Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography

Laser scanner and UAV digital photogrammetry as support tools for cosmic-ray muon radiography applications: an archaeological case study from Italy

The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach.

250 years of flood frequency and discharge in an ungauged Corsican mountain catchment: A dendrogeomorphic reconstruction

The primary goal of paleoflood hydrology is to estimate the frequency and magnitude of past floods. Botanical evidence, and particularly scars on trees, has been used repeatedly as paleostage indicators to reconstruct peak discharges and flood height. Yet, these reconstructions depend on the presence of visible scars on tree stems which tend to be masked as trees grow older. Here, we estimated flood magnitude using an alternative approach based on growth disturbances in tree-ring series, tree positions and the minimal discharge necessary to submerge the root collar of a tree as estimated by hydraulic modeling. We tested the reliability of this newly developed approach by using the traditional scar-based reconstruction as a benchmark. To this end, we sampled 60 trees showing evidence of flood damage on their stems along a 787-m long segment of the Asco river (Corsica, France). Based on 440 growth disturbances dated in tree-ring series, we reconstructed 28 floods between 1759 and 2020 and 18 during the 20th century. Using the two-dimensional Iber hydraulic model and detailed topographic data of the study site obtained from UAV imagery, we estimated that peak discharges of the 28 reconstructed events ranged between 10 and 210 m³s^-1, with 200 m³s^-1 being considered as the threshold for extreme floods. Not only do the scar-based and root collar submersion approaches yield similar results, findings are also clearly in line with the sparse information available from historical archives and short gauge station records on past floods. The unprecedented length and depth of the record presented here opens new avenues for climate change and flood impact research.

Application of Unmanned Aerial Vehicle DEM in flood modeling and comparison with global DEMs: Case study of Atrak River Basin, Iran

Digital Elevation Models (DEMs) play a significant role in hydraulic modeling and flood risk management. This study initially investigated the effect of Unmanned Aerial Vehicle (UAV) DEM resolutions, ranging from 1 m to 30 m, on flood characteristics, including the inundation area, mean flow depth, and mean flow velocity. Then, the errors of flood characteristics for global DEMs, comprising ALOS (30 m), ASTER (30 m), SRTM (30 m), and TDX (12 m) were quantified using UAV DEM measurements. For these purposes, the HEC-RAS 2D model in steady-state conditions was used to simulate the flood with return periods of 5- to 200 years along 20 km reach of Atrak River located in northeastern Iran. Results indicated when UAV DEM resolution decreased from 1 m to 30 m, inundation area and mean flow depth increased 17.0% (R² = 0.94) and 10.2% (R² = 0.96) respectively, while mean flow velocity decreased 16.8% (R² = -0.94). Validation of the hydraulic modeling using the modified normalized difference water index demonstrated that the HEC-RAS 2D model in conjunction with UAV DEM simulates the flood with ⁓92% accuracy. Comparing the global DEMs with UAV DEM showed that the root mean square error (RMSE) values of the flow depth for ASTER, SRTM, ALOS, and TDX DEMs were 1.77, 1.12, 1.02, and 0.93 m, and the RMSE values of the flow velocity for the same DEMs were 0.81, 0.66, 0.55, and 0.47 m/s, respectively. Furthermore, TDX DEM with a 6.15% error in the inundation area was the nearest to UAV measurements. Overall, TDX DEM revealed a better performance in hydraulic modeling of the fluvial flood characteristics. Hence, it is recommended for environments where high-resolution topography data is scarce. The results of this study could potentially serve as a guideline for selecting global DEMs for hydraulic simulations.

Integrated Geomatics Surveying and Data Management in the Investigation of Slope and Fluvial Dynamics

In mountain environments, slope and fluvial dynamics often interact, and their relationship can be investigated through an integrated methodological approach. Landslides are a source of supplying sediments into riverbeds and can interact or interrupt the water course. Water courses can trigger or re-activate slope movements. The complexity of investigating the interaction between the two dynamics needs a complementarity of methods and techniques, combining remote and proximal sensing, geotechnical in situ surveys, and repositories and catalogue datasets. This leads to a synergistic use of all the heterogeneous data from different fields and formats. The present paper provides a literature review on the approaches and surveying procedures adopted in the investigation of slope and fluvial dynamics and highlights the need to improve the integrated management of geospatial information complemented by quality information. In this regard, we outline a geodatabase structure capable of handling the variety of geoscientific data available at different spatial and temporal scales, with derived products that are useful in integrated monitoring tasks. Indeed, the future adoption of a shared physical structure would allow the merging and synergistic use of data provided by different surveyors as well as the effective storing and sharing of datasets from a monitoring perspective.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Remote Sensing of Riparian Ecosystems

