Evaluating Low Cost Topographic Surveys For Computations Of Conveyance.
Evaluating low cost topographic surveys for computations ofconveyance.Hubert T. Samboko1, Sten Schurer1, Hubert H.G. Savenije1, Hodson Makurira2, Kawawa Banda3, HesselWinsemius1, 4, 55101Department of Water Resources, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg1, 2628 CN, Delft, Netherlands2Department of Construction and Civil Engineering, University of Zimbabwe, Box MP 167, Mt. Pleasant, Harare, Zimbabwe3Department of Geology, Integrated Water Resources Management Centre, University of Zambia, Great East Road Campus,P.O. Box 32379, Lusaka, Zambia4Deltares, Delft, the Netherlands5Rainbow Sensing, The Hague, the NetherlandsCorrespondence to: Hubert T. Samboko (hsamboko@gmail.com)Abstract. Rapid modern technological advancements have led to significant improvements in river monitoring usingUnmanned Aerial vehicles (UAVs), photogrammetric reconstruction software and low-cost Real Time Kinematic Global15Navigation Satellite System (RTK GNSS) equipment. UAVs allow for the collection of dry bathymetric data in environmentsthat are difficult to access. Low-cost RTK GNSS equipment facilitate accurate measurement of wet bathymetry whencombined with subaqueous measuring tools such as Acoustic Doppler Current Profilers (ADCPs). Hydraulic models may beconstructed from these data, which in turn can be used for various applications such as water management, forecasting, earlywarning and disaster preparedness by responsible water authorities, and construction of river rating curves. We hypothesize20that the reconstruction of dry terrain with UAV-based photogrammetry combined with RTK GNSS equipment leads to accurategeometries particularly fit for hydraulic understanding and simulation models. This study sought to (1) compare open sourceand commercial photogrammetry packages to verify if water authorities with low resource availability have the option to utiliseopen source packages without significant compromise on accuracy; (2) assess the impact of variations in the number of GroundControl Points (GCPs) and the distribution of the GCP markers on the quality of Digital Elevation Models (DEMs), with a25particular emphasis on characteristics that impact hydraulics; and (3) investigate the impact of using reconstructions based ondifferent GCP numbers on conveyance and hydraulic slope. A novel method which makes use of a simple RTK tie line alongthe water edge measured using a low cost but highly accurate GNSS is presented so as to correct the unwanted effect of lensdistortion (‘doming effect’) and enable the concatenation of geometric data from different sources. Furthermore, we describehow merging of the dry and wet bathymetry can be achieved through gridding based on linear interpolation. We tested our30approach over a section of the Luangwa River in Zambia. Results indicate that the open-source software photogrammetrypackage is capable of producing results that are comparable to commercially available options. We determined that GCPs areessential for vertical accuracy, but also that an increase in the number of GCPs above a limited amount of 5 only moderatelyincreases the accuracy of results, provided the GCPs are well spaced both in horizontal and vertical dimension. Furthermore,1
insignificant differences in hydraulic geometries among the various cross sections are observed, corroborating the fact that a35limited well-spaced set of GCPs is enough to establish a hydraulically sound reconstruction. However, it appeared necessaryto make an additional observation of the hydraulic slope. A slope derived merely from the UAV survey was shown to be proneto considerable errors caused by lens distortion. Combination of the photogrammetry results with the RTK GNSS tie line wasshown to be essential to correct the slope and made the reconstruction suitable for hydraulic model setup.Key Words: Unmanned Aerial Vehicle, Digital Elevation Model, Ground Control Point, Conveyance401IntroductionTraditionally, flow measurements are performed through the use of current meters. A combination of measured depth andvelocities across a profile can be integrated to calculate the total discharge. In order to attain continuous discharge data, riverstage is recorded and plotted against corresponding discharge measurements to produce rating curves (Herschy, 2009; Mosleyand McKerchar, 1993). Ideally, discharge measurements are carried out over a wide range of river stages. The low and medium45river stages are usually relatively easy to record whereas the high river stages are difficult as they are associated with dangerousconditions such as floods and inaccessible terrains. Peaks