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Article

Geospatial Technologies Used in the Management of Water Resources in West of Romania

1
Faculty of Agriculture, University of Life Sciences “King Mihai I” from Timisoara, 119, Calea Aradului, 300645 Timisoara, Romania
2
Faculty of Civil Engineering, Politehnica University Timisoara, Piaţa Victoriei Nr. 2, 300006 Timisoara, Romania
3
Central and Eastern Europe, Leica Geosystem Part of Hexagon, Marina stny. No. 1, 1138 Budapest, Hungary
*
Authors to whom correspondence should be addressed.
Water 2022, 14(22), 3729; https://doi.org/10.3390/w14223729
Received: 20 October 2022 / Revised: 13 November 2022 / Accepted: 14 November 2022 / Published: 17 November 2022
(This article belongs to the Section Water Resources Management, Policy and Governance)

Abstract

:
Stability in time of major and important objectives is vital and can be achieved by 3D scanners which follow changes in time with construction, respective of the natural or artificial hydrotechnical dams and the obtaining of 3D data in real time with the possibility of evaluating and making quick decisions. This scientific paper approaches a research topic of great importance and actuality in the field of Civil Engineering, Hydrotechnics, and Geomatics using the 3D scanning technologies for the hydrotechnical arrangements (Topolovăţu Mic, Coșteiu and Sânmartinu Maghiar) and hydroameliorative (Cruceni Pumping Station). In Romania, data collection was carried out for the first time using the mobile scanning technology (MMS), “Backpack” type, namely, Leica Pegasus Backpack. Data collection using terrestrial laser scanning technology (Terrestrial Laser Scanning) was carried out with the Leica C10 equipment. The processing of point clouds was carried out using the Inertial Explorer program, and the processing of point clouds was carried out with the Cyclone program. The collection of ground checkpoints used for checking, correcting, and analyzing point clouds was carried out using the GPS Leica GS08 equipment. Compared with traditional methods using classical measuring instruments, precise data was obtained (with an error of 2–4 cm) through 3D laser scanning technology in a short time and with multiple possibilities of processing and visualizing point clouds.
Keywords:
LiDAR; GNSS; MMS; TLS; ZUPT; 3D; point cloud

1. Introduction

The evolution of technologies in the last half of the twenty-first century has led to huge progress in electronics, IT, and graphics (digital 3D models), making possible the development of laser scanning technology, both terrestrial and aerial. Thus, the possibility of processing the dense clouds of points in an efficient and cost-effective way facilitated a multitude of applications regarding the acquisition of 3D data in areas such as topography, environmental protection [1,2], the completion and conservation of cultural heritage, forest resources, etc. [3,4]. The first 3D scanning technology was created in the 1960s.
In modern engineering [5], the term “laser scanning” is used to describe two meanings closely related to each other but still separated. The first sense, more generally, is the controlled deviation of laser beams, visible or invisible [6]. The second direction, more specifically, is the controlled direction of the laser beams followed by the measurement of the distance in each direction indicator. This method, often called 3D scanning of an object or 3D laser scanning, is used to quickly capture shapes of objects, buildings, and landscapes.
The 3D laser scanning market, including hardware, software, and services, is quite dynamic, with major segments facing rapid product innovation [7]. Laser scanning systems have gone through a major evolution over the past decade. After the initial discovery of Airborne Laser Scanners (ALS), other types of laser scanning systems [8] appeared, namely terrestrial laser scanners (TLS) and Mobile Mapping Systems (MMS).
Many applications require 3D object data, and examples can be found in civil and industrial engineering, construction [9] and road maintenance, urban planning, environmental analysis [2], and precision forestry. LiDAR (Light Detection and Ranging) was widely used over the years to scan objects or map the environment in 3D to support these applications.
The types of ALS, TLS, and MMS systems each have their own applications, advantages, and disadvantages. A TLS may be suitable for the field, but it may be inappropriate due to the limited number of points and the requirement of excessive redundancy of data for the successful recording of the scan. Therefore, the growing need for modelling and monitoring of objects challenges the industry to develop new 3D data collection tools which are even more complete, accurate, and efficient [10].
Terrestrial Laser Scanning (TLS) uses the same principles as ALS, by allowing datasets to be collected far above this traditional topography [11].
The laser scanner can be described as a fully motorized station which automatically measures all points in its horizontal and vertical field [12].
Laser scanning and its synonyms LADAR (Laser Distance and Ranging) or LiDAR have become a major technology for the three-dimensional geometric capture of complex structure data [13,14]. Unlike classic photogrammetry, which requires a minimum of two images and provides image coordinates that must be scaled and transformed to generate 3D information, laser scanning directly generates a metric 3D information. Each point of this point cloud has three-dimensional coordinates [15] and has accuracy up to a few millimeters. In addition, the intensities of the received signals provide valuable indicators for post-processing.
Currently, LiDAR technology is used in more and more fields [16], and information about LiDAR technology dates back long before the discovery of the laser. For example, in 1930, the first attempt to measure the density of air in the upper part of the atmosphere appears. In 1960, the year of the discovery of the laser, we proceeded to the development of modern LiDAR systems, an evolution that is constantly growing.
The use of IMU for navigation has been largely limited to aviation and the maritime industry due to costs and sizes [17]. In recent decades, the IMU has become less expensive with the introduction of a micro-machined electromechanical technologies MEMS (Micro-machined electromechanical Systems), which allows many consumer products, such as mobile phones, cameras, and game consoles, to include an IMU [17]. Recent progress in computer processing has allowed image processing algorithms to be disseminated in real-time, even on consumer products, and together with the help of less expensive IMU, vision-assisted inertia navigation has become very important in the research field [18,19].
IMU measures acceleration and angular velocity, and measurements can be integrated over time to achieve a position and orientation.
The noise inherent in IMU measurements are also included in the integration and will cause position and orientation estimates to distance from the true value. An IMU can be taken with a very high sampling frequency, in which rapid movements are felt very well. An image does not represent a single moment of time but the scene in time. Images on a camera must be processed through an image processing algorithm to obtain a position. Image processing takes time and limits the frequency at which the position can be obtained from a camera.
The purpose of this paper is to carry out a scientific research using modern techniques and methods of scanning and measuring heritage objectives (the hydrotechnical nodes from Topolovăţu Mic and Coșteiu, the lock from Sânmartinu Maghiar as well as the Cruceni pumping station) with modern technologies in order to preserve in time these objectives of national and international interest, thus offering Romania and Serbia, through the IPA program, the ability to carry out the project of “rehabilitation of the infrastructure of the Bega Navigable Canal”, which has a value of 13.878 million euros (VAT included). The partners of this project are: Banat Water Basin Administration (project leader-Romania), Timis County Council and, from Serbia, the Provincial Secretariat for Interregional Cooperation and VDP Vode Vojvodine.

2. Materials and Methods

2.1. Methodology

The research explores the many representations and digital tools of the field to create digital environments using Leica 1200 and Leica GS08 GPS equipment, terrestrial laser (TLS), Leica ScanStation C10, Dorna (UAV) Phantom 4 Pro, and an ultramodern LiDAR mobile MMS (Mobile Mapping System) piece of scanning equipment, the Pegasus Backpack model, which offers unique mobile mapping solutions for fast reality capture and combines five cameras providing a 360-degree and two-degree panoramic view sensors which are based on LiDAR technology, Velodyne type (VLP16) [20], for measuring the laser distance, managing to determine 600 thousand points per second, at a maximum distance of 60–70 m left-right (300 thousand points/s for SA—vertical scanning and 300 thousand points/s for HS-horizontal scanning) (Figure 1).
In the current research, the Leica Pegasus Backpack scan was chosen because the differences between this scan (MMS) [21] and the GNSS measurements are about 2–3 cm at an absolute level, and the relative results are even better (in the order of millimeters, 13–20 mm). With such equipment, in addition to speed and accuracy in obtaining 3D data, one may also discuss a cost saving of almost 50%. The advantage of 3D scanning is that, at the time of scanning, the entire area will be captured, so that any forgotten detail may be obtained later, without the need for another exit into the field, for completion. Thus, UAV and MMS data can be combined with existing research technologies such as TPS, GPS and TLS, providing a more complex set of information.
By using SLAM (Simultaneous Localization and Mapping) technology, ARTK (Advance Real Time Kinematic) technology to solve ambiguities (minimum 5 satellites from the same constellation, optimal would be 7 or more), and high-precision IMU (Inertial Measurament Unit), the Leica Pegasus Backpack equipment ensures precise positioning together with GNSS technology [22] incorporated into the equipment and of Kalman filter. The final results are 3D point clouds (3D Point Cloud), orthophotoplane, a digital surface model (DSM) and a digital elevation model (DEM), 3D model, 3D polygons and polylines, GIS data [23], as well as 3D/2D situation plans that can also be made and overlapped on clouds of points collected from the field.

2.1.1. GNSS Technology

The GNSS campaign was carried out for all the locations taken in the study, but for the current paper, only the data for the Topolovăţu Mic hydrotechnical node will be presented. Rinex data recording was carried out using double frequency receivers Leica 1200 and Leica GS08 and GS08 plus.
The 1200 series and the GS08 series were used either as a reference station or as a Rover for both static measurements and kinematic measurements (RTK).
In the paper, for Rinex data recording, the static method has been used, and the data acquisition was carried out at 5 s by activation of the Long Raw Observation function. The measuring engine used is SmartTrack type which acquires satellites in seconds. The antennas used are of double frequency type: AX1202GG and ATX1230GG with SmartTrack, supporting GLONASS, GPS, and GALILEO signals. The raw data obtained was exported from the Leica 1200 GPS control using the Leica Geo Office Combined (RAW DATA) program. The reference system was WGS 84 (World Geodetic System).
For GS08 receivers, Rinnex data downloading was performed directly from the controller to USB. In order to achieve the post-processing of the data obtained from the field (by stationing with the GPS equipment on the concrete terminals) the Rinnex data was also acquired from the Permanent Stations, every 5 s, and together with the data acquired in the field, the post-processing and obtaining the WGS 1984 coordinates of the stationed terminals, have been carried out.
The transformation of the raw data (RAW DATA) from the ETRS89 system into the 1970 Stereographic projection system (which represents the official cartographic projection of Romania) was carried out within the paper with the TransDatRO program and, after that, passed to report the points in AutoCAD through the TopoLT program and their comparison with GNSS RTK values obtained from field readings.

