Riverine Sediment Changes and Channel Pattern of a Gravel-Bed Mountain Torrent

: The alluvial channel of the Langgriesgraben (Austria) is a highly active geomorphic riverine subcatchment of the Johnsbach River with intermittent discharge and braided river structures. The high sediment yield entails both issues and opportunities. For decades, the riverbed was exploited as a gravel pit. Today, as part of the Gesäuse National Park and after renaturation, the sediment yield endangers a locally important bridge located at the outlet of the subcatchment. High-resolution geospatial investigations are vital for the quantiﬁcation of sediment redistribution, which is relevant in terms of river management. Based on unmanned aerial system (UAS) surveys in 2015 (July, September, and October) and 2019 (August and October), high-resolution digital elevation models (DEMs) were generated, which enable us to quantify intra- and multiannual sediment changes. As surface runo ﬀ at the subcatchment occurs on only a few days per year with ﬂash ﬂoods and debris ﬂows that are not predictable and thus hardly observable, the subsurface water conditions were assessed based on electrical resistivity tomography (ERT) measurements, which were conducted in 2019 (November) and 2020 (May, June). Results of the UAS-based surveys showed that, considering the data quality, intra-annual sediment changes a ﬀ ected only small subareas, whereas multiannual changes occurred in the entire study area and amount to net sediment deposition of ≈ 0.3–0.4 m 3 m − 2 , depending on the channel section. In addition, the elevation di ﬀ erences for both intra-annual surveys revealed linear patterns that can be interpreted as braided river channels. As in both survey periods the same areas were a ﬀ ected by changes, it can be concluded that the channel mainly a ﬀ ected by reshaping persisted within the 4-year observation period. The subsurface investigations showed that although both near-surface and groundwater conditions changed, near-surface sediments are mostly dry with a thickness of several meters during the observations.


Introduction
Fluvial landforms are of great importance from an ecological and economical perspective. Fluvial processes vary greatly in size, dimension, and intensity: floods regularly threaten engineered constructions and even human lives, while plenty of small-scale, short-term changes often go unrecognized. Riverine landforms form where water runs as overland flow and streamflow in river channels, which is based on runoff production as the difference between precipitation and    Within the Langgriesgraben area, this study focuses on a section of 1.1 km in length reaching from the bridge close to the Johnsbach River to the junction with the Schwarzschiefergraben entering from the south (Figures 1a and 2a). The riverbed of the Langgriesgraben is a highly active geomorphological area [34][35][36][37], that has been influenced by gravel mining over the course of several decades, which began at least in the 1970s but probably even after World War II [6]. In 2009 and 2010 renaturation measures were carried out [37]. Since then, the Langgriesgraben has been undergoing natural processes of erosion and deposition of unconsolidated sediments [37,38]. The alluvial channel of the Langgriesgraben is characterized by braided structures (Figure 2). Surface runoff is prevalent on only a few days per year caused by rainstorms, which result in flash floods (see Figure 2d,e personal correspondence with local residents and the Gesäuse National Park management). Indications for perennial surface runoff, which are based on field evidence of observations in October 2019 as well as May and June 2020, are evident a bit uphill of the area investigated ( Figure 1a). During the period of the study's measurements (31 July 2015-06 June 2020) the precipitation at the nearest rain gauge (Weidendom) was characterized by seasonal variations with clear maxima (of up to ≈65 mm d −1 ) in the summer months ( Figure 3). Water discharge measurements at the nearest water gauge (Gsengbrücke) reflect the seasonality of discharge with maxima of up to ≈11 m 3 s −1 , even though the measurements appear distorted as of September 2018 because in the former years water discharge was detectable throughout the entire period (Figure 3), and which is a known issue caused by bedload (personal correspondence with the gauge maintaining personnel).  . Precipitation (daily sum, blue bars) measured at the nearest rain gauge Weidendom in the period of observations and water discharge (black line) at the nearest water gauge Gsengbrücke (for location of the gauges see Figure 1c).