are also easy to miss, as deployment of personnel and materials takestime. Due to these difficulties, high stage discharge measurement is usually extrapolated from the rating curve. On the otherhand, there is the risk of high variability in low flow measurements as a result of changing bed configurations, particularly insand rivers which change every season. Measurement are usually taken at one particular point frequently despite physical50changes in the profile. These problems lead to high levels of uncertainty in discharge estimates which makes it difficult forwater authorities to understand runoff generation processes especially during high flows when management is mostly required(Petersen-Øverleir et al., 2009). Another limitation is the time validity of the measurements which strongly depends on factorssuch as river bed degradation, river course changes after floods and overspill or ponding in areas adjoining the stream channel(Herschy, 2009; Rantz and Others, 1982). Changes in the geometry of the river due to these factors affect the rating curve55output. Therefore, measurements may cease to be valid across time.Using a hydraulic modelling strategy has become an alternative for discharge estimation (Mansanarez et al., 2019). Physicallybased river rating is based on capturing geometry in a power law expression. The physically based river rating makes use ofthe fact that river flow is a function of river slope, river-bed roughness and channel geometry. In this instance dischargecalculations of flow require information about the geometry of the channel in question (Costa et al., 2000). One of the most60commonly used equations is Manning’s formula which is based on steady and uniform flow (Chow, 1959).The Manning equation can be rewritten as the power law function Eq. (1):2
𝑄 𝑛 1 𝑖(𝐴𝑅2 3 ),(1)where Q is discharge [m3/s], n the Manning’s roughness coefficient, i is the bottom slope [-], A is the cross-sectional area [m2]and R the hydraulic radius [m], [s m-⅓].In this equation; the first part (𝑛 1 𝑖) depends on the bottom slope and channelroughness, the second part (𝐴𝑅652 3 ),depends on the cross-sectional geometry. We refer to A and R collectively as “hydraulicgeometry” and AR2/3 as the "conveyance".The hydraulic geometry is a critical input in the production of rating curves (Zheng et al., 2018). Improvements in technologyhave allowed for a wide range of options for the establishment of geometry. These methods include survey equipment (levels,theodolites, Differential GNSS), Ground Penetrating Radar, sensors mounted on satellites, aeroplanes, kites, unmanned aerialvehicles (UAV), hot air balloons (Feurer et al., 2008; Salamí et al., 2014). In general, manned aircraft which carry cameras are70much more costly than other forms of image data collection (Yang et al., 2006). A low-cost means of collecting geometry isthrough systematic capturing of images from one or multiple cameras mounted on an unmanned aerial vehicle (UAV).Advancements in technologies have resulted in the ability of surveyors to collect very high-resolution geometrical data indifficult to access places (Samboko et al., 2019).The advantages of using UAVs are, (i) the portability of UAVs; ii) the option to self-design and modify integrated sensors;75(iii) the availability of open source and user-friendly data processing software; (iv) the collection of data in difficult to accessterrains and; (v) the relatively low-cost of basic UAVs (Gindraux et al., 2017). UAVs, which operate at low altitudes, have amuch higher spatial resolution than satellites and are not limited in temporal resolution. When used in combination with GroundControl Points (GCPs), UAVs are capable of reconstructing dense and accurate terrains. Satellites with high spatial resolutionusually have long revisit intervals. Only a very limited amount of studies so far have used UAVs to collect data for hydraulic80model purposes.The application of UAV based imagery for dry bathymetry reconstruction is relatively well practiced and documented(Coveney and Roberts, 2017; Gustafsson and Zuna, 2017; Yao et al., 2019). Unfortunately, most low-cost UAVs with RGBsensors are incapable of mapping the geometry under water. Given that many large rivers of interest are perennial, the commonpractice is to use subaqueous measuring tools such as Acoustic Doppler Current Profilers (ADCPs) to determine the ‘wet’85bathymetry of rivers (Vermeyen, 2007; Zedel et al., 2018). Depth profiling has become more affordable with recentlydeveloped low-cost echo sounding devices, which are a viable alternative for typically high cost ADCP devices. This wasrecently shown by Broere et al. (2021) who used a low-cost echo sounder to detect macro-plastics in streams. However, most3
