARL sensor. 21

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24 The next 3 figures are from a study of the effects of glaciation on the spatial pattern of spawning habitat in the upper Middle Fork Salmon River, Idaho. This project used a combination of floodplain and in-stream topography mapped by the EAARL. 24

25 Figure shows valley post-glacial excavation that has proceeded upstream to the step in the topography at Channel Distance 4km. Note changes in channel form along this 10km valley. 25

26 Here the valley gradient has been removed (the data are detrended). This emphasizes the local inset erosion surfaces. Insets b and c show again the bathymetric detail that is available anywhere in the channel. 26

27 Example of a basic channel metric that can be extracted from the data. It is easy to change your definition of a pool and recalculate this. The data were collected in 300m reaches along the channel that can also easily be changed. Note the uneven distribution of pools along this unconfined channel. This is an example of the very objective measurement of habitat using digital data as opposed to the subjectivity of most field surveys. The more detailed lidar data are inset in a standard 10m DEM. 27

28 Map of channel changes over a 3 year period. In common with all airborne lidars, the EAARL maps elevations better than it does horizontal position (X,Y). This means that the sensor maps bed elevation changes much better than it does the planimetric location of a laterally-migrating stream bank. In this example a sand bed channel moves dramatically over time and it appears to be partially abandoning the upper flow path (in blue) and reoccupying an older meander on the lower right portion of the figure. In contrast, the older meander in the lower left is not reattached to the main flow. 28

29 Another example of change over a 3 year period. The circle and rectangle show areas where deposition has occurred. 29

30 An example of using EAARL data to investigate a potentially serious management issue using a 2D computational fluid dynamics model. Many climate change models predict more rain during winter in mountainous areas where historically the water year has been dominated by spring snowmelt and mid-winter flows are very low. The threatened and endangered fall-spawning Chinook salmon lay eggs in stream beds in late summer. Presently these eggs incubate during the very low flow conditions over the winter and emerge just prior to the snowmelt peak in the spring. A concern is that increased flows in mid-winter could mobilize the bed and endanger the eggs. 30

31 We use the bathymetric lidar data to define the boundary conditions in a computational fluid dynamics (CFD) flow model to analyze this question. First we model the bed shear stress with the CFD model MDSWMS (USGS; McDonald et al, 2005). We calibrate by water levels and discharges measured at a study site in upper Bear Valley Cr., Middle Fork Salmon River, Idaho. We can model shear stress as a function of discharge and the spatial resolution of our stress predictions is about 1 sq meter. 31

32 Here is another example of predicted bed shear stresses at low flow and bankfull flow conditions. 32

33 We compare the modeled shear stresses to a critical shear stress for initial motion. In this example, we use the Andrews and Parker equation for sizedependent critical shear stress. The grain size grading curves are for the entire study channel bed and for a subset of just the salmon spawning sites on riffles. The upper right graph shows mobility curves for each grain size (in mm) as a function of discharge. This shows the hypothetical bed areas that would be mobile if the bed was made entirely of each particular grain size. But there is a mix of grain sizes so we convolve the upper right mobility curves with the actual grain size distributions for the whole bed and for the spawning site subset. Assuming that if a given grain size is mobile, then all particles that are smaller than that size are also mobile, you end up with the cumulative mobility curves in the lower right figure. Conclusion: in this low-gradient, unconfined stream with a strong glacial legacy, the existing gravels are not very mobile, even at the highest possible flows (bankfull). This is corroborated by field measurements of bedload movement at 90% of bankfull. So salmon are not particularly vulnerable to this dimension of climate change in this landscape. 33

34 This instrument allows us to map long continuous segments of a channel network well beyond the spatial scope of other survey methods, albeit with lower precision and accuracy than, for example, field surveys. This then raises the question of if there are other useful ways to interpret these continuous data, beyond traditional studies of channels at cross-section and reach scales. I suggest the frequency domain may be a fruitful approach. 34

35 Here we see a 10km-long thalweg profile hand digitized in a raster made from EAARL data. The subject stream is in a wide unconfined alluvial valley throughout this length with essentially no outside contributing sources of water or sediment. The red straight line is included for perspective. There are multiple scales of nested topographic features. Remember that a very detailed field survey might cover 200m of this 10km (20X the channel width). Would that be a good representative sample? If you moved the 200m sample around, would you get the same representation of this 10km of channel? 35