2.1.2. UAV Technology

For the acquisition of aerial data [24], during the course of the research carried out, a Phantom 4 Pro Drone was used.
In the present work, the flight was carried out at a relative altitude of 90 m, over four parallel strips running in the North-South direction and two boarding lanes (Figure 2). The flight data was uploaded to the UAV using a wired connection, through an Iphone SE phone, which allows receiving and sending information on position, orientation, height, and speed during the flight.

2.1.3. TLS Technology

In order to restore some patrimony monuments, two Hydrotechnical Nodes (Coşteiu and Topolovăţu Mic, Timiș County, Romania), two locks (Sânmihaiu Român and Sânmartinu Maghiar, Timiş County, Romania) and a Pumping Station in Cruceni, Timiş County were used; knowing the geometry of the object represents the most important part.
A complex analysis of constructions can usually be done by making a simple drawing, or a spatial representation of the object, which is based on a limited number of shapes, as well as lines, points, or polygons.
In this study, a TLS laser scanner from Leica, the C10 model, was used, which has an acquisition range from 0.6 m up to 300 m, and which also has the possibility to overtake images through an integrated camera, making 260 RAW images from each station.
Between 2017–2019, a total of eight scans were performed to cover the studied surface, namely: 2 TLS scans at NH Coșteiu; 3 TLS scans at Topolovăţu Mic; 1 TLS scan at NH Sânmihaiu Român; 1 TLS scan at Sânmartinu Maghiar, and 1 TLS scan at Cruceni Pumping Station, with the possibility to choose the size of the GRID, the acquisition speed being 50,000 points per second.
The final coordinates resulted from the post-processing of the raw data and the acquisition of RINNEX data from the GNSS permanent stations.
The stationary time for each scan was between 25–75 min, in the case of scans performed within the current scientific paper, at a GRID of 2 cm of scanning. During this time of stationary, the Leica C10 scanner (TLS) used for scanning analyzes the maximum distance set for scanning and, depending on the size of the GRID, the stationary time will be determined.

2.1.4. MMS Technology

Leica Pegasus Backpack [20] is a platform with reality capture sensors, with a highly ergonomic design, combining five cameras that offer a fully calibrated 360-degree view and two LiDAR sensors (VLP-16) with an ultra-light carbon fiber chassis, allows obtaining LiDAR points, both inside (PURE SLAM method) and inside or outside (FUSED SLAM method), at a level of precision considered to be authoritative and professional (Figure 3).

LiDAR Data Acquisition Using MMS

For the acquisition of MMS data, the Hydrotechnical Node from Coșteiu and the two Locks have been scanned: Sânmihaiu Român and Sânmartinu Maghiar, as well as a scan at the Cruceni Pumps in the county Timis, the FUSED SLAM method being used for all the locations (Figure 4).
  • Planning the LiDAR data acquisition mission: preparing the street lanes for the missions performed outside or the route on which it will have to be followed or, in case of inside scanning, viewing the interior and creating space for turns and walks, opening and fixing the doors. Planning for the three objectives was done using the Google Earth [25] outdoor program.
  • Initialization of the equipment: in order to obtain data with high accuracy, the initialization has been done near the scanned objectives, in open air area, not covered by obstacles (trees, buildings). Initialisation consists of two steps (Figure 5):
    • STATIC initialization, for a period of 5 min, during which the backpack will stand still for 15–20 min.
    • min in case the ALMANAC will have to be updated, Almanach update needed when travelling > 200 km from last project site.
    • DYNAMIC initialization, with a duration of 3–5 min, during which the backpack is worn on the back, making a quadrilateral walking with it in the back, and, at the end, a walk on the diagonals of the quadrilateral until the INS GOOD message appears, and the INS_GNSS will have a value below 1; usually, the indicated value is 0.1–0.2.

Placing the Master GNSS Station and Collection of GCP Control Points

For the acquisition of LiDAR [26] data for the objectives pursued in this paper, the FUSED SLAM method was used, a method to which, in addition to the scanning backpack itself, we also need the placement of a Master GNSS station for field acquisition and Rinex data, an acquisition that was set to an interval of 1 s for the post-processing of the measurement trajectory as opposed to the static measurements for the thickening of the geodetic network that was set to a Rinex data acquisition at 5 s.
It is mandatory to have GNSS data at 1Hz or higher from the maximum location distance of the Master station, which must not exceed a maximum of 15 km from the position of the Leica Pegasus backpack. It may also be used the GNSS reference stations located at the Romania Offices of Cadastre and Real Estate Advertising, for a fee (3 EUROS/hour/reference station). In carrying out the post-processing for this work, a Master station was used, and the Leica GS 08 plus model was used, which was mounted on a tripod for each of the measurements in order to record the RINNEX data (Figure 6).
A more precise transformation was achieved with the Leica Infinity program. But the best solution was to record RTK data directly in the 1984 WGS system without turning it directly into the field or office (Table 1).
For the correct collection of Rinex data, the Master GNSS station (for all scanned objectives) was started 10 min before the initialization of the backpack (Leica Pegasus Backpack) and was turned off 10 min later after completing the collection of LiDAR data. For each of these scans, GCP checkpoints (Table 2) were determined directly in the field on the ground with the GPS equipment from Leica GS 08, and points that were used to check and realign the clouds of LiDAR points were collected with the Leica Pegasus Backpack equipment. The checkpoints were also imported into the Pegasus Manager to verify the quality of the recorded data and to verify the trajectory of the scanned objectives (Figure 7).

2.2. Description of the Hydrotechnical and Hydro-Ameliorative Arrangements under Study

From an administrative point of view, Banat is located in the Western Development Region of Romania, occupying entirely the administrative districts of Timiș county (47.46%), Caras-Severin (46.47%), and smaller parts of the counties: Arad (2.89%), Gorj (0.90%) and Mehedinti (2.29%). This hydrographic area covers an area of 18,272 km2 (7.7% of the total area of Romania) and consists of several hydrographic basins, whose main watercourses have a cross-border character. These basins are represented by the Bega-Timiș-Caraş hydrographic basin [27], the Nera-Cerna hydrographic basin, the Danube hydrographic basin between Baziaș and the interfluve with the Cerna river, and the Aranca hydrographic basin [28].
The hydrographic network [28,29,30] from the Banat hydrographic area has a length of 6297 km and consists of 10 watercourses (Aranca, Bega Veche, Bega, Timiș, Bârzava, Moravița, Caraș, Nera, Dunărea and Cerna) and their tributaries crossing the state border with Serbia and the Danube tributaries between Baziaș and the Cerna River (Table 3).
The Bega River [27], the second largest river (after the Timiş River) in the analyzed area, has an area of 2362 km2, a length of 170 km to the Romanian border and a hydrographic network density of 0.38 km/km2 [31]. In the eighteenth century, this river was channeled on the lower course, at 114 km, between the localities of Timișoara (Romania) and Titel (Serbia), being the first navigable canal in Romania and, by its completion, Timisoara became the first Romanian city with a navigable canal and an operational port.

2.2.1. Topolovăţu Mic Hydrotechnical Node

The Topolovăţu Mic hydrotechnical node is part of the hydrotechnical planning: “The Double Timiș-Bega Connection”, which was conceived by the Dutch engineer Maximilian Emmanuel de Fremaut and built in 1758 [27,32]. In order to defend the municipality of Timisoara from high waters, the dam from Topolovăţ controls, on Beghei, a maximum flow of 40 m3/s, and the extra flow is discharged in Timiș through a channel of 5.5 km long. Also, in order to ensure the level of navigation on the Bega Canal through the dam from Coșteiu, the Bega Canal from the Timiș River is fed through a channel of 10 km with the necessary flow [27,29].
The Timis—Bega interconnection is an anthropogenic [33] and natural system of watercourses. The spillway threshold (dam) at Coșteiu is located downstream of Lugoj. The spillway threshold was built in the eighteenth century to divert the water from Timis to the Bega through the 10 km long Timiș-Bega supply Canal to ensure the water supply of the Bega Canal and implicitly of the city of Timisoara.
In the over 200 years of existence, the arrangement has suffered various damages and restorations, and in 1998 the entire hydrotechnical node was damaged as a result of the floods, appearing a series of infiltrations, erosions, and breaches in the body of the dam.
The Topolovăţu Hydrotechnical Node has a great importance for the Banat plain area because it supplies water with almost constant level to the Bega Canal, thus decreasing the probability that the Timisoara municipality will be affected by drought, being favorable to navigation on the Bega Canal, which can be carried out all year round, and the floods on this river can be controlled; thus, the localities downstream of the dam are protected to some extent from unpredictable floods [27].

2.2.2. Coșteiu Hydrotechnical Node

It aims to make connections between the Timiş River and the Bega Canal since 1758. The hydrotechnical node was built in 1758 with the purpose of directing the waters of the Timiș River into the Bega Canal in times of drought for the latter to have a constant flow. This regulates the flow of the Bega Canal, which ensures the necessary drinking water (for a part of Timisoara) as well as the flow required for navigation. At the same time, the hydrotechnical node from Coșteiu performs the function of defense against floods.
It is a hydrotechnical derivation system located at the branching of the Timiș-Bega discharge channel. From the administrative point of view, N.H. Coşteiu is located in Timiș County, Coşteiu Commune.
The Timiş–Bega interconnection is a double connection consisting of two channels connecting the Timiş River and the Bega River [33,34,35] via a discharge channel (Figure 8).
The Coșteiu Hydrotechnical Node is one of the five hydrotechnical nodes administered by the Banat Water Directorate [36]. It is the only hydrotechnical system located on the Timiș River, the other four hydrotechnical nodes being built on the Bega.
The dam has a canopy length of 130 m and a height of 10.5 m. The Timiș dam is a spillway dam, with a wide threshold of rocks in wooden houses filled with raw stone. In 1998 the entire hydrotechnical node was damaged by floods [37].
In terms of importance, the Coșteiu Hydrotechnical Node has a particularly high importance for the Banat plain area. First of all, the hydrotechnical works in Coștei supply the Bega Canal with water, thus decreasing the probability that Timisoara municipality to be affected by drought. At the same time, the supplementation of the water flow from Timiș to the Bega is also favorable for navigation on the Bega Canal, which can take place throughout the year. Through the spillway dam built on The Timis, the floods on this river can be controlled so that the localities downstream of the dam are protected to some extent from unpredicted floods. The fact that the dam provides a constant level of water upstream facilitates the water supply to the municipality of Lugoj. Last but not least, when the flows of the Timiș River register particularly high values, a part of the waters can be directed on the Bega Canal, so that the risk of flooding the localities upstream of Coșteiu decreases [38,39,40].
The flood protection system of Timiș County is composed of the Coșteiu hydrotechnical node and the draining system composed of 91 pumping stations [41,42], which sums up 325 aggregates, which are necessary to drain the water from precipitation. The evolution of precipitation is an essential factor influencing the functioning of the flood protection system [43,44].

