3D morphological characteristics of gravel bars in an engineered river using LiDAR data and aerial photographs

<p>Fine sediment is a fundamental component of the river system. Fine sediment conditions support good ecological status in different environments since they can affect river habitat and also transport pollutants and nutrients. Moreover, fine sediments can lead to security issues for hydroelectric buildings in the river channel, i.e. sedimentation in reservoirs, turbine erosion, etc. In alpine rivers, a large amount of fine sediments travels over the gravel-bed system as suspension and interacts with the gravel matrix (deposition, infiltration, resuspension). Recent studies highlight that fine sediment stocks in the river bed can be a significant source of suspended load at the event scale, and can have a non-negligible effect on sediment budget estimation. However, there exists no proper estimation of fine sediment stocks in gravel-bed rivers, especially for the sand fraction. One can also question the spatial and temporal variability of these stocks, which makes the estimation of such source of fine sediments challenging.</p><p>In this study, we intend to quantify fine sediment stocks in an Alpine river system (Arc-Isère in the French Alps) characterized by the presence of alternate bars. We estimate the potentially resuspended fine stocks from the gravel bar matrix for different discharges by coupling field measurements, GIS spatial analysis, and 1D modelling. Fine sediment stocks in the gravel bars are firstly measured using a field protocol optimized from the one proposed by Misset et al. (2021). The evaluation of the total stocks of fine sediments is made by combining these local measurements to GIS spatial analysis based on LiDAR data. Then, in order to predict the resuspended fine stocks, a 1D numerical hydraulic model is used to calculate bed shear stresses on the bar surface and evaluate the thickness of the potential remobilized coarse sediments. Having the volume of sediments remobilized, one can evaluate the potentially re-suspended fine stocks for different discharges. The measured fine stocks show a significant amount of sand present in the river bed, which was rarely if not measured in most studies. The silt-clay part of the calculated re-suspended stocks is found equivalent to around 30% of annual Suspended Sediment Matter (SSM) flux for a 15-year return period flood event, which appears consistent since such a large event may yield up to 50% of the annual SSM flux. However, the silt-clay stocks represent around 20% of the total fine stocks only, 80% corresponding to sand. Therefore, a large amount of sand could be re-suspended from the gravel bar matrix and should not be neglected when estimating the downstream sediment budget.</p>

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

Alternate bar development in an alpine river following engineering works

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.

Alternate bars are bedforms recognizable in straight or weakly curved channels as a result of riverbed instability. The length and height of alternate bars scale with the river width and the water depth, respectively. During low water stages, alternate bars become exposed and can be colonized by riparian vegetation. The effects of established plants on the morphodynamics of alternate bars have been poorly investigated. In this work, we focus on the effects induced by rigid vegetation on the dynamics and morphology of previously developed alternate bars in a straight channel by means of flume experiments. We investigate three different spatial densities of plants to reproduce scenarios of vegetation establishment. The results illustrate that vegetation alters both the altimetric and planimetric characteristics of bar patterns. In particular, as compared to bare‐bed bars, vegetated bars have a higher wave amplitude and scour, and this effect becomes stronger with plant density. Moreover, they exhibit decreasing wavenumbers according to vegetation density. A comparison with previous fundamental work for the planimetric instability of straight channels with bare‐bed alternate bars, suggests that the established vegetated bars may promote the transition to river meandering.

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semi‐automatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi‐level, knowledge‐based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non‐channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi‐automatic approach adopted.

Braided reaches were common along near‐natural Alpine rivers, and the associated habitat dynamics supported plant and animal species specialized on early‐successional stages. The extensive riparian zones could mitigate climate change by absorbing floods and by retaining water during droughts. Human impacts largely reduced active river corridors through altered discharge and construction of dykes, while recent restoration projects aim at increasing river dynamics. The causes and consequences of Alpine river degradation are well understood, but there are only few quantitative studies on floodplain degradation and restoration. Thus, we have reconstructed historical changes of gravel bars along five Alpine rivers (Iller, Inn, Isar, Lech, and Wertach) in Southern Germany in the period 1808–2009, based on historical maps and aerial images. We found losses of >90% in gravel bar area along these rivers since the mid‐19th century. The decline was caused by a reduction of the active river corridor and by ongoing succession of the remaining open habitats. Within the past 30 years, at the Isar River, restoration measures were realized with the aim to widen the active river corridor and to recreate gravel bars. In four restored reaches, we found that 5% of the historical gravel bar area recovered, and that the proportion of restored gravel bar area was highest after intermediate flooding. We conclude that the active river corridors of German Alpine rivers are almost completely lost, and that more extensive restoration needs to be done to preserve the habitat dynamics and biodiversity of these systems, and to adapt Alpine rivers to climate change.