Unmanned Aerial System-Based Survey
The area covered with the use of UAS measures ≈17 ha and the channel investigated is ≈3.5 ha large (Figure 1a, white outline). Five flight campaigns in 2015 and 2019 were conducted using two different multirotor UAS (Table 1). Due to still unknown technical issues with the UAS in the first two field campaigns (July and September 2015), the area covered was remarkably smaller than the planned area. This uncovered area was then covered with a separate flight campaign in October 2015 ( Figure 1a) and this is why these subareas are presented in separate maps. The planned image overlap was 80% forward and 60% side. By taking into account the actual average height above ground (nadir of perspective center) of ≈79-≈111 m, at nadir points ground sampling distances (GSD) of ≈0.02 and ≈0.04 m resulted (Table 1), irrespective of the rather slight slope gradient. During the acquisition of the vertically oriented photographs, the UAS stopped at so-called waypoints. The cameras used are a Ricoh GXR A12 and the integrated DJI Phantom 4 camera. The resolution of the consumer grade Ricoh camera with a focal length of 18.3 mm (fixed manual focus set at infinity) is 4288 × 2848 pixels (px). The size of the camera's sensor (Ricoh) is 23.6 × 15.7 mm (≈371 mm 2 ), which gives a pixel size of ≈5.5 µm. The DJI camera's sensor is characterized by a size of 6.3 × 4.7 mm (≈30 mm 2 ), a resolution of 4000 × 3000 px (pixel size of ≈1.58 µm), and a focal length of 3.61 mm. Using the Ricoh camera, the shutter speed was fixed (shutter priority) to 1/1000 s during all three surveys in 2015, as recommended, e.g., [39]. The DJI camera used in the 2019 surveys was not widely adjustable and was used in auto focus mode. As the areas covered using the UAS varied and the cameras used differed, the number of photographs taken ranged from 31 images for a single flight to 190 images for the entire area covered by three flights ( Table 1). The camera mounting of the hexacopter UAS does not allow for off-nadir image capture and it was not possible to acquire convergent imagery. The DJI camera would have made it possible to taking oblique imagery but due to efforts required for field work there was not enough time to conduct additional flights.
Signalized objects that are only viewed from a restricted range of angles cause precisions that are significantly reduced along the viewing axis, e.g., [40,41]. The achievable precision in the viewing direction, σ z , can be estimated by where D is the mean object distance, σ i is the precision of image measurements (assumed to be a half pixel), b is the distance between the camera centers (i.e., the stereo base), and d is the principal distance of the camera. According to [41], the achievable object precision parallel to the image plane, σ x (=σ y ), can be estimated by where m b is the image scale number (calculated by D d ). Considering the intersecting geometry of the imaging configuration, this object precision can be weighted by introducing a design factor q [42], which can be up to a value of 3.0 for weak imaging configurations [41]. Table 1 shows the according estimated precision values.

Electrical Resistivity Tomography
Electrical resistivity is a physical parameter related to the chemical composition of a material and its porosity, temperature, water, and ice content [43]. The principle of electrical resistivity tomography (ERT) is based on electric current that is directly injected into the ground using a pair of electrodes [44]. Consequently, voltage between another pair of electrodes is measured. The impedance of the Earth is derived (which is the ratio of the voltage output measured to the current input) and transformed to apparent resistivity (Ωm). This apparent resistivity is an indicator of the actual underlying electrical resistivity structure of the Earth [44].
For ERT, we used a multi-electrode system (GeoTom, Geolog, Germany) and two-dimensional data inversion (Res2Dinv) for data analysis. For this study, a 196 m long cross profile at the lower section of the study area was chosen for repeated measurements (Figure 1a, Table 2). This profile covers the entire gravel-bed braided river system and the adjacent terrace structures. At the southern end, the profile ends close to bedrock. Along the entire profile 50 electrodes (metal rods) with 4 m spacing Remote Sens. 2020, 12, 3065 7 of 21 in between were installed during the measurements. For the central part of the profile, a more detailed measurement setup was applied with only 2 m spacing between two adjacent electrodes ( Table 2). We applied mainly the Wenner array (partly also Schlumberger for cross-check) because this array is more suitable for layered structures as can be expected in water-related research questions [43].