36 There are many ways to evaluate continuous data like a thalweg elevation profile in the frequency domain. I use wavelets as it is a technique that quickly explores patterns in space and scale at the same time. Above is a cartoon of how the wavelet convolution method works. 1. A reference shape or mother wavelet is chosen normally from a library of such wavelets. For smoothly-varying continuous data, almost any of the mother wavelets gives a similar answer. This reference wavelet is set at some spatial scale and then moved along the signal being analyzed (the thalweg profile in our case). 2. At each position the reference wavelet is convolved with the portion of the data signal that it covers. This establishes an objective quantitative measure of the similarity between the reference and the object signal at that scale and location. 3. Then the spatial scale of the reference wavelet is changed (it is either stretched or shrunk, but the shape is kept the same) and the process is repeated. Thus one builds up a field of similarity coefficients as a function of spatial scale and position. 4. The similarity coefficients can be squared to give a measure of spectral power. 36

37 Here we have a fairly obvious change in stream morphology that illustrates one use of the wavelet method. The raster (made from EAARL data) has been detrended to remove the general valley slope. At the Morphologic Domain Boundary in the inset figure the stream exits a broad unconfined alluvial valley and enters a confined mountain canyon. Here it also changes from a meandering pool-riffle morphology to more of a straight plane bed stream. The former is very good salmon-spawning habitat while the latter is very poor habitat. 37

38 The upper figure is the detrended thalweg profile of the entire 22km of channel shown in the previous slide. The lower graph shows the variation in spectral power mapped by a 2 nd order Gaussian reference wavelet that was 100m long. There is an obvious change in power at the morphologic domain boundary. The table presents spectral statistics of the two domains. 38

39 We have built an ArcGIS toolkit to automatically extract many common hydraulic geometry measures from high resolution DEMs. The DEMs can be from any source not confined to a bathymetric lidar This slide shows the basic work flow in the toolkit. 39

40 The RBT depends critically on an accurate definition of bankfull. There are a variety of ways this stage can be defined. 1. The software attempts to do this morphologically using a calculation of the ratio of wetted reach volume / wetted reach area. Starting from a dry stream bed, this ratio initially rises with stage. If a well defined floodplain is present, the ratio will drop when the stage is just high enough to inundate the floodplain. 2. The user can also manually adjust the bankfull by flooding a detrended raster as a horizontal water stage is raised through the topography and the changing flooded area is shown in a map view (the lower right image in this slide). 3. Field survey locations of bankfull can also be entered into the detrended raster. 4. A flow model can predict bankfull 5. Bankfull can be digitized from airphotos. 40

41 A detrended raster made from EAARL data showing about 15km of unconfined channel. The next image is a close-up of the area in the inset dashed green box. 41

42 The RBT allows the user to: 1. Use several methods to define where to place cross sections. 2. Examine the shape of an individual section and its associated hydraulic geometry metadata. The example graph is for the blue cross section in the map and the graph shows the geomorphically-defined bankfull level and hydraulic geometry values Metadata are stored in a dbf and available to compute geometry statistics from large groups of cross sections or to export data to 1D flow models. 42

43 At the right is a close-up of the upper end of a channel segment in which 900 cross sections were mapped at 10m intervals down the channel. The cross section explorer is used to look at any 1 or more sections. Here we are looking at section 21 (the one in blue on the right side map). The explorer shows the hydraulic geometry measures of this section these data are also stored in an extractable data base file. The geometry measures were taken using the water table elevation shown in the figure that was produced automatically, but can be adjusted by the user to wherever you think bankfull is. The toolkit has several ways of estimating bankfull. 43

44 The upper figure shows the thalweg profile extracted using all 900 cross sections. The lower figure is a close-up view in the area where the gradient changes. 44

45 Any of the cross section hydraulic geometry metrics can be quickly mapped to the channel long profile. In this figure the variation with distance along the channel of both gradient and sinuosity are shown. The channel lengths over which these two variables are calculated is defined by the user (in this case it was 200m). 45

46 Longitudinal variation in bankfull width (top) and width/depth (bottom). 46

47 Scope = spatial range over which you can measure/ spatial resolution of things you can map 47

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