2.2.3. Sânmartinu Maghiar Hydrotechnical Node

Its history begins in 1728 during the time of governor Florimund Mercy, who ordered the digging of a canal to help drain the swamps from the area. Up to the canal that we know today, there were numerous works, made by the Austrian administration, and then by the Hungarian one. It became for many centuries the main channel for the transportation of goods. In 1752, the quantity of goods transported on the Bega was 20,000 tons. In 1912, 415,000 tons of cargo were loaded into the Port of Timisoara and about 200,000 tons were unloaded.
Between 1937–1938, the volume of goods transported reaches a maximum of 250,000 tons per year. However, few people know what the first transportation ship came with on the Bega, which circulated in 1732, more than four years after the start of the works. Florimund Mercy nurtured great plans for the transformation of the region so that it would bring as much benefit as possible to the Habsburg court. Immediately after the liberation of Banat from the Ottoman yoke, canals were dug for the draining of the swamps around the city. He separated the basin of Beghei from that of Timis. From a tributary of the Timiş River, the Bega became a river in its own right. At that time, in the stone-free areas of the lowlands, no roads could be built, and the dirt ones were impassable for many months of the year. The only possibility of transportation was by water. In order to supply Timisoara, then to export the province’s products, the navigable canal was built downstream of Timisoara. “The first boat loaded with bacon and salted meat arrived from Pančevo to Timisoara in November 1732,” wrote Arpad Jancso in the volume “The Bega, the pampered river of Banat”.
In the period 1753–1755, it was decided to dig, in parallel with the route of Count Mercy, a few hundred meters to the south, a new navigable canal. Between 1799–1809 the Hungarian Royal Directorate, through a very large financial effort, again regularized the Bega canal. In 1780, construction of five dams with five locks began. In 1899, the project for the construction of six hydrotechnical complexes was submitted, in Ecica, Clec, Itebe (today in Serbia), Sânmartinu Maghiar and Sânmihaiu Român. Thus, the two hydrotechnical nodes (Sânmihaiu Român and Sânmartinu Maghiar) were built on the Bega canal, meant to maintain the controlled levels necessary for the navigation of the canals, to ensure the water levels for the consumers who take water from the canal as well as for other uses in the municipality of Timisoara (recreation, evacuation of the sewerage systems, etc.). In the Bega hydrographic basin, the rivers are monitored at 10 hydrometric stations [27,34] located on the Bega River (Luncani, Făget, Balinț, Chizătău, Topolovăţu Mic and Remetea) and other important rivers such as Sasa (Poieni), Chizdia (Ghizela), Gladna (Fârdea Gladna) and Hăuzeasca (Fârdea Hăuzeasca) [30]. The most important watercourses in Banat (Timiș and Bega) constitute a common hydrographic system in the lower section called the Timiș-Bega hydrographical system and, due to the existing hydrotechnical installations, was designed to facilitate a better management of the water in their area. It refers primarily to the double connection between the two rivers (Chizătău-Coștei Canal and Topolovăţ-Hitiaș Canal), which transfer excess water from one river to another, according to needs [45].

2.2.4. Cruceni Pumping Station

The civilian flood defence system has the major purpose of protecting urban and rural settlements, avoiding loss of life, and reducing material damage. Floods are the natural phenomenon that has marked and deeply marks the development of human society [46,47].
Statistics show us that in the period 2000–2009, floods had the highest frequency of all types of natural disasters. Out of an average of 387 natural disasters occurring during that aforementioned period, 173 (45%) were floods. In 2010, out of a total of 373 natural disasters, 182 (49%) floods were recorded. All this data is an additional argument in favour of preparations to deal with such events. Among the factors that favor the occurrence of floods we may mention climate change, soil compaction, and land consolidation that go hand in hand with intensive agriculture.
The floods in June-July 2010 affected 481 localities in 37 counties, with 20,000 people being recorded who were evacuated from their homes and about 4000 damaged households, of which 863 were completely destroyed. The damage caused by the floods in the summer of 2010 amounts to EUR 875 million according to assessments by the authorities. On 15 April 2005, Timiș County experienced the worst floods in the modern history of Banat. Following the rains and the melting of the ice, the Timis River exited the major riverbed causing damage of 150 million euros, according to estimates made by the authorities. The waters of the Timiș, Caraș and Bârzava rivers overflowed, resulting in the evacuation of 2800 people, the destruction of 5300 households, the damage of 4900 houses, of which 654 were completely destroyed, and the damage of 128,000 ha of land, 617 bridges, 300 km of roads (27.3 km of national roads, 92 km of county roads, 107 km of communal roads).

3. Results and Discussions

The purpose of this paper is conservation of the hydrotechnical heritage objectives using a different type of measurement tool through terrestrial 3D laser scans (the hydrotechnical nodes from Topolovăţu Mic [48] and Coșteiu, the lock from Sânmartinu Maghiar as well as the Cruceni pumping station) with the latest technologies.
The combined use of this equipment has been studied.

3.1. The Use of GNSS Technology to Thicken the Geodetic Network through Satellite Measurements for GCP Checkpoints

To be able to perform the measurements using the terrestrial laser scanner (TLS) and to be able to determine the coordinates of the ground control points GCP (Ground Control Point) used for checking and processing the SLAM for the mobile MMS laser scanning, a geodetic network was performed, using GNSS technology, static method [48,49].
The comparison of the data was done between the GNSS RTK values and the static GNSS values obtained from post-processing. The transcalculation of coordinates from the ETRS’89 reference system to the 1970 Stereographic system was performed with the TransDat 4.01 software produced by A.N.C.P.I. (Agency for Cadastre and Real Estate Advertising). The permanent GNSS stations from which the Rinex data was operated at 5s were Timisoara, Moldova Noua, and Arad (Romania) (Table 4). By performing static measurements, the coordinates of the points of the thickening and lifting networks were obtained by determinations relative to the National Geodetic Network GNSS (RGN—GNSS) consisting of permanent GNSS stations (Class A) and stocking terminals (Class B, respectively Class C). It was mandatory to take into account the existence of visibility between the points of the lifting network.

3.1.1. Acquisition and Processing of Rinex Data

a. Preparatory phase
Preparatory stage—at this stage it is aimed at obtaining as much data as possible, comparing them to start the field stages. Thus, we recall the collection of data and the establishment of the working method and the necessary equipment.
Planning GPS measurements—in Figure 9a is presented GDOP for Topolovăţu Mic and in Figure 9b is presented the constellation and trajectory of satellites for Topolovăţu Mic.
Elevation of satellites
Another important element that was taken into account during the measurements was the elevation of the satellites. Even if for measurements it can be programmed that the receiver also records data from satellites below 15 degrees, it is necessary that when entering data in the program Leica Geo Office Combined to eliminate those that induce errors. Also, it was studied with the Almanac to see where it can be noticed if the PDO values met the conditions. We will present in Figure 10 the elevation of the satellites.
Stationary time on each concrete milestone.
In the case of static measurements made within the radius of Topolovăţu Mic Territory (Romania), the stationary time was 1.5 h for each measurement session (milestone). This means T = 60 min + 15 min = 75 min stationary time/milestone.
For an additional verification on the Feno type terminals that were located at the Topolovăţu Mic hydrotechnical node, determinations were also made in the kinematic mode (RTK-Real Time Kinematic) having a length of the session of about 600 s for each point. The WGS 1984 coordinates obtained from RTK readings were transformed directly into the field due to the implementation of the TransDatRO 4.06 [50] system directly in the controller.
b. Field stage
In the first phase, the recognition of the land was carried out, before planting the landmarks on the ground and in the second phase the actual measurements were made. Also in this phase, the field sheets were completed.

3.1.2. Postprocessing Data and Presentation of Obtained Results

At this stage, all the data files downloaded from the receivers into the software have been uploaded, the field sheets are carefully tracked. In the case of the paper, the filling in with the data referring to the height of the instrument has been carefully observed.
The software was used for data processing, a program that allows data processing and network clearing at the same time. The data files were imported in RINEX format-universally recognized format (Figure 11).
Table 5 presents the data entered in the processing software. In Table 6 and Table 7, we can see the fulfillment or non-fulfilment of ambiguities as well as the accuracies obtained.
After processing the data, we proceeded to adjust the network with the Leica Geo Office Combined program and to compare the data obtained, those by Static methods with those obtained by RTK method (Table 8, Table 9, Table 10, Table 11, Table 12 and Table 13).

3.2. Data Acquisition and Processing Using UAV Technology in the Studied Facilities

3.2.1. Photogrammetric Data Processing

In terms of photogrammetric data analysis, software programs use different approaches, both with commercial and open-source solutions [51].
In order to carry out the research and to be able to make a comparison of the data, several flight sessions have been carried out for the heritage objectives researched within the present work, where only 2 flight sessions will be presented (due to the large volume of data), made for the Topolovăţu Mic Hydrotechnical Node. Import of photos: it is the first step that will be done in order to carry out the processing of photogrammetric data. Regarding the two sessions, 50 aerial images were collected in the first session and in the second one, 287 images. Aligning images with PhotoScan Professional: the algorithm looks for common points on the images and matches them by finding the camera position for each image, estimating the camera calibration parameters. The program then calculates the positions of the camera and generates the first point cloud (Figure 12a,b).