ADCPs or echo sounders are equipped with consumer grade GNSS instruments with 2 meter accuracy. This level of accuracyis unacceptable for accurate hydraulic modelling purposes.90The demand for both accurate and accessible measurements have driven the development of low-cost GNSS instruments(Glabsch et al., 2009; Poluzzi et al., 2019). Recent multi frequency GNSS receivers are affordable, lightweight and are able tofunction in static and dynamic mode. They also act as accurate replacements for the on-board consumer grade GNSSinstruments as they have been proven to be highly accurate and applicable as substitutes for traditional methods (Cina andPiras, 2014). A low cost GNSS chipset (ZED-F9P) was released by U-blox in 2019. In this study we use this chipset on a95breakout board of Ardusimple, type SimpleRTK2B. The set is uniquely capable of receiving corrections from both the L1 andL2 bands (u-blox, 2021). Research conducted using the SimpleRTK2B GNSS set have confirmed its ability to produce resultscomparable to accurate geodetic measurements (Hamza et al., 2020, 2021).Apart from the impact of instrumental (GNSS, ADCP and UAV) inaccuracies on hydraulic geometry, there are more factorsto consider for conveyance calculations. These factors can be divided into three groups; (i) pre-flight (UAV, flight application,100flight path and site selection), (ii) flight settings (camera angle, direction, velocity, altitude, light intensity, wind speed, overlap)and (iii) post-flight processing (photogrammetry software, camera lens distortion, GCP configuration and slope). There havebeen a number successful attempts to review and evaluate best practices for pre-flight and flight settings of UAV acquisitionsystems, orientation and regulation (Abou Chakra et al., 2020; Chaudhry et al., 2020; Seifert et al., 2019; Yao et al., 2019).We proceed by evaluating the four constituents (photogrammetry software, GCP configuration, camera lens distortion and105slope) of post-flight processing which are important for accurate reconstruction of hydraulic geometry.Firstly, the post-flight processing of UAV derived imagery is largely and increasingly facilitated by ‘structure-from-motion’(SfM) photogrammetry software. It offers image processing workflows which are easier to work with than traditionalphotogrammetry techniques. SfM based approaches have been successfully used in various applications such as soil and coastalerosion and lava emplacement (Castillo et al., 2012; James and Robson, 2012; James and Varley, 2012; Smith et al., 2015).110Unfortunately, SfM photogrammetry requires software which is usually available at a cost beyond the reach of most researchersand other interested parties. Some of the more common software packages are (commercial) Pix4D, Agisoft meta-soft and(non-commercial and open-source) OpenDroneMap (ODM). Several researchers have made some comparisons between thecommercially available software (Alidoost and Arefi, 2017; Grussenmeyer and Khalil, 2008; Probst et al., 2018). ODM is anopen-source software which can be used to generate digital elevation models and other photogrammetry results. Not only does115the non-commercial nature of ODM make it more accessible to researchers and practitioners with limited resources, it alsopresents an opportunity to tweak and investigate the impact of individual variables on the output (Burdziakowski, 2017).The second aspect of post-processing which is important for hydraulic geometry is the GCP configuration. Similar to ADCPs,UAVs are equipped with a consumer grade GNSS with an accuracy of 2 meters. This means that all UAV based images and4
outputs of photogrammetry have a maximum error of 2 meters (Udin and Ahmad, 2014). For the purposes of hydraulic120modelling, this inaccuracy is unacceptable, therefore, the application of GCPs is paramount. A number of studies haveinvestigated the number and distribution of GCPs necessary to generate accurate elevation models (Awasthi et al., 2019;Bandini et al., 2020; Ferrer-González et al., 2020; Rock et al., 2011). However, studies have not gone as far as to investigatehow to adjust the number and distribution of GCPs specifically for the purposes of modelling flow in hydrodynamic conditions.For instance, the specific impact on hydraulic geometry of GCP proximity to a flowing river is largely unknown. This particular125information would be handy for water managers who aim to survey the dry and wet bathymetry of a river using low-costtechnologies.The third aspect of post-processing which is important for hydraulic geometry is camera lens distortion. Investigation intocamera lens distortion