Geodetic Measurements
For processing the UAS-based photographs and for accuracy assessment, geodetic surveys using a Global Navigation Satellite System (GNSS) receiver (Topcon HiPerV dual-frequency) and Real Time Kinematics (RTK) corrections via EPOSA [45] accompanied the UAS surveys. Thus, ground control points (GCPs) as signalized points were measured for each flight campaign, which is required for indirect georeferencing of the UAS images. The spatial distribution of GCPs for each flight campaign is shown in Figure 4. The precision of the position measurement is 0.02-0.03 m horizontally and 0.03-0.05 m vertically (personal correspondence, Wiener Netze GmbH 2017). In addition to these GCPs so-called independent check points (ICPs) were surveyed in the field campaigns using the same technique. The ICPs differ from GCPs, as these are not signalized points and serve to independently verify the vertical accuracy of the finally generated DEMs (see Section 4.1.2). GNSS was also used to measure each electrode location during the ERT campaigns and thus the course of the ERT profile.

Terrestrial Laser Scanning Data
For accuracy assessment of the UAS-based results of October 2015, which was not accompanied by GNSS measurements, terrestrial laser scanner (TLS) measurements were conducted on 13 October 2015 to independently assess the quality of the UAS-based results. By sending out a laser beam that is reflected by the surface covered, a TLS measures the distance from the device to the surveyed surface and thus 3D coordinates for each surveyed point are derived from the range measurement, the horizontal direction and the vertical angle. Consequently, point clouds are generated that are internally referenced and need to be georeferenced [46,47].
The device used is a Riegl laser scanner LMS-Z620, which was designed for survey ranges of up to 2000 m [48]. The data processing was performed in RiScanPro (Version 2.1.1) and can be summarized as follows: target-based registration of the scan positions (project coordinate system), filtering and manual cleaning of the point cloud data, georeferencing of the point cloud via GNSS measured targets and export of the point cloud for further analysis. Further details are presented in [38] and [49].
Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 21 so-called independent check points (ICPs) were surveyed in the field campaigns using the same technique. The ICPs differ from GCPs, as these are not signalized points and serve to independently verify the vertical accuracy of the finally generated DEMs (see Section 4.1.2). GNSS was also used to measure each electrode location during the ERT campaigns and thus the course of the ERT profile.

Terrestrial Laser Scanning Data
For accuracy assessment of the UAS-based results of October 2015, which was not accompanied by GNSS measurements, terrestrial laser scanner (TLS) measurements were conducted on 13 October 2015 to independently assess the quality of the UAS-based results. By sending out a laser beam that is reflected by the surface covered, a TLS measures the distance from the device to the surveyed surface and thus 3D coordinates for each surveyed point are derived from the range measurement, the horizontal direction and the vertical angle. Consequently, point clouds are generated that are internally referenced and need to be georeferenced [46,47].

Photogrammetric Processing
The processing of the UAS-based photographs is based on SfM [50] photogrammetry. The SfM photogrammetry approach uses photographs captured from different perspectives and these photographs are then automatically assembled to point clouds using image matching techniques. This matching uses the identification of interest points and is based on the Scale-Invariant Feature Transform [51] algorithm. In combination with multi-view stereo (MVS) techniques, SfM photogrammetry enables simultaneous reconstruction of dense 3D models, camera positions, and orientations [52]. Recently, SfM-MVS photogrammetry became increasingly used in a variety of geoscientific fields, e.g., [53][54][55][56][57].
The SfM photogrammetry approach was implemented using the commercial software Agisoft Metashape (v. 1.5.3). Data processing was conducted by generating a sparse point cloud in a first step (feature matching in photos to estimate camera positions and orientations), followed by a bundle adjustment, and finally, a dense point cloud was generated with so-called aggressive depth filtering, which means that small surface details are not considered because they possibly show model-induced errors. The following processing settings were chosen: a default key point limit of 40,000 and a default tie point limit of 4000. The camera parameters such as focal length, principal point offsets (cx, cy), and radial distortion (K1, K2, K3) were set to adjustable in the process of camera self-calibration. The projection accuracy (i.e., the precision of GCPs on the image plane) was set to 0.5 pixel. The measurement precision of the GCP coordinates on the observed surface (in the software referred to as 'marker accuracy') was set to 0.0075 m (as a general value and as the software description recommends a divisor of 4 to be applied to the measurement precision). The dense point clouds were exported to raster files (orthophotos and DEMs) with GSDs of 0.02-0.04 m.