3.2.2. Developing the Digital Elevation Model

PhotoScan [52] allows to perform measurements of point, distance, area, volume based on DEM, as well as the generation of cross-sections for a part of the selected scene by the user. In addition, contour lines can be calculated for the 3D model and are represented either over DEM images or over Orthomosaic.
To generate the digital elevation model (DEM), it is necessary to first classify dense cloud points to divide them into at least two classes: ground points and the rest. For this, the base value for the class parameter should first be selected in the Build DEM dialogue to generate the DEM.
The resolution value shows an actual ground resolution for the DEM, estimated for the source data. The resulting DEM size, calculated in relation to the ground resolution, is shown in the total size text box.
Session I-a was made up of 80 million points (80,371,748 points) obtained with high resolution (Ultra high quality), and the 3D model consisted of 16 million faces (16,054,425 faces). The whole processing took about 12 h. The resolution of the DEM was 3.46 cm/pix. (14,875 × 144,414).
Session II consisted of 32 million points (32,119,012 points) obtained with high resolution (High quality), and the 3D model consisted of 6 million faces (6,458,745 faces). The whole processing took about 5 h. The DEM resolution in this case was 8.28 cm/pix. (8196 × 5180).
One may notice that although the first session had only 50 images of which 48 images remained for photogrammetric processing, the processing time was more than double, resulting in an accuracy of 3.46 cm/pixel, while session II had 5 times more images, namely 287, of which 273 images remained for processing; the quality of the orthophotoplane obtained is much lower, which can be seen from the resolution per pixel which is 8.28 cm, almost 3 times lower (Figure 13).
Triangular photogrammetry can be used to generate an orthophoto image and a DSM. These products were generated, in our case with a resolution of 0.05 m.
The dense clouds of 3D points for NH Coșteiu, based on UAV images, consisted of 43 million points (42,517,472 points) obtained with high resolution (Ultra high quality), and the 3D model consisted of 2 million faces (2,834,498 faces). The whole processing took about 8 h. The resolution of the DEM was 6.99 cm/pix (9778 × 8718).

3.2.3. Orthomosaic and Orthophotoplane Production

Orthomosaic export is normally used to generate high-resolution images based on aerial photographs and the reconstituted model. The most common application is the processing of aerial photographic data, but it can also be useful when a detailed view of the object is required. PhotoScan [53] allows performing orthotomosaic editing in a straight line for better visual results.
PhotoScan [53] generates orthomosaics for the entire area, where surface data is available. The limitations of the limitation boxes were not applied. To build an orthophotoplan, for a specific (rectangular) part of the research, the Region section of the Build Orthomosaic dialogue (Figure 14a,b) was used.
A comparative analysis of the resulting georeferencing data was also made by comparing two orthophotoplans: one from 2015, made by the ANCPI and the the second one conducted in the research in 2018. Figure 15a,b shows the georeference orthophotoplan for HN Topolovăţu Mic.

3.3. GIS Data Acquisition and Processing Using TLS Technology

The digital preservation of cultural heritage monuments is a 3D modeling challenge. Objects and cultural heritage sites differ greatly from one to the other [54], and a fidelity of 3D modeling is an essential condition. Terrestrial laser scanning is a 3D technology that, lately, has become increasingly known, especially for the possibility that it offers the benefit of making a documentation that offers very dense 3D points on a surface with high accuracy. Therefore, the resulting 3D models can be used to produce a digital documentation, as well as the possibility of performing analysis, such as: measurements, monitoring, preservations, detail extractions, and virtual restoration [55].
UAV-LiDAR will therefore allow to ask new questions on large-scale hydrological connectivity in complex landscapes with an important relevance to the WRM.
Each scan was performed on the topographical principle of orientation of the device using the method of known coordinates, coordinates marked in the field by FENO type terminals, whose coordinates were determined by satellite measurements using the STATIC method of determining the position and recording of RINNEX data at an interval of 5 s [56].
The final 3D model was obtained by integrating point clouds (Point Cloud) from aerial scans (UVS) with point clouds resulting from terrestrial ones (TLS), but only after the UAV point clouds were georeferenced and defined in a single frame of unique reference: Stereographic 1970 and Black Sea 1975, respectively.

3.3.1. TLS Data Acquisition

The results obtained by merging the point clouds required the use of a PC computer (high-performance graphics station, due to the large volume of data) Intel Core i7, Windows 10, 16 GB of RAM, resulting in a processing time of about 2–3 h, with the final result being a 3D points cloud.
The location for performing scans and obtaining point clouds is located in five places in Timiș County (NH Coşteiu, NH Topolovăţu Mic, NH Sânmihaiu Român, NH Sânmartinu Maghiar and Cruceni pumping station) where, for the measurements, a high-precision 3D laser scanner from Leica was used, the ScanStation Leica C10 model, which records the geometry of the surfaces and structures, thus rendering the digital image of the scanned lens and the laser data was processed using version 9.3.1. of the Cyclone program.
To complete the digital model for the upper part of the heritage objectives we have used the UAV technique, namely a Phantom4 Pro drone, which was correlated with the TLS (Terrestrial Laser Scanner) technique through specialised programs.
The current scientific paper consists of producing and processing the sets of measurements made in the field with the ScanStation Laica C10 scanner for the locations under study.
Performing measurements using the Leica ScanStation C10 scanner can be presented in four stages, namely: planning measurements, scanning itself, downloading data from the device, and processing the data.

3.3.2. Data Processing and Obtaining 3D Point Clouds

The processing of measurements was carried out with the program Cyclone 9.3 version 9.3.1. A first step is to import the raw data into the Cyclone program. After the introduction of the scans, the Cyclone program includes relative alignment of the setups to each other, which can be done manually or automatically. The alignment of the scans made, from different stations to a single common station, can be done either with the option ‘Smart Align’ (direct station alignment technique) or by a ‘Manual Registering’ (indirect station alignment technique) called georeference, and at the end we can also perform a pre-visualization of the scans, the recorded constraints and errors.
The recording of the resulting point clouds, coming from different stations, consists of entering the scans named ‘ScanWorld’ in one single ‘Registration’ record, adding some automatic constraints of cloud points type, “Control Spaces”.
The position of each scanning station is defined by the coordinates entered in the scanner. In order to be able to achieve the alignment of the scanning positions it is necessary to know the position and the orientation, according to a coordinate system (Table 14, Table 15 and Table 16). After viewing the recorded errors in each station, one can proceed further to the realization of the 3D model of the scanned lenses and the visualization of the 3D models (Figure 16, Figure 17, Figure 18 and Figure 19), with the possibility of cleaning the 3D model (such as 3D points recording from the station at the time of contact of the lens with the sun, passing people, cars, etc.).
With the help of this program, the alignment of the clouds of points obtained from the UAV flights for the completion of the upper parts were also achieved (for example, the roof), by identifying as many as possible common points in point clouds resulting from terrestrial scanning with the ScanStation Leica C10 scanner.

3.3.3. Making the Virtual Tour (TruView)

At the end of the work, the data were exported in different formats required by the architect: * .E57; * .ptx; * .x,y,z. For the presentation and visualization of historical monuments, a directory link was created, which can be directly accessed, and trough which, a virtual heritage tour can be made, TruViewSetup32-309.exe.
The TruView site map is a panoramic image (360 degree) of data from cloud points, the view being located in the same location as the scanner that captured the point clouds. The point of view in TruView is made exactly from the location where the scanner was placed and where the scanning of each lens took place. With the help of this program, you can achieve increases and decreases around the stage while extracting the dimensions from one point to another and extracting coordinates, but it will not be possible to fly around the stage as in some 3D systems.
After creating the TruView link, it was possible to move from one location of the scanner to another in order to have different points of view within the project. Each point of view has a separate ScanWorld. The site map contains image icons located in the center of each available TruView scene, where with a single click on an icon, which is triangle type, we will have another point of view from another station. TruView may be launched directly from Internet Explorer.
The data processing, obtaining the TruView link, as well as the data processing report and the errors recorded when performing the scans for the Coșteiu Hydrotechnical Node are shown in Figure 20; for the Topolovăţu Mic Hydrotechnical Node, they are shown in Figure 21; for the Sânmihaiu Român Lock, they are shown in Figure 22; for the Sanmartinu Maghiar Lock, they are shown in Figure 23; and for the Cruceni Pumping Station, they are shown in Figure 24.

3.4. GIS Data Acquisition and Processing Using Mobile LiDAR Technology (MMS)

3.4.1. Pre-Processing of LiDAR Data and Data Visualization in QC Tools

Viewing the processing of the LiDAR scan trajectory, the correctness of the data collection and the visualization of the measurement quality (QC Tools) will be done in the Inertial Explorer program (Figure 25), using the graphs presented below (Figure 26, Figure 27, Figure 28 and Figure 29).
Within each group, the plots appear in alphabetical order in three colors: green, blue, and black. Green plots are generally the most frequently accessed plots, blue parcels less, and black parcels are rarely accessed, except for advanced users.

3.4.2. Data Processing in Pegasus Manager and Obtaining Point Clouds

The processing of LiDAR data (Figure 30), collected with the help of Leica Pegasus Backpack equipment, is carried out with the help of the Pegasus Manager program, and includes several steps https://www.mdpi.com/2073-4441/12/10/2895 (accessed on 1 October 2022).
In Figure 31a–d one may notice the intensity of the nuance of the LiDAR data, the coloring per height and RGB coloring for H NCoșteiu, which was performed on 15 May 2019.
Figure 32a–d also show the LiDAR point clouds according to the intensity, the coloring according to the Walks (i.e., how many subprojects were made within a scanning project) and the RGB visualization of the point clouds, where each LiDAR point is colored according to the color of the corresponding pixel from the images taken during the scan for the Cruceni Pumping Station, a scan performed on 3 June 2019. On 23 July 2019, the scanning of the Lock from Sânmartinu Maghiar was also performed, and this is represented in the work by Figure 33a–d.