can be traced as far back as 1919 when A. Conrady developed the decentering distortion method(Conrady, 1919). Based on the decentering model, Brown developed the Brown-Conrady model (Brown, 1971; Clarke and130Fryer, 1998). There have been a number of improvements and modifications to the Brown-Conrady model with respect todifferent applications (Beauchemin and Bajcsy, 2001; Ma et al., 2003; Shah and Aggarwal, 1996). Despite tremendousimprovement in terms of reduced distortion, some DEMs show systematic broad scale deformation which is known as the‘doming effect’ (also known as the ‘bowling effect’) (Javernick et al., 2014; Rosnell and Honkavaara, 2012). The domingeffect emanates from inaccuracies in modelling the radial distortion of camera lens (Fryer, John & Mitchell, 1987). This135fundamental drawback makes it difficult to fully exploit the potential of SfM products in many situations such as gradientsensitive applications, e.g. rainfall runoff and slope estimation. Some guidelines for avoiding the doming effect have beenoutlined (James and Robson, 2014a). A novel method which aimed at correcting the doming effect was presented by Magri(2017), who iteratively applied a planarity constraint through a Bundle adjustment framework. The results were encouragingas they concluded that it was possible to mitigate the doming effect through manipulation of the bundle adjustment process.140Bundle adjustment is a technique for calculating the errors that occur when we transform the XYZ location of a point in theenvironment to a pixel point on a camera image.Documentation from ODM suggests that making use of a configuration called Fixed Camera Parameter (FCP) can help reducethe doming effect (ODM, 2021). The FCP turns off camera optimisation while performing bundle adjustment. This is becausein certain circumstances, particularly when mapping linear (low amplitude, limited features) topographies, bundle adjustment145performs poor estimation of distortion parameters (Griffiths and Burningham, 2019).Finally, in order to estimate flow based on the Manning’s formula (Equation 1), it is important to accurately measure the slopeof the terrain. Similar to hydraulic geometry, there is growing interest in non-contact methods of estimating slope. Commonmethods of slope measurement require accurate point data measured using GNSS and geodetic based methods. It is possibleto extract elevations from photogrammetry outputs and derive slope, however, the accuracy of this method is largely unknown.5
150Ultimately, the factors (photogrammetry software, GCP configuration, lens distortion and slope) which affect hydraulicgeometry can be evaluated in terms of their impact on discharge or flow proxies such as conveyance. A study was conductedby Mazzoleni (2020) on the potential for using UAV derived topography for hydraulic modelling. The study concluded thatthese topographies extracted from UAVs presented results comparable to LIDAR and RTK GNSS-based topographies.However, it did not accurately measure the permanently wetted bathymetry of the river. Rather, the study mechanically filtered155out the river which brought about some uncertainty. A similar study which investigated the impact of the number of GCPs onflood risk model performance concluded that UAVs could successfully be used for data collection as long as a minimumnumber of control points were utilised (Coveney and Roberts, 2017). Nevertheless, the study was located in a large city andthus, did not include the measurement of inundated areas, nor did it focus on the ability to reconstruct typical hydraulicproperties.160The practical utility of accurate hydraulic geometry for flow estimation is unquestionable (Gleason, 2015). However, thereexists minimal research on how the factors which affect the accuracy of geometry can be adjusted to improve the quality ofelevation models in hydrodynamic environments and when applied for the ultimate purposes of discharge estimation.Furthermore, earlier contributions have not put the focus on the ability to reproduce hydraulic geometry characteristics andhave not focused on the entire bathymetry (including the permanently wet river bed section). Hence, this paper investigates if165low-cost methods for data collection and processing, i.e. a combination of precise wet bathymetry points with UAVphotogrammetry, can be used to provide satisfactory quality elevation models for hydraulic models, quantified in hydraulicgeometry characteristics. In this paper, a novel and practical method of correcting the doming effect using data collected usinga low cost GNSS, mounted on a mobile cart is applied. We tested the methods on the Luangwa River in Zambia.This