Riparian zones are dynamic ecosystems that form at the interface between the aquatic and terrestrial components of a landscape. They are shaped by complex interactions between the biophysical components of river systems, including hydrology, geomorphology, and vegetation. Remote sensing technology is a powerful tool useful for understanding riparian form, function, and change over time, as it allows for the continuous collection of geospatial data over large areas. This paper provides an overview of studies published from 1991 to 2021 that have used remote sensing techniques to map and understand the processes that shape riparian habitats and their ecological functions. In total, 257 articles were reviewed and organised into six main categories (physical channel properties; morphology and vegetation or field survey; canopy detection; application of vegetation and water indices; riparian vegetation; and fauna habitat assessment). The majority of studies used aerial RGB imagery for river reaches up to 100 km in length and Landsat satellite imagery for river reaches from 100 to 1000 km in length. During the recent decade, UAVs (unmanned aerial vehicles) have been widely used for low-cost monitoring and mapping of riverine and riparian environments. However, the transfer of RS data to managers and stakeholders for systematic monitoring as a source of decision making for and successful management of riparian zones remains one of the main challenges.

UAV and Structure-From-Motion Photogrammetry Enhance River Restoration Monitoring: A Dam Removal Study

Dam removal is a river restoration technique that has complex landscape-level ecological impacts. Unmanned aerial vehicles (UAVs) are emerging as tools that enable relatively affordable, repeatable, and objective ecological assessment approaches that provide a holistic perspective of restoration impacts and can inform future restoration efforts. In this work, we use a consumer-grade UAV, structure-from-motion (SfM) photogrammetry, and machine learning (ML) to evaluate geomorphic and vegetation changes pre-/post-dam removal, and discuss how the technology enhanced our monitoring of the restoration project. We compared UAV evaluation methods to conventional boots-on-ground methods throughout the Bellamy River Reservoir (Dover, NH, USA) pre-/post-dam removal. We used a UAV-based vegetation classification approach that used a support vector machine algorithm and a featureset composed of SfM-derived elevation and visible vegetation index values to map other, herbaceous, shrub, and tree cover throughout the reservoir (overall accuracies from 83% to 100%), mapping vegetation succession as well as colonization of exposed sediments that occurred post-dam removal. We used SfM-derived topography and the vegetation classifications to map erosion and deposition throughout the reservoir, despite its heavily vegetated condition, and estimate volume changes post-removal. Despite some limitations, such as influences of refraction and vegetation on the SfM topography models, UAV provided information on post-dam removal changes that would have gone unacknowledged by the conventional ecological assessment approaches, demonstrating how UAV technology can provide perspective in restoration evaluation even in less-than-ideal site conditions for SfM. For example, the UAV provided perspective of the magnitude and extent of channel shape changes throughout the reservoir while the boots-on-ground topographic transects were not as reliable for detecting change due to difficulties in navigating the terrain. In addition, UAV provided information on vegetation changes throughout the reservoir that would have been missed by conventional vegetation plots due to their limited spatial coverage. Lastly, the COVID-19 pandemic prevented us from meeting to collect post-dam removal vegetation plot data. UAV enabled data collection that we would have foregone if we relied solely on conventional methods, demonstrating the importance of flexible and adaptive methods for successful restoration monitoring such as those enabled via UAV.

A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.

Methodology for Developing a Combined Bathymetric and Topographic Surface Model Using Interpolation and Geodata Reduction Techniques

The research in this paper is concerned with the development of a continuous elevation model in the coastal zones of inland waters. The source data for the creation of numerical terrain models were data acquired by contemporary sensors, such as such as a single-beam echosounder and an unmanned aircraft system. Different interpolation methods were tested in the study. A new approach in the research field is an interpolation method based on the processing of datasets with different degrees of spatial data reduction. The authors call it the Spatial Interpolation Method based on Data Reduction (SIMDR). The choice of method is based on quantitative and qualitative analysis, taking into account the type of interpolation and the method of geodata reduction. A proposal for the practical implementation of the method involves script processing, which automates the processes of modeling and error calculation.

Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning

Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.

Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs.

Bathymetric Detection of Fluvial Environments through UASs and Machine Learning Systems

In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.