Ground Control Points
For assessing the quality of the photogrammetric processing, the horizontal (XY) and vertical (Z) root mean square errors (RMSEs) of the GCPs were used. The GCPs' XY RMSEs are in the range of 0.02-0.04 m and the Z RMSEs are in the range of 0.03-0.05 m, which gives total RMSEs (XYZ) of 0.03-0.07 m. The residuals are graphically presented in Figure 4. Almost all horizontal residuals stay within the range of a few centimeters, and the vertical residuals are in the same order of magnitude. For all UAS-based results, the spatial distributions of the residuals follow no discernible pattern ( Figure 4). Therefore, the residuals indicate that the georeferencing was conducted adequately.

Independent Check Points
Independent GNSS measurements are of prime importance to assess the quality of DEMs from SfM photogrammetry. In the 2019 field campaigns, such independent check points (ICPs) were recorded for all flight campaigns on the same day as the UAS flights ( These SD values were then used to determine the threshold for the differentiation of meaningful results from possibly model-induced errors. Using the highest SD value from all the DEMs generated (0.075 m) as the error metric, the uncertainty range of a single DEM was estimated with ±0.15 m (2 × SD). This was used as a basis for the rather conservative estimate of the threshold of meaningful elevation differences, which was set to ±0.30 m (Section 4.2) as double the 2 × SD value for a single DEM. The principle of error propagation would allow for a lower threshold, but we opted for a stricter (higher) one to focus on the most substantial results (Section 5.1.2).

Comparison of TLS with UAS Data
For the UAS flight campaign in October 2015 an independent quality assessment was conducted based on three subareas (A-C) covered by TLS data (measured on 13 October 2015, Figure 5). The subarea A is located on an adjacent slope of the channel and subareas B and C are located in the riverbed. The temporal offset between the UAS and TLS data acquisition of 9 days should be neglectable as no extraordinary precipitation at the rain gauge Weidendom (for location see Figure  65) were found. These SD values were then used to determine the threshold for the differentiation of meaningful results from possibly model-induced errors. Using the highest SD value from all the DEMs generated (0.075 m) as the error metric, the uncertainty range of a single DEM was estimated with ±0.15 m (2 × SD). This was used as a basis for the rather conservative estimate of the threshold of meaningful elevation differences, which was set to ±0.30 m (Section 4.2) as double the 2 × SD value for a single DEM. The principle of error propagation would allow for a lower threshold, but we opted for a stricter (higher) one to focus on the most substantial results (Section 5.1.2).

Comparison of TLS with UAS Data
For the UAS flight campaign in October 2015 an independent quality assessment was conducted based on three subareas (A-C) covered by TLS data (measured on 13 October 2015, Figure 5). The subarea A is located on an adjacent slope of the channel and subareas B and C are located in the riverbed. The temporal offset between the UAS and TLS data acquisition of 9 days should be neglectable as no extraordinary precipitation at the rain gauge Weidendom (for location see Figure 1c) occurred ( Figure 3).
The comparison was conducted using DEM differencing. The UAS-based DEM was subtracted from the TLS-based DEM. In the subareas B and C, a systematic offset of the UAS data can be observed ( Figure 5). This visual interpretation is confirmed by a clear positive mean difference for these two small sites (Table 3), which indicates that the UAS-based elevations are generally slightly lower than the TLS-based elevations. Details of the data preparation are presented in [36].

Elevation Differences
The intra-annual elevation differences from 2015 and 2019 are shown in Figure 6a,b. In 2015 (during a 53 days lasting period from July to September), the far eastern and lower section of the Langgriesgraben generally shows only minor changes, with some exceptions of notable net erosion and deposition (Figure 6a). Similarly, only small subareas were affected by noteworthy changes (Figure 6b Figure 6c). The western and uppermost section of the study area shows similar but slightly more distinct changes due to erosion and deposition in the period from October 2015 to October 2019 (Figure 6d).