paper is organised as follows: section 2 describes the methodology and gives a brief outline of what materials were used170in the study. In Section 2.1 describe the study area (Luangwa Basin). Furthermore, the methodology section outlines how flowestimation was determined and software packages were compared. Furthermore, Section 3 presents results and a discussion ofthe results. We conclude with section 4 which presents a conclusion and recommendation for future studies.We investigate the following research questions and determine whether the said factors have a significant effect on the accuracyof results. These are:1751. Can the freely available (Open Source) ODM software package produce results that are comparable to commercial packagessuch as Agisoft Metashape?2. What is the optimal GCP number and GCP distribution necessary to reconstruct accurate elevation models?3. What impact do elevation models, reconstructed based on different GCP numbers have on hydraulically simulatedconveyance and hydraulic slope?6
1802Materials and MethodsThis section first describes the data collection procedures, including flight plan, collection of ground control points, dry andwet bathymetry. Then it describes which experiments are conducted to investigate our research questions.2.1185Study siteThe study was conducted along the Luangwa River, South of the Luangwa Bridge. The Basin has a catchment area ofapproximately 160,000 km2. The Luangwa River originates in the Mafinga Hills in the North-Eastern part of Zambia and isapproximately 850 km in length, flowing in South-Western direction. The river drains into the Zambezi River, shaping a broadvalley along its course. The river has naturally created a valley, which is well-known for its abundant wildlife and relativelypristine surroundings (WARMA, 2016). The study area is shown on Figure 1.190Figure 1 Study area map A: Zambia, B: Luangwa River, C: Mapped AreaThe data collection was conducted in the late stages of the dry season (December, 2019) to maximise the visible floodplain.To optimise on access and data collection efficiency, the reach which was chosen is relatively straight with low sinuosity. The195wet river channel is however meandering within the floodplain. The channel is also wandering and braiding, especially duringlow flows. The longitudinal section has a gentle gradient (approximately, 1:5000), which is difficult to identify without the7
use of accurate survey instruments. At high water, the river cross section is approximately 400 meters in width, with amaximum depth of approximately 8 meters. At the time of data collection (in the dry season) the flowing water had a maximumdepth of 2 meters. The channel substrates are alluvial, comprised of sand. Erosion, siltation and sedimentation are therefore200highly prevalent occurrences. It is not unusual to see the river channel in a different location after every wet season or after aheavy storm event due to high morphological activity.2.2Data acquisitionThe data acquisition basically consists of two parts, data collection and data-processing. The data collection includes measuringground control points, measuring the river bathymetry and collection of UAV images.2052.2.1Low cost GNSS equipmentIn 2019, U-blox launched the ZED-F9P chip capable of receiving satellite signals in the lower and upper bands (L1 and L2)from the BeiDou, Galileo, GLONASS and GPS constellations. The ZED-F9P chip was integrated with an ArduinosimpleRTK2B board which can function in RTK mode, produced by Ardusimple. The board can transmit or receive Radio210Technical Commission for Maritime (RTCM) corrections and can be configured by the user using u-center, a freely availableopen source software (u-blox, 2021). The simpleRTK2B set is low-cost (receiver 172 Euro and patch antenna 50 Euros at thetime of writing) with the possibility to acquire 1cm level precision with base-rover and 1cm level precision with RTCMcorrections. The exact accuracy depends on multiple factors including the used antenna, the satellite reception quality andamount, the accuracy of the base station surveyed location, and the baseline distance. Long-Range radio antennas were used215to communicate RTCM messages. Figure 2 (a) shows the SimpleRTK2B Base and Rover which was used to measure markerpoints. Figure 2 (b) shows the simpleRTK2B setup on site. At the initial stages of configuration, the board was connected toa laptop which also provided power supply and data storage through a USB port. Upon realisation that a laptop would not beable to supply power for a prolonged period of time in harsh fieldwork conditions, we replaced it with two 20 000mAh powerbanks and a Raspberry Pi. The time between starting the base station and actually beginning to take measurements using the220rover has an impact on accuracy i.e. an extended time period results in better results because the base is able to survey itslocation more precisely over time.8