3.4.3. LiDAR Data Processing in ArcGIS—Leica Pegasus: MapFactory

The results obtained from data processing will be further processed with the GIS program using the MapFactory module which is an ArcGIS module of work used from data collection to final extraction.
MapFactory programs for GIS, MapFactory for AutoCAD, and Cyclone with CloudWorx module and 3D modeling module (Cyclone model) allow viewing images and clouds of LiDAR points (Figure 34), to analyze, collect, and export LiDAR data. After the objects have been measured and meta-labeled, the data can be exported in different formats, including AutoCAD.
Also the clouds of LiDAR points obtained from the scanning of the Coșteiu Hydrotechnical Node, of the Sânmihaiu Român and Sânmartinu Maghiar Locks, as well as of the Cruceni Pumping Station, using the MMS scanning technology with the Leica Pegasus Backpack in the Pegasus Manager program, they were exported to a LiDAR file of type LAS and E57 and later on imported into the Cyclone Model program from Leica where, through CloudWorx mode for AutoCAD Map and the 3D modeling mode used, allows viewing the digital reality in CAD and achieving the 3D situation plan of the hydrotechnical objectives. This CloudWorx module uses the Cyclone program as a working platform and as a basis for viewing point clouds. Thus, LiDAR points can be viewed, analyzed, vectorized, and exported to CAD.
It automatically recognizes the change in the road level. The deformation is classified according to the colored height—thus, a quick recognition of the extent of the deformation is allowed (Figure 35).

3.5. Comparison of TLS—MMS Data

If in the case of engineering photogrammetry the point clouds are obtained from the aerial images collected with the drone (Phantom 4 Pro), in the case of MMS mobile scanning technology, where we used a Leica Pegasus backpack, point clouds are obtained from the two Velodyne scanning systems [20], each provided with 16 LiDAR data retrieval lasers (LiDAR SO + LiDAR SA), with an amazing accuracy of a few millimeters, and the images obtained from the 5 cameras will be used to color the LiDAR points, from the raw shape, which have flashy colors of red, yellow, and green in a real form, where each LiDAR point will color based on the pixels in the images, in fraction of a second.
The same happened with the use of TLS terrestrial scanning technology, where a Leica ScanStation C10 equipment was used to scan hydrotechnical objectives, with the following clear differences (Table 17).

3.6. Comparing UAV-TLS-MMS Data

In the case of UAV equipment, due to the fact that the data is recorded from a height, they face difficulties in obtaining information about objects from the ground, for example under trees and along the facades of the buildings. On the contrary, MMS collects high-precision point clouds from the ground along with stereographic images but lacks information about the top. For example, a roof. In case of errors, due to the lack of the GNSS signal or when it is weak, it will be intervened to correct it by compensating the time, meaning that it will be compensated the time during which the Leica Pegasus backpack was left without a good signal to the GNSS, or not at all, through the SLAM function and the Kalman filter.
In the table below you can see some parameters compared between DJI Phantom 4 Pro GNSS RTK [57], Leica ScanStation C10 and Leica Pegasus Backpack (Table 18).
This paper focuses on the integration of UAV images and LiDAR point clouds-MMS and TLS to build high-resolution 3D models for hydrotechnical objectives and realization a data visualization link using the Leica TruView program, thus creating a digital world.
Table 19 shows comparative data made between several mobile scanning equipment, equipment that was the subject of the research, before purchasing the equipment from Leica, the Pegasus Backpack model.
In the Table 20 can see similarities and differences in the way of data collection, accuracy of cameras, batteries, weight, etc.

4. Conclusions

This scientific paper reports the current state of research in the field of UAV, TLS, and MMS for geomatic and hydrological applications, providing an overview of different platforms, applications, and case studies with the help of a Drone Phantom 4 Pro. This paper also presents the latest developments in UAV image processing. At the same time, the technique of using UAV platforms is correlated with the TLS (Terrestrial Laser Scanners) technique, using a Leica ScanStation C10 scanner together with the mobile scanning technique (MMS—Mobile Mapping System), the Leica Pegasus Backpack, for the 3D scanning of hydrotechnical and hydro-ameliorative lenses [58].
Through this paper, scientific research was carried out, which was necessary to create a database through terrestrial (fixed and mobile) and aerial 3D laser scans of the objectives taken in the study (the hydrotechnical nodes from Topolovăţu Mic and Coșteiu, Sânmartinu Maghiar, as well as the Cruceni pumping station).
The georeference error in the case of using the control points (GCP) and checking them in AutoCAD was between 2–4 cm for the hydrotechnical objectives studied, namely NH Topolovăţu Mic, NH Coşteiu, HN Sânmihaiu Român, Sânmartinu Maghiar Lock and Cruceni Pumping Station.
By comparing with the traditional methods using classical tools, data collection is difficult or even impossible to be acquired but using the laser scanning method we will obtain accurate investigation data in a short time and with multiple possibilities of processing and viewing point clouds.
By using the Cyclone Model program used for data processing, the topographical elements of the land could be easily extracted in order to make a numerical model of the land, process the facades of the constructions, realize the situation plans, and make the transverse and longitudinal profiles; in the end, we obtained information on the X, Y, Z values of the points, as well as the realization of a virtual flight on the scanned objective or of a virtual tour where we also had information about the clouds of points, distances and also the creation at the end of a ‘link’ that can be transmitted further to the interested units and the import of this data on the website so that it can be accessed by anyone.
Due to continuous developments in both scanning and navigation technologies, the landscape of mobile mapping systems varies and is evolving rapidly. Consequently, we have produced a comparative table that offers a review of the main current commercial systems available on the market (Table 21).
The RADMIN program helped to achieve the calibration of the images, the realization of the project and also provided important data during the scans regarding, the scanning speed (m/s), the INS value, the number of satellites at the time of scanning.
All this presented data finally led to a scan with high precision, resulting at the end of data processing and application of the Kalman filter into a cloud of points with an accuracy of 1–3 cm, accuracy which was verified with the help of the resulting stereographic images, where the positioning accuracy of the point clouds were improved when used ground control points (GCP). These ground control points (GCP) had the role of verification and control as well as in the processing of point clouds.
As part of the research carried out for the elaboration of this scientific paper, LAS data type was imported and processed using Cyclone, Global Mapper [59], 3D Resheaper and CloudCompare [60] programs and the file with the E57 extension, thanks to the new version from Cyclone Model, version 9.3, processed data could be imported directly into Cyclone Model due to the new option of ‘Import data from Leica Pegasus’.
Estimated errors for the scanned lenses in this work are characterized by an average dispersion of ± 5.7 cm (MMS vs. UAV) and ± 1.6–2 cm (MMS vs. TLS) [61].
The combinations for the Kalman Filter for guidance, navigation and control decreased from 55 to just 6 combinations, thus reducing the post-processing time [62].
With the help of modern 3D scanners, it is possible to successfully track in time the constructions, respectively of the natural or artificial hydrotechnical dams and to obtain 3D data in real time with the possibility of quick evaluation and decision-making, where the most accurate data and with the smallest errors were obtained with the 3D scanning equipment, the Leica C10 model comparing to the Leica Pegasus Backpack equipment.
UAV technology can be used mainly for monitoring slopes, landslides, soil erosion monitoring or farm monitoring.
In conclusion, the measurements made with the ScanStation Leica C10 equipment, require longer time with stages similar to the use of a total station, but with amazing results, with high accuracies of the order of millimeters, 0–6 mm, while the use of the Leica Pegasus Backpack backpack requires a very short time to perform scans, which also involves the installation of a reference for RiNNEX data collection, which can be performed, by walking or from a car, with a maximum speed of 20 km/h, where the obtained accuracies are of the order of centimeters, 1–3 cm, resulting in a volume large of raw data, where after carrying out the processing and obtaining the clouds of points these final data will be 5–6 times higher.
The Bega Canal was arranged 250 years ago, and from a hydrotechnical point of view, it is a building with special arrangements, which presents a significant importance from a historical and architectural point of view. The Timișoara—Klek waterway has a total length of 114.50 km, of which on the Romanian territory about 44.5 km and the Serbian territory of 70 km.
The navigable sector of the Bega Canal on the territory of the Republic of Serbia, is connected with the Danube-Tisza-Danube hydroameliorative and navigation system. The Bega Canal is one of the significant waterways for Zrenjanin, Zitiste and Timișoara; its relaunch in terms of navigation would mean the connection of Banat, both to the Black Sea and to the North Sea, the existing system of canals from Serbia DTD (Danube—Tisza—Danube) and the Rhine-Main–Danube Channel.
By repairing the Sânmihaiu Român, Srpski Itebej and Klek hydrotechnical hubs, the connection of the bicycle route on the Romanian side with the one on the Serbian sector will be made, thus also making a connection between Timisoara and Zrenjanin along the Bega Canal and defending much wider opportunities for commercial and economic cooperation. Such a project will result in the creation and development of transport links on both sides of the border, as well as the promotion of good neighbourliness and cooperation between Romania and Serbia.
Currently, these activities are in progress, and in August 2021 the Bega became navigable. Following the implementation and completion of the project, a new border crossing point with Serbia will also be arranged through the point/place where the Bega leaves the country. The steps have already been taken, and through the canal system in Serbia, Timisoara will be again connected to the Danube and then to the North Sea [63].
Supplementing the results obtained with the help of modern 3D scanning technologies with photogrammetric results are necessary where these technologies complement each other and are not excluded.
The data presented in this scientific paper are original and were made in the period 2017–2020 and constitutes the future for obtaining data in a short time and of good quality through the possibility of comparing the results and by using the ground control points (GCP) on a mandatory basis.

Author Contributions

Contributed to the study and writing of this research, A.Ș., L.Ș., C.A.P., S.H., T.E.M., F.I., A.H., S.M., T.S. and R.P.; data acquisition and data processing, A.Ș. and S.M.; conceptualization and drew the main conclusions, L.Ș.; analysed the data, T.E.M., S.H., C.A.P., F.I., A.H. and T.S.; writing—reviewing and editing, R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University of Life Sciences “King Mihai I” from Timisoara, Romania.