Evidence of Bank Erosion
The deposited sediments originate from further up the valley and from adjacent slopes, as evident comparing orthophotos from 2015 and 2019 (Figure 7). Erosion and displacement of the riverbank amounted to up to ≈1-3 m on both sides of the channel. For instance, alongside a steep slope located in the uppermost north-western section of the study area loose sediment was deposited (Figure 7c). In addition, changing braided river structures are recognizable (Figure 7).

Evidence of Bank Erosion
The deposited sediments originate from further up the valley and from adjacent slopes, as evident comparing orthophotos from 2015 and 2019 (Figure 7). Erosion and displacement of the riverbank amounted to up to ≈1-3 m on both sides of the channel. For instance, alongside a steep slope located in the uppermost north-western section of the study area loose sediment was deposited (Figure 7c). In addition, changing braided river structures are recognizable (Figure 7).

Volumetric Changes
Based on the elevation differences, the volumetric changes were calculated for the corresponding periods of time and different subareas ( Table 4). In both the intra-annual and multiannual cases deposition exceeded erosion. A further discussion on the effect of different thresholds used (to distinguish noise from actual change) is presented in Section 5.1.3. Table 4. Volume change based on UAS-based surveys (numbers rounded to integer). Note that the values of the first two columns are related to the lowest section of the study site, whereas the comparison of October-based data is related to the upper (and western) section of the study site and the intra-annual comparison of 2019 is related to the entire study area.

Volumetric Changes
Based on the elevation differences, the volumetric changes were calculated for the corresponding periods of time and different subareas ( Table 4). In both the intra-annual and multiannual cases deposition exceeded erosion. A further discussion on the effect of different thresholds used (to distinguish noise from actual change) is presented in Section 5.1.3. Table 4. Volume change based on UAS-based surveys (numbers rounded to integer). Note that the values of the first two columns are related to the lowest section of the study site, whereas the comparison of October-based data is related to the upper (and western) section of the study site and the intra-annual comparison of 2019 is related to the entire study area.

Subsurface Conditions
The ERT measurements give insight into the changes of subsurface water conditions between November 2019 and June 2020 (Figure 8). According to [58], resistivity values in the range of 100 (humid and fractured limestone) to >100,000 Ωm (very compact limestone) can be expected for limestone rocks. [59] measured resistivity values of 10,000-30,000 Ωm for unfrozen and compact Wetterstein limestone. Furthermore, the authors of [60] measured 3000-28,000 Ωm for compact and unweathered Dachstein limestone. The two long profiles measured in November 2019 and June 2020 revealed bedrock at the southern end and along the first third of the profile with values exceeding 10,000 Ωm, which is a reasonable estimate value for fractured limestone and dolomite as outlined above. The sediment thickness at the southern section of the long profiles is in the order of 20 m, whereas at the northern section sediments exceed depths of 30 m. The superficial layer of higher resistivity values is related to dry sediments with air voids in between. In November 2019, this layer was about 2 m thick at the streambed covering a groundwater-filled sediment body. On 15 May 2020, the upper 10 m of the sediments in the riverbed dried out causing a lowering of the groundwater table as indicated by values exceeding 4000 Ωm. However, close to the surface (i.e., upper 5 m) the sediments revealed higher resistivity values compared to November 2019 indicating moistening. About 3 weeks later (6 June 2020), subsurface conditions changed again with lower resistivity values also closer to the surface of the streambed indicating an increase of the water table. However, the comparison of the November 2019 data (both short and long profiles) with the June 2020 data shows that the subsurface channel was more water-saturated in late autumn compared to late spring and early summer.