The magnitude of river morphological changes are better analyzed through the use of quantitative approaches, wherein resolution accuracy and uncertainty assessment are treated as crucial key-factors. In this sense, the creation of precise DEMs (Digital Elevation Models) of rivers represents an affordable tool to analyze geomorphic variations and budgets, except for wetted areas, where reliable channel digitalization can normally be obtained only using expensive bathymetric surveys. The proposed work aims at improving channel surface models without having available bathymetric sensors, by deriving dry areas elevations from LiDAR data and water depth of wetted areas from aerial photos through a predictive depth-colour relationship. The methodology was applied to two different sub-reaches of the Piave River, a gravel-bed river which suffered severe flood events in 2010. Erosion and deposition patterns were identified through DEM differencing, showing a predominance of scour processes which can lead to channel instability situations. The bathymetric output was compared to other previously-derived models confirming the accuracy of the in-channel elevation estimates. Finally, a discussion on the role played by longitudinal protections during the studied flood events is proposed, focusing the attention on the incidence of two major bank erosions that removed significant volumes of stable areas.

In the 19th and 20th centuries, numerous alpine rivers were modified into straightened and monotonous channels by river regulations, which had significant negative effects on the ecology of the river systems and their floodplains. River revitalisations aim to restore river sections in order to create stepping stones in anthropogenic altered rivers. A large-scale example are the measures along the alpine Inn River between Stams and Rietz in Tyrol (Austria) over a length of about 3 river kilometres, which are implemented within the extension project of the hydropower plant Sellrain-Silz. During two low-water periods (October - April), starting in 2021, the existing bank protections were removed to a large extent, the river bed was widened up to 75 meters and a back water zone as well as a branch were created. As a result, the river stretch can develop by its own dynamics and ecologically valuable areas such as shallow water zones as well as gravel and sand banks are supposed to be formed.In order to assess the morphodynamics and ecological functionality of these measures, while also maintaining flood protection, a comprehensive monitoring program is being conducted. This includes nine photogrammetric surveys using an unmanned aerial vehicle (UAV) between March and October 2022. The UAV data were used to generate ortho-images and digital elevation models. The accuracy was assessed by comparison with airborne LiDAR data from one flight in March 2022. The objective is to quantify the erosion and deposition processes in the area of the measures and to determine the sedimentological processes that occurred during the survey period using the UAV results and hydro-morphological data (e.g., hydrograph, suspended sediment concentration) from nearby gauging stations on the Inn River. In addition, the suitability of high-resolution data from UAV surveys for monitoring sediment and bedload dynamics in alpine rivers will be evaluated. First results show that deposition processes dominated in the area of the measures, while relatively low discharge values were recorded throughout the study period. Based on the data analysis, we elaborate a suggestion for further monitoring in addition to the cross-section surveys already conducted since 1980, i.e., one UAV flight per year at low-flow conditions in order to establish a long term monitoring of morphodynamics.

ABSTRACTEarthflow‐type landslides are persistent natural hazards having deep socio‐economic and environmental consequences. They have significantly contributed to the geomorphic evolution of mountainous slopes in Europe since the Late Glacial. An understanding of their complex kinematics is crucial to better constrain the processes governing their occurrence and mobility.In this work we explored the possibility to quantify displacement vectors on a spatially distributed basis and to quantify volumetric transfer at the slope scale with regard to a large flow‐type landslide located in the northern Apennines of Italy. For this purpose we applied digital image correlation (DIC) and digital elevation model differencing (DEMoD) techniques to multi‐temporal airborne LiDAR surveys of 2006, 2007 and 2009.The DIC was applied to greyscale slope gradient maps retrieved after precise co‐registration of LiDAR surveys. Thereby, movement patterns over various sectors of the landslide were reconstructed and quantified, most notably up to 20 m in the head zone, up to 51 m in the lower main track, and up to about 27 m at the landslide toe. The DEMoD analysis revealed significant mass transfer from the source to the tracks and toe zone, with the upper flow tracks acting as temporal storage of large amounts of material. The mass balance indicated that significant amounts of advancing landslide debris were eroded by a local stream. An integrated analysis of DEMoD and DIC results allowed for a discussion of governing processes, such as the transition from slide to flow, the influence of underlying topography on earthflow mobility, and the role of undrained loading as a mechanism of toe zone reactivation.In conclusion, the successful application of DIC and DEMoD to the case study underlines the added value of high‐resolution DEMs in the analysis of earthflow kinematics toward a better understanding of their role in the geomorphic evolution of slopes. Copyright © 2012 John Wiley & Sons, Ltd.