Acknowledgments

This paper is published from the own research funds of the University of Life Sciences “King Mihai I” from Timisoara, Romania.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Leica Pegasus equipment (https://leica-geosystems.com/ (accessed on 12 December 2021).
Figure 1. Leica Pegasus equipment (https://leica-geosystems.com/ (accessed on 12 December 2021).
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Figure 2. Planning the flight in the study area, with the indication of the flight bands followed by the UAV.
Figure 2. Planning the flight in the study area, with the indication of the flight bands followed by the UAV.
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Figure 3. Mission type.
Figure 3. Mission type.
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Figure 4. Fused SLAM Acquisition.
Figure 4. Fused SLAM Acquisition.
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Figure 5. Initialization of the MMS equipment.
Figure 5. Initialization of the MMS equipment.
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Figure 6. Rinex data collection at one second ( Pumping Station, Cruceni, Romania).
Figure 6. Rinex data collection at one second ( Pumping Station, Cruceni, Romania).
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Figure 7. Presentation of the checkpoints and trajectory for the scanned objectives.
Figure 7. Presentation of the checkpoints and trajectory for the scanned objectives.
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Figure 8. Timiș-Bega interconnection—synoptic scheme (A.B.A. Banat).
Figure 8. Timiș-Bega interconnection—synoptic scheme (A.B.A. Banat).
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Figure 9. Planning measurements: (a) Chart of GDOP Values Topolovăţu Mic; (b) Constellation and trajectory of the satellites for Topolovăţu Mic.
Figure 9. Planning measurements: (a) Chart of GDOP Values Topolovăţu Mic; (b) Constellation and trajectory of the satellites for Topolovăţu Mic.
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Figure 10. Height of satellites.
Figure 10. Height of satellites.
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Figure 11. Presentation of errors for the measurements performed.
Figure 11. Presentation of errors for the measurements performed.
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Figure 12. Alignment of images: (a) session I-a; (b) session II-a.
Figure 12. Alignment of images: (a) session I-a; (b) session II-a.
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Figure 13. DSM generated by photogrammetric technique.
Figure 13. DSM generated by photogrammetric technique.
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Figure 14. Orthophotoplan obtained by aerial data processing, Topolovăţu Mic, Timiș, Romania.
Figure 14. Orthophotoplan obtained by aerial data processing, Topolovăţu Mic, Timiș, Romania.
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Figure 15. Orthophotoplan obtained by processing aerial data: (a) Ortophotoplan ANCPI—2015 (Topolovăţu Mic, Timiș, Romania); (b) Ortopotoplan UAV—2018 (Topolovăţu Mic Hydrotechnical Node, Timiș, Romania).
Figure 15. Orthophotoplan obtained by processing aerial data: (a) Ortophotoplan ANCPI—2015 (Topolovăţu Mic, Timiș, Romania); (b) Ortopotoplan UAV—2018 (Topolovăţu Mic Hydrotechnical Node, Timiș, Romania).
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Figure 16. Point Cloud-NH Coșteiu.
Figure 16. Point Cloud-NH Coșteiu.
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Figure 17. Point Cloud-Sânmihaiu Român Lock.
Figure 17. Point Cloud-Sânmihaiu Român Lock.
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Figure 18. Point Cloud-Sânmartinu Maghiar Lock.
Figure 18. Point Cloud-Sânmartinu Maghiar Lock.
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Figure 19. Point Cloud-Cruceni Pumping Station.
Figure 19. Point Cloud-Cruceni Pumping Station.
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Figure 20. TruView link for NH Coșteiu.
Figure 20. TruView link for NH Coșteiu.
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Figure 21. TruView link for NH Topolovăţu Mic.
Figure 21. TruView link for NH Topolovăţu Mic.
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Figure 22. TruView link for the Lock from Sânmihaiu Român.
Figure 22. TruView link for the Lock from Sânmihaiu Român.
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Figure 23. TruView link for the Lock from Sânmartinu Maghiar.
Figure 23. TruView link for the Lock from Sânmartinu Maghiar.
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Figure 24. TruView link for Cruceni Pumping Station.
Figure 24. TruView link for Cruceni Pumping Station.
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Figure 25. View option in Inertial Explorer (IE).
Figure 25. View option in Inertial Explorer (IE).
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Figure 26. 3D scanning of the HN Coșteiu—Museum lens on: 13.05.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) PDOP values.
Figure 26. 3D scanning of the HN Coșteiu—Museum lens on: 13.05.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) PDOP values.
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Figure 27. 3D scanning of the objective NH Costeiu–Dam on 15.05.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) Estimation of data accuracy (X, Y, Z and Time).
Figure 27. 3D scanning of the objective NH Costeiu–Dam on 15.05.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) Estimation of data accuracy (X, Y, Z and Time).
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Figure 28. 3D scanning of the objective CRUCENI PUMPING STATION on 03.06.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) Estimation of data accuracy (X, Y, Z and Time).
Figure 28. 3D scanning of the objective CRUCENI PUMPING STATION on 03.06.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) Estimation of data accuracy (X, Y, Z and Time).
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Figure 29. 3D scanning of the objective HN SÂNMARTINU MAGHIAR on 23.07.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) PDOP values.
Figure 29. 3D scanning of the objective HN SÂNMARTINU MAGHIAR on 23.07.2019: (a) View of scan accuracy (Combined separation); (b) The number of visible satellites at the time of the scan; (c) RMS values; (d) PDOP values.
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Figure 30. Fused SLAM Processing Workflow.
Figure 30. Fused SLAM Processing Workflow.
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Figure 31. Coșteiu Hydrotechnical Node, Timiș, Romania, scan performed on 15 May 2019: (a) The intensity of the LiDAR data nuance, the orthographic visualization; (b) The nuance intensity of the LiDAR data, 3D visualization; (c) LiDAR data coloured by altitude; (d) View point clouds (RGB).
Figure 31. Coșteiu Hydrotechnical Node, Timiș, Romania, scan performed on 15 May 2019: (a) The intensity of the LiDAR data nuance, the orthographic visualization; (b) The nuance intensity of the LiDAR data, 3D visualization; (c) LiDAR data coloured by altitude; (d) View point clouds (RGB).
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Figure 32. Cruceni pumping station, Timiș, Romania, scanning performed on 03 June 2019: (a) Intensity of the nuance of LiDAR data, orthographic visualization; (b) The nuance intensity of the LiDAR data, 3D visualization; (c) Coloring of point clouds after walks; (d) View point clouds (RGB).
Figure 32. Cruceni pumping station, Timiș, Romania, scanning performed on 03 June 2019: (a) Intensity of the nuance of LiDAR data, orthographic visualization; (b) The nuance intensity of the LiDAR data, 3D visualization; (c) Coloring of point clouds after walks; (d) View point clouds (RGB).
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Figure 33. Sânmartinu Maghiar Lock, Timiș, Romania, scan performed on 23 July 2019: (a) The nuance intensity of the LiDAR data, orthographic visualization; (b) View point clouds (RGB); (c) LiDAR data coloured by altitude; (d) Coloring of point clouds after walks.
Figure 33. Sânmartinu Maghiar Lock, Timiș, Romania, scan performed on 23 July 2019: (a) The nuance intensity of the LiDAR data, orthographic visualization; (b) View point clouds (RGB); (c) LiDAR data coloured by altitude; (d) Coloring of point clouds after walks.
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Figure 34. View the LiDAR points on the stereographic image.
Figure 34. View the LiDAR points on the stereographic image.
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Figure 35. Visualization of LiDAR data in the GIS environment, Cruceni pumping station.
Figure 35. Visualization of LiDAR data in the GIS environment, Cruceni pumping station.
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Table 1. Master station coordinates for targets scanned in the WGS 1984 system.
Table 1. Master station coordinates for targets scanned in the WGS 1984 system.
No. Crt.Master StationTypeB [m]L [m]He [m]