Subsurface Conditions
The ERT measurements give insight into the changes of subsurface water conditions between November 2019 and June 2020 (Figure 8). According to [58], resistivity values in the range of 100 (humid and fractured limestone) to >100,000 Ωm (very compact limestone) can be expected for limestone rocks. [59] measured resistivity values of 10,000-30,000 Ωm for unfrozen and compact Wetterstein limestone. Furthermore, the authors of [60] measured 3000-28,000 Ωm for compact and unweathered Dachstein limestone. The two long profiles measured in November 2019 and June 2020 revealed bedrock at the southern end and along the first third of the profile with values exceeding 10,000 Ωm, which is a reasonable estimate value for fractured limestone and dolomite as outlined above. The sediment thickness at the southern section of the long profiles is in the order of 20 m, whereas at the northern section sediments exceed depths of 30 m. The superficial layer of higher resistivity values is related to dry sediments with air voids in between. In November 2019, this layer was about 2 m thick at the streambed covering a groundwater-filled sediment body. On 15 May 2020, the upper 10 m of the sediments in the riverbed dried out causing a lowering of the groundwater table as indicated by values exceeding 4000 Ωm. However, close to the surface (i.e., upper 5 m) the sediments revealed higher resistivity values compared to November 2019 indicating moistening. About 3 weeks later (6 June 2020), subsurface conditions changed again with lower resistivity values also closer to the surface of the streambed indicating an increase of the water table. However, the comparison of the November 2019 data (both short and long profiles) with the June 2020 data shows that the subsurface channel was more water-saturated in late autumn compared to late spring and early summer.

Estimation of Survey Quality
With this section we aim to discuss the quality of the data and potential implications. Issues faced in topographic studies of complex terrain are seldom reported [61]. Several survey steps involve factors that, if not carefully managed, could reduce the study's meaningfulness, such as the flight planning, e.g., [62].
The estimated achievable precisions of the five UAS surveys range from σ x = ≈0.04 to ≈0.06 m and σ z = ≈0.07 to ≈0.12 m. Compared to the GCPs' (total) RMSEs of all surveys, which range from 0.03 to 0.07 m, these estimated precision values correspond with the actually achieved values (Table 5). We therefore can assume that the surveys were adequately planned and conducted. In addition, the ICPs' height precision values (SD = 0.03-0.075 m) confirmed the estimated achievable precisions. Furthermore, relative precision ratios (ratio of the theoretical estimate σ z to D) of ≈1:1000 are achievable in SfM-MVS studies, not only in traditional stereo photogrammetry [63,64]. In our case, estimated precision ratios (of σ z to camera distance) are actually in the range of ≈1:1000 or slightly below, which is a result of the flight height above ground and the camera used (Table 5).  Moreover, in a range of SfM-related studies, ratios of RMSE to a survey range of 1:639 are reported [65]. In our case, these ratios are in a range of ≈1:1045-≈1:1305 (Table 5), which is a further indication of the expected and adequate accuracy of our results.

Implications of the Survey Quality for the Interpretation
Due to error propagation the chosen threshold of elevation differences used (±0.30 m) to distinguish actual change from noise could overestimate errors (Section 4.1.2). On the other hand, this approach could be interpreted as conservative estimate, which means that difference values exceeding even this threshold are more reliably showing change. However, in terms of estimating the data quality for the elevation difference calculations, the following equation can be used to consider the propagation of error (see [66]): where E Diff is the error estimate used as threshold, SD DEM1 and SD DEM2 are the standard deviations of errors in each DEM, and t is the critical t-value (of 1.96) at the chosen confidence level of 95%.
Considering the same SD value as in Section 4.1.2 of 0.075 m and additionally SD of 0.05 m (which could be applicable as this value is in between the different SD values resulting from ICPs) would then give thresholds of ±0.21 and ±0.14 m.
Considering different thresholds to distinguish noteworthy elevation changes from noise results to some extent in totally different areal coverage, whereas other subareas of the study area are not influenced by the data's uncertainties (Figure 9). Here, the intra-annual elevation differences of the lower and far eastern section of the Langgriesgraben would be clearly affected by elevation differences in the 2015 survey if the threshold was remarkably lower. Hence, this would lead to the conclusion that most of the study area changed in the particular period of time (Figure 9a). The other cases, namely the 2019 survey (Figure 9b) and the larger threshold used (Figure 9c,d), would not lead to different visually based interpretations. The multiannual elevation differences are not further discussed as the elevation differences are generally far above the thresholds in question.
Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 21 differences in the 2015 survey if the threshold was remarkably lower. Hence, this would lead to the conclusion that most of the study area changed in the particular period of time (Figure 9a). The other cases, namely the 2019 survey ( Figure 9b) and the larger threshold used (Figure 9c,d), would not lead to different visually based interpretations. The multiannual elevation differences are not further discussed as the elevation differences are generally far above the thresholds in question.