The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia’s Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Δ 0.24) and a TOST test indicated that the methods were statistically equivalent (±5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.

The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia's Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Δ 0.24) and a TOST test indicated that the methods were statistically equivalent (±5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.

Building-level population data are of vital importance in disaster management, homeland security, and public health. Remotely sensed data, especially LiDAR data, which allow measures of three-dimensional morphological information, have been shown to be useful for fine-scale population estimations. However, studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution. In this article, we proposed a framework to estimate population at the building level by integrating POI data, nighttime light (NTL) data, and LiDAR data. Building objects were first derived using LiDAR data and aerial photographs. Then, three categories of building-level features, including geometric features, nighttime light intensity features, and POI features, were, respectively, extracted from LiDAR data, Luojia1-01 NTL data, and POI data. Finally, a well-trained random forest model was built to estimate the population of each individual building. Huangpu District in Shanghai, China, was chosen to validate the proposed method. A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R 2 = 0.65 at the building level and R 2 = 0.79 at the community level. The NTL radiance intensity was found to have a positive relationship with population in residential areas, while a negative relationship was found in office and commercial areas. Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data, the accuracy of building-level population estimation can be improved.

Natural climatic hazards like flood, an important hydro-geomorphic process of earth’s surface, have different regional and local impacts with significant socio-economic consequences. Similar was the case in Gujarat State, India during last week of June 2005. This study is about assessing the impact of Gujarat flood on river dynamics. It deals with extraction of water bodies information using radiance image and standard water indices i.e., Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) for pre- and post-flooding periods. Geomorphometric analysis along with drainage network extraction was done using two different Digital Elevation Models (DEMs) i.e., Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) and Shuttle Radar Topographic Mission (SRTM) and compared. Finally, depressions mapping and comparative analysis of magnitude and directional change of drainage networks was carried out. Results confirmed better accuracy of MNDWI in separating water bodies. The water bodies area increased by 10.4 % in post-flood monsoon compared to pre-flood monsoon and by 3.8 % in post-flood dry season compared to pre-flood dry season. Geomorphometric analysis indicated that ASTER DEM gave more values of maximum slope, average slope, and standard deviation as compared to SRTM. Aspects distribution algorithm did not work well in low relief regions. The drainage network generated using SRTM DEM was more accurate. The depressions identified were more susceptible to flood events. Change analysis of drainage network (deviating 100–300 m) indicated that 5.22 % points deviated between October, 2004 and 2005 and 3.18 % between February, 2005 and 2006.

We examined whether presence of vegetation and seasonal changes in flow affected N chemistry and denitrification rates within sandbars of a 7th-order sandy alluvial river (Wisconsin River, USA). We addressed these questions with a broad-scale approach of measuring parafluvial water chemistry and denitrification rates in multiple sandbars distributed along a 15-km river reach during summer 2004 and 2005. After recession of spring flooding, parafluvial chemistry in unvegetated bars was characterized by moderate dissolved O2 (DO) and elevated NO3−-N concentrations (>3.5 mg N/L), whereas vegetated bars tended to be hypoxic (<2 mg/L DO) and depleted in NO3−-N (0.2 mg N/L) relative to unvegetated bars and surface water (0.47 mg N/L). As flow declined over the summer, NO3−-N also declined in both bar types, whereas SO42− was relatively constant in unvegetated bars but decreased in vegetated bars. Amendment experiments demonstrated that denitrification was limited primarily by NO3−-N and secondarily by organic C in both bar types, but the strength of this limitation varied over time and was greater in vegetated bars, a result suggesting loss of denitrification capacity. Thus, spatial and temporal patterns of water chemistry and denitrification activity among multiple sandbars indicated that unvegetated bars shifted from N transformers/NO3−-N sources early in the summer to N retainers/NO3−-N sinks as discharge declined, whereas vegetated bars always supported anaerobic processes and probably shifted from NO3−-N to SO42− sinks. We hypothesize that the contribution of vegetated islands to overall riverine N retention is small because establishment of vegetation reduces hydrologic linkages between bars and surface water. Modern changes in the flow regime of the Wisconsin River have increased establishment of riparian vegetation on exposed bars, a pattern suggesting that parafluvial N retention is being reduced while riverine N loading is increasing.