Figure 2 (a) RTK GNSS Set (b) RTK GNSS Base Station Setup along the Luangwa River Floodplain2252.2.2Flight PlanGCPs were recorded using RTK GNSS equipment on a 1 km long floodplain. Flights were conducted at two different heights(90m and 100m) at a constant speed of 10 m/s, a 100 camera angle used to optimise on 3D reconstruction results. The twoflight patterns were separated by 20 degrees from each other so as to limit the effects of image lens distortion. The side andforward image overlap was set to 80%. Figure 3 shows the flight paths of the two patterns which were flown. The UAV used230is a DJI Phantom 4 Advance with a 12 Megapixel FC330 RGB camera with a focal length of 3.61 mm. A flight planningandroid application called Pix4D Capture was used to control the autonomous flights. This application was chosen due to its9
capability to tilt the camera forward during the image capturing process, important to capture more depth information thanwhen using a nadir-looking configuration. The coordinate system was set to WGS 84 / UTM zone 36S (EPSG::32736).235Figure 3 Flight paths flown at two different heights (90m and 100m) at a 20 degree angle to each other.2.2.3Dry river bathymetryIn order to refine the camera calibration parameters and to optimise the geometry of the output, GCPs have to be used. Thedry bathymetry data collection can be divided into two procedures; placing the GCPs on the ground and collecting the images.A total of 17 GCP markers were placed on the floodplain, with some being closer to the road, others more in the middle of the240dry floodplain and the last closer to the water line. Figure 4 shows the location of the GCPs in relation to the floodplain. TheGCPs were placed on one side of the floodplain because the other side was steep and covered with dense vegetation. An effortwas made to make sure all elevation variations were covered by the placement of GCPs. This was achieved through a basicGNSS based inspection of the terrain; the difference between the highest point on the terrain and the lowest was calculatedand divided into seventeen elevation levels. Taking the elevation levels into consideration, the 17 GCP markers were245strategically distributed within each level in a 2-1-2 formation as practically as possible. The markers were 40 cm by 40 cm indimension and had an alternating black/white colour.10
Figure 4 Spatial distribution of 17 GCPs on Floodplain250Different GCP numbers and combinations were tested for two different experiments. The first experiment with the objectiveof determining if open source software could perform as well as commercial software used 3 GCP numbers in a 2-1-2formation. The 2-1-2 formation is sometimes known as the ‘checkerboard’ method, it is a relatively common method ofdistributing marker points on a terrain. The GCP numbers used in this experiment were 5, 9, and 13. The second experimentwith the objective to determine the impact of the number and distribution of GCPs used 5 GCP numbers and 2 different255formations. The GCP numbers used in this experiment were 0, 5, 9, 13 and 17 GCPs. In the instance where zero GCPs wereused, we adjusted the calibration setting to FCP (see introduction section 1) to establish if this would improve results insituations when no GCPs are available. Both the 2-1-2 and the linear biased formations are used in the second experiment. Thephrase we refer to as ‘linear biased’ distribution is a method of marker distribution whereby the markers are placed in arelatively straight line on one side of terrain. In our case the markers are either closer to the river or furthest away from the260river (see section 2.3.2).2.2.4Wet River bathymetryThe Luangwa River, similar to other large tributary rivers of the Zambezi, is perennial meaning the bathymetry of the riverneeds to be measured under flow conditions. The wet river bathymetry was recorded using a combination of an ADCP andRTK GNSS. The GNSS of the ADCP was not used in favour of the RTK GNSS for improved accuracy. The RTK GNSS was265mounted directly onto the ADCP sonar beam, whilst the ADCP was attached to a canoe rowed by local fishermen, as shownin Figure 5(b). The ADCP and the RTK GNSS were configured to take measurements at one second intervals. The canoe11