Volume Calculations
The consequences of uncertainties on volume calculations are depicted in Figure 10, which is based on the conservative threshold of ±0.30 m (see Section above and 4.1.2).
As the intra-annual elevation differences were to a large extent not meaningful compared to the threshold used to distinguish change from possible error (see Figure 6) or slightly above the threshold, the uncertainties directly influence the calculated volume ( Figure 10). Hence, in both intraannual cases (2015 and 2019) the uncertainty considered would also suggest the conclusion that eroded volume exceeded deposited volume. In contrast, applying the same uncertainties (i.e., using the same threshold) to the volume calculation of the multiannual elevation differences does not suggest this dependency because of the comparably large changes prevalent in most of the study area. Thus, irrespective of the estimated data quality and subarea, the multiannual differences result in the conclusion that the study area was affected by net deposition of sediments.

Volume Calculations
The consequences of uncertainties on volume calculations are depicted in Figure 10, which is based on the conservative threshold of ±0.30 m (see Section above and Section 4.

Geomorphic Importance of Surveys
The exclusion of elevation differences in the order of ±0.05 m applied to the mean of elevation differences (which is not necessarily zero) allows to map the areas affected by intra-annual channel reshaping and reveals a linear pattern. Consequently, a braided channel pattern becomes visible (Figure 11a,b). By considering both of these results, areas that were affected by channel reshaping in both periods (in 2015 and 2019) can be extracted (by matching the areas of both intra-annual patterns) for the lower section of the study area ( Figure 11c). Thus, over the course of 4 years, the channel section in question was affected by sediment reshaping in similar and linear subareas.
We showed that SfM photogrammetry is essential in fluvial environments for geomorphic characterization, which is in line with, e.g., [67]. Similar to TLS, which has proven beneficial for highresolution surveys of gravel-bed rivers compared to (manned) airborne LiDAR and photogrammetry [68], UAS-based photogrammetry is valuable for the assessment of fluvial changes. Furthermore, for studying gravel-bed rivers at the reach-scale, the same methodology of SfM photogrammetry (UASbased, terrestrial) is becoming an essential approach for characterization of grain roughness and grain size distribution [67,69]. However, as SfM photogrammetry became established in geosciences relatively recently, a precondition for reliable morphological interpretations is a detailed consideration of methodological characteristics [67,70]. This makes it possible to better understand the studied forms and the underlying processes generating them [71,72]. Moreover, SfM photogrammetry facilitates easy-to-use change detection even though further research is required to provide researchers and users with the essential methodological information [54,71]. As the intra-annual elevation differences were to a large extent not meaningful compared to the threshold used to distinguish change from possible error (see Figure 6) or slightly above the threshold, the uncertainties directly influence the calculated volume ( Figure 10). Hence, in both intra-annual cases (2015 and 2019) the uncertainty considered would also suggest the conclusion that eroded volume exceeded deposited volume. In contrast, applying the same uncertainties (i.e., using the same threshold) to the volume calculation of the multiannual elevation differences does not suggest this dependency because of the comparably large changes prevalent in most of the study area. Thus, irrespective of the estimated data quality and subarea, the multiannual differences result in the conclusion that the study area was affected by net deposition of sediments.