1H NCoşteiuFENO Terminal45°44′10.90829″ N21°51′07.31158″ E157.481
2Cruceni pumpsFENO Terminal45°27′05.562528″ N20°53′15.953888″ E120.995
3HN Sânmartinu MaghiarMetal bolt45°39′26.63270″ N20°55′28.31293″ E126.516
Table 2. GCP ground checkpoints collected in the field.
Table 2. GCP ground checkpoints collected in the field.
ObjectiveNameX (m)Y (m)Z (m)
HN CoşteiuMaster_SR1475,565.920255,206.307114.399
1RTK475,558.675255,259.001114.607
2RTK475,591.581255,275.722116.604
3RTK475,610.997255,258.163116.986
4RTK475,596.074255,240.448116.822
4RTK475,620.283255,221.991116.501
HN Sânmihaiu RomânMaster_SR1475,209.986196,699.31084.695
GPS1475,180.393196,683.44386.942
GPS2475,178.302196,678.64986.940
GPS12475,267.737196,623.46186.615
GPS13475,266.853196,621.21086.610
GPS14475,213.282196,711.08684.727
GPS20475,193.586196,719.97984.712
Cruceni pumping stationMaster_SR1447,352.270178,587.62177.771
GPS1447,343.822178,583.79977.941
GPS2447,343.710178,593.62577.576
GPS3447,330.955178,596.60677.627
GPS4447,349.065178,611.74577.509
H NSânmartinu MaghiarMaster_SM1470,063.192182,627.37483.394
GPSM1470,066.158182,631.73983.382
GPSM2470,100.617182,696.31583.356
GPSM13470,078.430182,624.03583.391
Table 3. Hydrographic network in the Banat hydrographic area (http://www.rowater.ro (accessed on 14 May 2021).
Table 3. Hydrographic network in the Banat hydrographic area (http://www.rowater.ro (accessed on 14 May 2021).
Hydrographic BasinBasin Area (km2)Length of the Network (km)Main Course Length (km)
Aranca1080328114
Old Bega2108527107
Bega2362892170
Timiş56731907244
Bârzava1202355127
Moraviţa38717247
Caraş128050279
Nera1380574124
Tributaries of the Danube
(between Buziaş and Cerna)1091530123
Cerna136052479
Table 4. GNSS permanent stations used in post-processing. Ellipsoidal coordinates—ETRS89.
Table 4. GNSS permanent stations used in post-processing. Ellipsoidal coordinates—ETRS89.
Name of Standing StationClassB [m]L [m]He [m]
Arad (ARAD)A46°10′23.51004″ N21°20′ 40.51052″ E167.6742 m
Moldova NouaA45°51′16.42753″ N22°10′ 37.78289″ E216.4898 m
Timişoara 1 (TIM1)A45°46′47.65271″ N21°13′ 51.46281″ E154.7278 m
Table 5. Raw data entered in LGO, Topolovăţu Mic with corrected stations.
Table 5. Raw data entered in LGO, Topolovăţu Mic with corrected stations.
No.PctPoint ClassDuration of MeasurementGNSSType of
Measurement
Measuring Height
TIM1Control9h 59′55″GPS/GLONASSStatic0,0000
MOLDControl9h 59′55″GPS/GLONASSStatic0,0000
FAGEControl9h 59′55″GPS/GLONASSStatic0,0000
TMIC2Measured2h 18′20″GPS/GLONASSStatic2,0000
TMIC1Measured1h 52′49″GPS/GLONASSStatic2,0000
TMIC3Measured1h 55′55″GPS/GLONASSStatic2,0000
TMIC8Measured1h 23′30″GPS/GLONASSStatic2,0000
TMIC9Measured1h 23′15″GPS/GLONASSStatic2,0000
Table 6. Fulfillment or non-fulfilment of ambiguities, Topolovăţu Mic, Timiș County.
Table 6. Fulfillment or non-fulfilment of ambiguities, Topolovăţu Mic, Timiș County.
No.pctEra
10/13/2017/hour
Status of
Ambiguities
Type of MeasurementSolutionEastNorth
TMIC211:07:32notStaticFloat45°45′43.93254″ N21°38′08.85145″ E
TMIC211:07:32yesStaticfixed all45°45′43.93126″ N21°38′08.84424″ E
TMIC211:07:32yesStaticfixed all45°45′43.93198″ N21°38′08.84525″ E
TMIC113:32:07notStaticFloat45°45′42.05516″ N21°38′08.62780″ E
TMIC113:32:07yesStaticfixed all45°45′42.05499″ N21°38′08.62696″ E
TMIC113:32:07yesStaticfixed all45°45′42.05534″ N21°38′08.62661″ E
TMIC313:37:27notStaticFloat45°45′42.22913″ N21°38′12.53778″ E
TMIC313:37:27yesStaticfixed all45°45′42.22745″ N21°38′12.53795″ E
TMIC313:37:27yesStaticfixed all45°45′42.22853″ N21°38′12.53784″ E
TMIC813:44:42notStaticFloat45°45′36.81844″ N21°38′08.30090″ E
TMIC813:44:42yesStaticfixed all45°45′36.81829″ N21°38′08.29966″ E
TMIC813:44:42yesStaticfixed all45°45′36.81865″ N21°38′08.29931″ E
TMIC913:48:17notStaticFloat45°45′35.75665″ N21°38′09.21471″ E
TMIC913:48:17yesStaticfixed all45°45′35.75523″ N21°38′09.21749″ E
TMIC913:48:17yesStaticfixed all45°45′35.75627″ N21°38′09.21732″ E
Table 7. Post-processing static network by satellite measurements, Topolovăţu Mic.
Table 7. Post-processing static network by satellite measurements, Topolovăţu Mic.
Item No.Height on
Ellipsoid
Orthometric HeightGeoidError on X (m)Error on Y (m)Error 3D (m)
TMIC2361.9191.024.098−662.1790.00640.00260.0069
TMIC2361.6451.023.824−662.1790.00040.00070.0008
TMIC2363.4871.025.665−662.1790.00040.00060.0007
TMIC1357.3801.019.559−662.1790.00130.00090.0016
TMIC1355.9091.018.089−662.1790.00050.00080.0010
TMIC1357.9091.020.088−662.1790.00030.00050.0006
TMIC3388.2221.050.411−662.1900.00450.00380.0059
TMIC3385.7611.047.950−662.1900.00040.00070.0008
TMIC3387.6861.049.876−662.1900.00040.00060.0007
TMIC8363.3511.025.532−662.1810.00210.00140.0026
TMIC8362.0251.024.206−662.1810.00050.00070.0009
TMIC8363.9571.026.138−662.1810.00040.00060.0007
TMIC9364.0741.026.258−662.1840.00560.00400.0069
TMIC9361.6881.023.872−662.1840.00050.00070.0009
TMIC9363.6261.025.810−662.1840.00050.00070.0008
Table 8. Coordinate inventory of new points in WGS 84 system, Topolovăţu Mic.
Table 8. Coordinate inventory of new points in WGS 84 system, Topolovăţu Mic.
Coordinates WGS 1984
Station NameCoordinatesCorrectionsSd
TMIC1Longitude21°13′51.46271″ E0.0000 m-
Height154.7278 m0.0000 m-
TMIC2Latitude45°45′44.01926″ N−0.0004 m0.0271 m
Longitude21°38′08.84475″ E0.0032 m0.0212 m
Height145.6545 m0.0000 m0.0612 m
TMIC3Latitude45°45′42.31566″ N−0.0004 m0.0293 m
Longitude21°38′12.53784″ E0.0032 m0.0220 m
Height148.0696 m0.0000 m0.0547 m
TMIC8Latitude45°45′36.90617″ N−0.0004 m0.0320 m
Longitude21°38′08.29941″ E0.0032 m0.0224 m
Height145.7076 m0.0000 m0.0568 m
TMIC9Latitude45°45′35.84338″ N−0.0004 m0.0344 m
Longitude21°38′09.21734″ E0.0032 m0.0251 m
Height145.6592 m0.0000 m0.0611 m
Table 9. Coordinate inventory of new points in Stereographic System 1970, resulting from transformations for STATIC measurements, Topolovăţu Mic.
Table 9. Coordinate inventory of new points in Stereographic System 1970, resulting from transformations for STATIC measurements, Topolovăţu Mic.
1970 Stereographic Coordinates
StationCoordinatesCorrectionsSd
FAGEY(m)280,960.4506 m0.0000 m-
X (m)487,749.6411 m0.0000 m-
Z (m)173,080 m0.0000 m-
TIM1Y(m)207,132.2474 m0.0000 m-
X (m)482,495.1249 m0.0000 m-
Z (m)111.641 m0.0000 m-
TMIC1Y(m)238,501.5611 m0.0032 m0.0208 m
X (m)479,067.2393 m−0.0006 m0.0280 m
Z (m)101.950 m0.0000 m0.0536 m
TMIC2Y(m)238,508.7103 m0.0032 m0.0212 m
X (m)479,124.9330 m−0.0006 m0.0271 m
Z (m)102.483 m0.0000 m0.0612 m
TMIC3Y(m)238,586.2440 m0.0032 m0.0220 m
X (m)479,069.0104 m−0.0006 m0.0293 m
Z (m)104.900 m0.0000 m0.0547 m
TMIC8Y(m)238,487.6787 m0.0032 m0.0224 m
X (m)478,905.9765 m−0.0006 m0.0320 m
Z (m)102.539 m0.0000 m0.0568 m
TMIC9Y(m)238,506.1187 m0.0032 m0.0251 m
X (m)478,872.3513 m−0.0006 m0.0344 m
Z (m)102.491 m0.0000 m0.0611 m
Table 10. 1970 Stereographic coordinates obtained by CINEMATIC measurements (RTK).
Table 10. 1970 Stereographic coordinates obtained by CINEMATIC measurements (RTK).
Point No.Stereographic Coordinates 1970
Static Reading (RTK)
X (m)Y (m)Z (m)
TMIC1479,067.286238,501.567102,111
TMIC2479,124.943238,508.712102,647
TMIC3479,069.033238,586.241105,086
TMIC8478,905.988238,487.673102,689
TMIC9478,872.351238,506.115102,642
Table 11. 1970 Stereographic coordinates obtained by STATIC measurements after post-processing.
Table 11. 1970 Stereographic coordinates obtained by STATIC measurements after post-processing.
Point No.Stereographic Coordinates 1970
Results from Post-Processing (STATIC)
X (m)Y (m)Z (m)
TMIC1479,067.239238,501.561101,950
TMIC2479,124.933238,508.710102,483
TMIC3479,069.010238,586.244104,900
TMIC8478,905.976238,487.679102,539
TMIC9478,872.351238,506.119102,491
Table 12. Coordinate differences between KINEMATIC (RTK) and STATIC measurements, resulting from field measurements and post-processing.
Table 12. Coordinate differences between KINEMATIC (RTK) and STATIC measurements, resulting from field measurements and post-processing.
Point No.Coordinate Differences
RTK vs. STEREO
X (m)Y (m)Z (m)
TMIC10.0470.0060.161
TMIC20.0100.0020.164
TMIC30.023−0.0030.186
TMIC80.012−0.0060.150
TMIC90.000−0.0040.151
Table 13. Coordinate differences between KINEMATIC (RTK) and STATIC measurements, resulting from field measurements and post-processing in AutoCAD.
Table 13. Coordinate differences between KINEMATIC (RTK) and STATIC measurements, resulting from field measurements and post-processing in AutoCAD.
Point No.RTK vs. STEREO Coordinate Differences
(m)(cm)
TMIC10.0101
TMIC20.0505
TMIC30.0202
TMIC80.0101
TMIC90.0000
Table 14. Position and orientation of the Leica C10 scanner, NH Coșteiu, ID costei1.
Table 14. Position and orientation of the Leica C10 scanner, NH Coșteiu, ID costei1.
ProjectStation PointScanWorldDateTimeMethod
Scan Costei1Costa
1-GPS
sw-00320.10.201712.09.53“Known Backsight”
Position: 0.006 m; Height: 0.004 m
Observation
NameEast Y (m)North X (m)Share Z (m)Target H (m)Target type
Costei1255,279.914475,633.333116.8202.170HDS Tgt 6 inches
Residual value
NameδxδyδzΔHz
Costei2−0.003−0.0040.0110.005
Result
NameEst Y (m)North X (m)Share Z (m)Scanner H (m)Orientation [°]
Costei1255,245.829475,594.854116.7161.621−33.1794