UAV-Derived Multispectral Bathymetry

Bathymetry is considered an important component in marine applications as several coastal erosion monitoring and engineering projects are carried out in this field. It is traditionally acquired via shipboard echo sounding, but nowadays, multispectral satellite imagery is also commonly applied using different remote sensing-based algorithms. Satellite-Derived Bathymetry (SDB) relates the surface reflectance of shallow coastal waters to the depth of the water column. The present study shows the results of the application of Stumpf and Lyzenga algorithms to derive the bathymetry for a small area using an Unmanned Aerial Vehicle (UAV), also known as a drone, equipped with a multispectral camera acquiring images in the same WorldView-2 satellite sensor spectral bands. A hydrographic Multibeam Echosounder survey was performed in the same period in order to validate the method’s results and accuracy. The study area was approximately 0.5 km2 and located in Tuscany (Italy). Because of the high percentage of water in the images, a new methodology was also implemented for producing a georeferenced orthophoto mosaic. UAV multispectral images were processed to retrieve bathymetric data for testing different band combinations and evaluating the accuracy as a function of the density and quantity of sea bottom control points. Our results indicate that UAV-Derived Bathymetry (UDB) permits an accuracy of about 20 cm to be obtained in bathymetric mapping in shallow waters, minimizing operative expenses and giving the possibility to program a coastal monitoring surveying activity. The full sea bottom coverage obtained using this methodology permits detailed Digital Elevation Models (DEMs) comparable to a Multibeam Echosounder survey, and can also be applied in very shallow waters, where the traditional hydrographic approach requires hard fieldwork and presents operational limits.

UAV-based evaluation of morphological changes induced by extreme rainfall events in meandering rivers

Morphological changes, caused by the erosion and deposition processes due to water discharge and sediment flux occur, in the banks along the river channels and in the estuaries. Flow rate is one of the most important factors that can change river morphology. The geometric shapes of the meanders and the river flow parameters are crucial components in the areas where erosion or deposition occurs in the meandering rivers. Extreme precipitation triggers erosion on the slopes, which causes significant morphological changes in large areas during and after the event. The flow and sediment amount observed in a river basin with extreme precipitation increases and exceeds the long-term average value. Hereby, erosion severity can be determined by performing spatial analyses on remotely sensed imagery acquired before and after an extreme precipitation event. Changes of erosion and deposition along the river channels and overspill channels can be examined by comparing multi-temporal Unmanned Aerial Vehicle (UAV) based Digital Surface Model (DSM) data. In this study, morphological changes in the Büyük Menderes River located in the western Turkey, were monitored with pre-flood (June 2018), during flood (January 2019), and post-flood (September 2019) UAV surveys, and the spatial and volumetric changes of eroded/deposited sediment were quantified. For this purpose, the DSAS (Digital Shoreline Analysis System) method and the DEM of Difference (DoD) method were used to determine the changes on the riverbank and to compare the periodic volumetric morphological changes. Hereby, Structure from Motion (SfM) photogrammetry technique was exploited to a low-cost UAV derived imagery to achieve riverbank, areal and volumetric changes following the extreme rainfall events extracted from the time series of Tropical Rainfall Measuring Mission (TRMM) satellite data. The change analyses were performed to figure out the periodic morphodynamic variations and the impact of the flood on the selected meandering structures. In conclusion, although the river water level increased by 0.4-5.9 meters with the flood occurred in January 2019, the sediment deposition areas reformed after the flood event, as the water level decreased. Two-year monitoring revealed that the sinuosity index (SI) values changed during the flood approached the pre-flood values over time. Moreover, it was observed that the amount of the deposited sediments in September 2019 approached that of June 2018.

Ground Control Point Distribution for Accurate Kilometre-Scale Topographic Mapping Using an RTK-GNSS Unmanned Aerial Vehicle and SfM Photogrammetry

Unmanned Aerial Vehicles (UAVs) have revolutionised the availability of high resolution topographic data in many disciplines due to their relatively low-cost and ease of deployment. Consumer-grade Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) equipped UAVs offer potential to reduce or eliminate ground control points (GCPs) from SfM photogrammetry surveys, removing time-consuming target deployment. Despite this, the removal of ground control can substantially reduce the georeferencing accuracy of SfM photogrammetry outputs. Here, a DJI Phantom 4 RTK UAV is deployed to survey a 2 × 0.5 km reach of the braided River Feshie, Scotland that has local channel-bar relief of c.1 m and median grain size c.60 mm. Five rectangular adjacent blocks were flown, with images collected at 20° from the nadir across a double grid, with strips flown in opposing directions to achieve locally convergent imagery geometry. Check point errors for seven scenarios with varying configurations of GCPs were tested. Results show that, contrary to some published Direct Georeferencing UAV investigations, GCPs are not essential for accurate kilometre-scale topographic modelling. Using no GCPs, 3300 independent spatially-distributed RTK-GNSS surveyed check points have mean z-axis error −0.010 m (RMSE = 0.066 m). Using 5 GCPs gave 0.016 m (RMSE = 0.072 m). Our check point results do not show vertical systematic errors, such as doming, using either 0 or 5 GCPs. However, acquiring spatially distributed independent check points to check for systematic errors is recommended. Our results imply that an RTK-GNSS UAV can produce acceptable errors with no ground control, alongside spatially distributed independent check points, demonstrating that the technique is versatile for rapid kilometre-scale topographic survey in a range of geomorphic environments.