Geomorphic Importance of Surveys
The exclusion of elevation differences in the order of ±0.05 m applied to the mean of elevation differences (which is not necessarily zero) allows to map the areas affected by intra-annual channel reshaping and reveals a linear pattern. Consequently, a braided channel pattern becomes visible (Figure 11a,b). By considering both of these results, areas that were affected by channel reshaping in both periods (in 2015 and 2019) can be extracted (by matching the areas of both intra-annual patterns) for the lower section of the study area (Figure 11c). Thus, over the course of 4 years, the channel section in question was affected by sediment reshaping in similar and linear subareas.
We showed that SfM photogrammetry is essential in fluvial environments for geomorphic characterization, which is in line with, e.g., [67]. Similar to TLS, which has proven beneficial for high-resolution surveys of gravel-bed rivers compared to (manned) airborne LiDAR and photogrammetry [68], UAS-based photogrammetry is valuable for the assessment of fluvial changes. Furthermore, for studying gravel-bed rivers at the reach-scale, the same methodology of SfM photogrammetry (UAS-based, terrestrial) is becoming an essential approach for characterization of grain roughness and grain size distribution [67,69]. However, as SfM photogrammetry became established in geosciences relatively recently, a precondition for reliable morphological interpretations is a detailed consideration of methodological characteristics [67,70]. This makes it possible to better understand the studied forms and the underlying processes generating them [71,72]. Moreover, SfM photogrammetry facilitates easy-to-use change detection even though further research is required to provide researchers and users with the essential methodological information [54,71].
Remote Sens. 2020, 12, x FOR PEER REVIEW 17 of 21 Figure 11. Interpretation of intra-annual braided channel pattern resulting from elevation differences exceeding ±0.05 m of mean elevation differences (a, b). Intersection of (a) and (b) showing in blue only areas that were affected by reshaping during both survey periods in 2015 and 2019 (c).

Conclusions
For the purpose of recording the central, lower and vegetation-free area of the Langgriesgraben it can be noted that UAS-based recordings are appropriate for delivering high-resolution data. Elevation changes in the alluvial channel of the Langgriesgraben between the period 2015-2019 were in the order of magnitude of up to several meters and affected both its width and thickness. Results of the UAS-based surveys showed that, considering the data quality, intra-annual sediment changes affected only small subareas, whereas multiannual changes occurred in the entire study area and amount to net sediment deposition of ≈0.3−0.4 m 3 m −2 , depending on the subarea, with the lowest section of the channel mainly affected by deposition. Scrutinizing estimates of the data quality and the relating implications revealed that riverine structures and rate of sedimentation are prone to misinterpretation, which is relevant for river management. In addition, the elevation differences for both intra-annual survey periods revealed a pattern that can be interpreted as the channel of the braided river. As in both survey periods a similar pattern emerged, it can be concluded that channel reshaping affected the same subareas in 2015 and 2019 although the channel section was at the same time characterized by net sediment deposition. The subsurface investigations showed that although both near-surface and groundwater conditions changed, near-surface sediments with a thickness of several meters remained dry throughout the observations. In sum, the results provide a meaningful basis for managing the sediment yield endangering a traffic pathway as the amount and pattern detected clearly indicate ongoing sediment demobilization. Figure 11. Interpretation of intra-annual braided channel pattern resulting from elevation differences exceeding ±0.05 m of mean elevation differences (a,b). Intersection of (a,b) showing in blue only areas that were affected by reshaping during both survey periods in 2015 and 2019 (c).

Conclusions
For the purpose of recording the central, lower and vegetation-free area of the Langgriesgraben it can be noted that UAS-based recordings are appropriate for delivering high-resolution data. Elevation changes in the alluvial channel of the Langgriesgraben between the period 2015-2019 were in the order of magnitude of up to several meters and affected both its width and thickness. Results of the UAS-based surveys showed that, considering the data quality, intra-annual sediment changes affected only small subareas, whereas multiannual changes occurred in the entire study area and amount to net sediment deposition of ≈0.3−0.4 m 3 m −2 , depending on the subarea, with the lowest section of the channel mainly affected by deposition. Scrutinizing estimates of the data quality and the relating implications revealed that riverine structures and rate of sedimentation are prone to misinterpretation, which is relevant for river management. In addition, the elevation differences for both intra-annual survey periods revealed a pattern that can be interpreted as the channel of the braided river. As in both survey periods a similar pattern emerged, it can be concluded that channel reshaping affected the same subareas in 2015 and 2019 although the channel section was at the same time characterized by net sediment deposition. The subsurface investigations showed that although both near-surface and groundwater conditions changed, near-surface sediments with a thickness of several meters remained dry throughout the observations. In sum, the results provide a meaningful basis for managing the sediment yield endangering a traffic pathway as the amount and pattern detected clearly indicate ongoing sediment demobilization.