AccuracyPosition0.006Altitude0.004
Table 15. Position and orientation of the Leica C10 scanner, NH Coștei, ID costei3.
Table 15. Position and orientation of the Leica C10 scanner, NH Coștei, ID costei3.
ProjectStation PointScanWorldDateTimeMethod
Scan Costei1Costa
1-GPS
sw-00320.10.201712.09.53“Known Backsight”
Position: 0.006 m; Height: 0.004 m
Observation
NameEast Y (m)North X (m)Share Z (m)Target H (m)Target type
Costei1255,245.817475,594.846116.7022.170HDS Tgt 6 inches
Residual value
NameδxδyδzΔHz
Costei2−0.012−0.012−0.0140.014
Result
NameEst Y (m)North X (m)Share Z (m)Scanner H (m)Orientation [°]
Costei1255,206.308255,206.308114.3051.515−34.2774
AccuracyPosition0.006Altitude0.004
Table 16. Position and orientation of the Scanner Leica C10, NH Coștei, ID costei4.
Table 16. Position and orientation of the Scanner Leica C10, NH Coștei, ID costei4.
ProjectStation PointScanWorldDateTimeMethod
Scan Costei1Costa
1-GPS
sw-00320.10.201712.09.53“Known Backsight”
Position: 0.006 m; Height: 0.004 m
Observation
NameEast Y (m)North X (m)Share Z (m)Target H (m)Target type
Costei3255,206.308475,565.924114.2612.170HDS Tgt 6 inches
Residual value
NameδxδyδzΔHz
Costei3−0.000−0.0000.0040.000
Result
NameEast Y (m)North X (m)Share Z (m)Scanner H (m)Orientation [°]
Costei4255,184.348475,493.796110.5421.586−11.6676
AccuracyPosition0.006Altitude0.004
Table 17. Comparison of TLS—MMS scanning systems.
Table 17. Comparison of TLS—MMS scanning systems.
FeaturesScanning Equipment
ScanStation Leica C10Leica Pegasus
Number of points50,000 points/s600,000 points/s
Cameras1 camera5 rooms
GyroscopeNo, being staticYes
AccelerometerNo, being staticYes
GNSSNoYes
GRID choiceYes (from 1m—mx cm)No (it’s according to the speed)
Data collection timeLong allotted time/station, requiring knowledge for the implementation and orientation of topographical instrumentsShort time, being mobile, does not require targets and knowledge of placing topographic equipment in the station
Processing timeQuite short, knowledge for automatic and manual alignment of pointsLong, SLAM processing, Kalman Filter
Number of images260/stationDepending on the chosen distance, which will be multiplied by 5
(5 cameras)
Preparation of the gearTime identical to the placement of a total stationIt involves mounting a GNSS reference station, a dynamic initialization and a static one, and after the scans are completed, the repetition of the two initializations
Use targetsYesNo
Ground control points (GCP)NoYes
Weight16 kg11.9 kg
Battery lifeLong, with the possibility of change while performing the scan, scans with 2 batteries at a time, and automatic permutation between themLong, scans with 4 batteries at once, and with automatic permutation between them, first the top two will be consumed, then the bottom two
Scanning angle 360 degrees in the horizontal plane with 270 degrees in the vertical plane360 degrees horizontally with 200 degrees in the vertical plane
Scanning temperatureGreater than 4 °C, or possibly a “jacket” will be purchased for the device, avoiding places with strong and cold windsBelow 0 °C, because the mechanism is covered, scanning is also possible in windy and cold conditions
Other optional itemsThe need to make black & white targets for each work, or to purchase 6-inch targets from suppliersA GNSS equipment for RiNNEX data collection, installation of a reference station
Table 18. Comparisons between UAV, TLS and MMS equipment.
Table 18. Comparisons between UAV, TLS and MMS equipment.
FeaturesDJI Phantom 4 Pro GNSS RTK-UAVLeica ScanStation C10_TLSLeica Pegasus Backpack_MMS
Control networkA lot of needsA lot of needsJust a few needed if GNSS visibility is poor
RegulationsStrict regulations, especially in urban areasNoNo
Inside/outsideExterior onlyInterior & ExteriorInterior & Exterior
Data collection timeReducedLargerReduced
Data processing timeFastMediumElevated
Absolute accuracy in 3D5.0 to 8.0 cm0.1 to 1.0 cm2–4 cm
FlexibilityAverage (due to strict regulations)HighHigh
CostMediumReducedElevated
Price in Euro≈1500≈80,000≈400,000
Table 19. Comparative data for several devices using mobile scanning technology.
Table 19. Comparative data for several devices using mobile scanning technology.
ManufacturerLeica Pegasus: BackpackViametris BMS3DOSLAMHERONLi-BackpackROBIN 3DLM–Walk Drive Fly
Spherical images200°–5 cam–20 MPIX360°–LB5:30 MPIX or 4cam 200°–20 MPIX360°–LB5: 30 MPIX360°–2 MPIXN Oy 70°–5 MPIX
PhotogrammetryYESNONONONONO
ScannerVLP16VLP16VLP16VLP16VLP16NO
GNSSHigh-precision GNSS antennaGNSS AntennaGNSS AntennaNONO2 GNSS antennas
IMUIMU High, 125 HZNONONONOYES
SLAMYESYESYESYESYESYES with the help of an additional ZEBREVO device
Flashlight/
lantern
YESNONONONONO
PositioningGNSS/IMU/SLAM/RTKSLAM/GNSSSLAM/GNSSSLAMSLAMGNSS/IMU/SLAM
ErgonomicsPerfectly wearable, it is up to the level of the head, there is no danger of hitting the door frame for example (REDDOT award for design)It is not perfectly wearable, the antenna arm poses a danger of hitting the door frame, it is overheadIt is not perfectly wearable, the antenna arm poses a danger of hitting the door frame, it is overheadDanger of hitting the heightDanger of hitting the heightDifficult to wear for more than 45 min because it has 2 antennas, the center of gravity is not suitable
Table 20. Comparing data on mobile scanning systems (MMS).
Table 20. Comparing data on mobile scanning systems (MMS).
Producer/DistributorVexcelLeica
CountryAustriaSwitzerland
points/second300,000600,000
field of view26 cameras 360 oriz/30 vert. 6,6 MP360 oriz/200 vert.
Camera1 camera 360 roiz/180 vert 105 mp5 cameras × 4 MP CCD + 2 LiDAR sensors
CCD2088 × 21522046 × 2046
pixel size1.4 × 1.4 microns5.5 × 5.5 microns
Lens3.24 mm focal length6 mm focal length
Max. frame speed/second1.5 fps2 fps
data typeLiDARLiDAR
3D laser scannerRotating multi-beam LiDARDual Velodyne VLP16 with 270 × 30 aperture to scanner
No. of channels1616
Frequency10 Hz10 Hz
working distance100 m100 m is maximum, useful is 70 m
Batteries2 batteries up to 2 h of work4 batteries up to 6 h of work
positioning systemGNSS/IMUGNSS/IMU (Clear Navigation Unit for mapping in locations without GNSS/SLAM signal (Simultaneus Localisation And Mapping) for mapping and localization
Constellations-GPS, GLONASS, GALILEO, BEI DU
BandMultiband GNSSL-band, SBAS, QZSS
software includedUltra-Essential Terrestrial MapInfinity Basic
Temperature0–40 degrees C0–40 degrees C
storage temp.−20 to + 50from −20 to + 50
-1GB/minute of travel
weight16.0 kg11.9 kg
Dimensions110 × 37 × 3273 × 27 × 31
Relative3 cm2–3 cm outside/inside
absolute on the outside5 cm5 cm
absolute on the inside without checkpoints-5–50 cm for 10 min walking, minimum 3 double passes
ImagesJPG, TIFFJPG, ASCII for photogrammetric parameters
point cloudsLASLAS, RGB, Recap, 2D, 3D, E57, DWG, DGN, KMZ
Table 21. The most common common commercial mobile cartography systems and related components post-processed precision data values such as RMS.
Table 21. The most common common commercial mobile cartography systems and related components post-processed precision data values such as RMS.
ProviderNameLaser ScannerIMU/GNSSDigital Camera
Sensor (s)RangeAccuracyPos.
Absolute
Resolution
TOPCONIP-S31 scanner100 m, @ ρ100%50 [email protected] 10 m (1σ)0.015–0.025 mSpherical camera,
8000 × 4000 px
TRIMBLEMX81-2 VQ-250500 m, @ ρ80%10 [email protected] 50 m (1σ)0.020–0.025 mUp to 7 cameras, 5 Mpx
1-2 VQ-450800 m, @ ρ80%8 [email protected] 50 m (1σ)
3D laser mappingStreet Mapper1-2 VUX-1HA400 m, @ ρ80%5 mm, (1σ)0.050 mPanoramic camera, 12 Mpx
RieglVMX-2502 VQ-250500 m, @ ρ80%10 [email protected] 50 m (1σ)0.020–0.050 mUp to 6 cameras, 5 Mpx
renishawDynascan S2501-2 scanner(s)250 m10 [email protected] 50 m (1σ)0.020–0.050 m-
TELEDYNE
OPTECH
Lynx SG12 scanners250 m, @ ρ10%5 mm, (1σ)0.050 mUp to 5 cameras, 5 Mpx
and/or panoramic camera
Lynx MG11 scanner0.200 m
Leica GeosystemsLeica PegasusZF 9012119 m0.9 [email protected] 50 m, ρ80% (1σ)0.015–0.020 m8 cameras, 2000 × 2000 px
Leica P20 scan120 m, @ ρ18%6 [email protected] 100 m (1σ)
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Șmuleac, A.; Șmuleac, L.; Popescu, C.A.; Herban, S.; Man, T.E.; Imbrea, F.; Horablaga, A.; Mihai, S.; Paşcalău, R.; Safar, T. Geospatial Technologies Used in the Management of Water Resources in West of Romania. Water 2022, 14, 3729. https://doi.org/10.3390/w14223729

AMA Style

Șmuleac A, Șmuleac L, Popescu CA, Herban S, Man TE, Imbrea F, Horablaga A, Mihai S, Paşcalău R, Safar T. Geospatial Technologies Used in the Management of Water Resources in West of Romania. Water. 2022; 14(22):3729. https://doi.org/10.3390/w14223729

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Șmuleac, Adrian, Laura Șmuleac, Cosmin Alin Popescu, Sorin Herban, Teodor Eugen Man, Florin Imbrea, Adina Horablaga, Simon Mihai, Raul Paşcalău, and Tamas Safar. 2022. "Geospatial Technologies Used in the Management of Water Resources in West of Romania" Water 14, no. 22: 3729. https://doi.org/10.3390/w14223729

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