moved from one side to the other in a zigzag manner and tried as much as possible to reach the edges to both sides. The GNSScrossed the river 21 times and a total of 3102 measurements were recorded. The program suitable for the particular ADCP,Winriver II, was used for real-time data collection. For the purposes of interpolation, the canoe was manoeuvred along both270sides of the river. The river was however shallow, especially on the right bank, this means that it was not possible for the canoeto adequately move close to the water line. To capture the slope, the RTK GNSS was mounted on a wooden cart and towedmanually along the waterline. An image of the cart is shown in Figure 5(a). The waterline tie line was subsequently used asthe true value reference to enable establishment of the level of deviation of the ODM and Agisoft values.275Figure 5 (a) Low Cost RTK GNSS Rover mounted on a mobile cart for recording RTK water line 5 (b) ADCP combined with anRTK GNSS Rover attached to a fisherman’s canoe measuring the wet bathymetry.2.2.5Processing the Dry and Wet BathymetryImages taken by the UAV were collected and fed into the ODM and Agisoft software. The images were processed locally on280a Dell Core i7 8th generation machine with 32 Gigabytes of RAM. These computer specifications meet the requirements andfit the description of a ‘Basic Configuration’ (Agisoft, 2021). The same settings were applied in the processing steps as far aswas permissible. Figure 6 outlines the steps which were taken in the production of the point cloud and DEM. The first stageshown on Figure 6 is fieldwork. As with all other images, aerial photographs are optically distorted. In order to correct thesedistortions, geometric corrections had to be made. These distortions are caused by the camera optics, the topographical relief285and the tilt of the camera (Verhoeven et al., 2013). One of the most effective ways to correct distortions is to m
1 Evaluating low cost topographic surveys for computations of conveyance. Hubert T. Samboko1, Sten Schurer1, Hubert H.G. Savenije1, Hodson Makurira2, Kawawa Banda3, Hessel Winsemius1, 4, 5 5 1Department of Water Resources, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, Netherlands 2Department of Construction and Civil Engineering .
2.1) for all surveys so that profiles obtained from all survey types (topographic, bathymetric and LiDAR) can be compared. The spreadsheet also details which profiles are to be surveyed for each survey type e.g. topographic baseline survey, topographic interim profile survey, topographic post-storm profile survey, topographic repeat baseline .
The authors distinguish topographic image maps and thematic image maps. 3. TOPOGRAPHIC IMAGE MAPS . The term “topographic image map” is defined analogically as with the term topographic map. Topographic maps with their topographic and hypsometric content are one of the most import
What is a Topographic Map? Mapping is a crucial part of earth science. Topographic maps represent the locations of major geological features. Topographic maps use a special type of line, called a contour line, to show different elevations on a map. Contour lines are drawn on a topographic
U.S. Geological Survey (USGS) Historical Topographic Map Collection (HTMC). The HTMC is a digital archive of about 190,000 printed topographic quadrangle maps published by the USGS from the inception of the topographic mapping program in 1884 until the last paper topographic map using lithographic printing technology was published in 2006. The
Access Forms & Surveys The Forms & Surveys Workspace is where you create and manage forms and surveys. Each Site, Subsite, Channel and Section Workspace has a Forms & Surveys Workspace. You can access Forms & Surveys two different ways, through the Tools tab and by clicking Forms & Surveys in Common Tools. Access the Site Workspace Forms & Surveys
topographic maps to determine where roads, tunnels, and bridges should go. Land use planners and architects use topographic maps when planning development projects, such as housing projects, shopping malls, and roads. Bathymetric Maps Oceanographers use a type of topographic map that s
Topographic and geologic maps Lab experiment Reading Topographic Maps Purpose: to learn the most important parts of a topographic map, how to read the map, and how to interpret and calculate data using the map. Note: Maps used will also be available in digital format online. A. Identify
weekend, your pet will be kept at the airport due to customs duty hours. If possible pets should arrive during weekday/daytime hours to prevent unnecessary stress for the pet or owner. Commercial Airline Transport . If flying commercially, contact the airline prior to purchasing tickets to ensure pets will actually be able to fly on the day of travel (e.g. ask about the airline’s regulations .