Let the river erode! Enabling lateral migration increases geomorphic unit diversity

River restoration practice frequently employs conservative designs that create and maintain prescribed, static morphology. Such approaches ignore an emerging understanding of resilient river systems that typically adjust their morphology in response to hydrologic, vegetative and sediment supply changes. As such, using increased dynamism as a restoration design objective will arguably yield more diverse and productive habitats, better managed expectations, and more self-sustaining outcomes. Here, we answer the following question: does restoring lateral migration in a channelised river that was once a wandering gravel-bed river, result in more diverse in-channel geomorphology? We acquired pre- and post-restoration topographic surveys on a segment of the Allt Lorgy, Scotland to quantify morphodynamics and systematically map geomorphic units, using Geomorphic Unit Tool (GUT) software. GUT implements topographic definitions to discriminate between a taxonomy of fluvial landforms that have been developed from an extension of the River Styles framework, using 3-tiered hierarchy: (1) differentiation based on stage or elevation relative to channel; (2) classification of form based on shape (mound, bowl, trough, saddle, plane, wall); and (3) mapping geomorphic units based on attributes (e.g., position and orientation). Results showed restoration increased geomorphic unit diversity, with the Shannon Diversity Index increasing from 1.40 pre-restoration (2012) to 2.04 (2014) and 2.05 (2016) after restoration. Channel widening, due to bank erosion, caused aerial coverage of in-channel geomorphic units to increase 23% after restoration and 6% further in the two-years following restoration. Once bank protection was removed, allowing bank erosion yieled a local supply of sediment to enable the formation and maintenance of lateral and point bars, riffles and diagonal bar complexes, and instream wood created structurally-forced pools and riffles. The methodology used systematically quantifies how geomorphic unit diversity increases when a river is given back its freedom space. The framework allows for testing restoration design hypotheses in post-project appraisal.

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit.

Improving distribution models of riparian vegetation with mobile laser scanning and hydraulic modelling

This study aimed at illustrating how direct measurements, mobile laser scanning and hydraulic modelling can be combined to quantify environmental drivers, improve vegetation models and increase our understanding of vegetation patterns in a sub-arctic river valley. Our results indicate that the resultant vegetation models successfully predict riparian vegetation patterns (Rho = 0.8 for total species richness, AUC = 0.97 for distribution) and highlight differences between eight functional species groups (Rho 0.46-0.84; AUC 0.79-0.93; functional group-specific effects). In our study setting, replacing the laser scanning-based and hydraulic modelling-based variables with a proxy variable elevation did not significantly weaken the models. However, using directly measured and modelled variables allows relating species patterns to e.g. stream power or the length of the flood-free period. Substituting these biologically relevant variables with proxies mask important processes and may reduce the transferability of the results into other sites. At the local scale, the amount of litter is a highly important driver of total species richness, distribution and abundance patterns (relative influences 49, 72 and 83%, respectively) and across all functional groups (13-57%; excluding lichen species richness) in the sub-arctic river valley. Moreover, soil organic matter and soil water content shape vegetation patterns (on average 16 and 7%, respectively). Fluvial disturbance is a key limiting factor only for lichen, bryophyte and dwarf shrub species in this environment (on average 37, 6 and 10%, respectively). Fluvial disturbance intensity is the most important component of disturbance for most functional groups while the length of the disturbance-free period is more relevant for lichens. We conclude that striving for as accurate quantifications of environmental drivers as possible may reveal important processes and functional group differences and help anticipate future changes in vegetation. Mobile laser scanning, high-resolution digital elevation models and hydraulic modelling offer useful methodology for improving correlative vegetation models.

Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifyingabove- and below-water geomorphic change using Structure-from-Motion photogrammetry andinvestigate the implications of water surface elevations, refraction correction measures, and thespatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme,Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed usingStructure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification ofsubmerged geomorphic change to levels of accuracy commensurate with exposed areas is possiblewithout the need for calibration data or a dierent method from exposed areas; (2) there is minimaldierence in results produced by dierent refraction correction procedures using predominantlynadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processingcomplexity; (3) improvements to our estimations of water surface elevations are critical for accuratetopographic estimation in submerged areas and can reduce mean elevation error by up to 73%;and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian NaïveBayes classifier, based on the relationship between error and 11 independent variables, to generate ahigh resolution, spatially continuous model of geomorphic change in submerged areas, constrained byspatially variable error estimates. Our multiple regression model is capable of explaining up to 54%of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-goingtesting and improvements, this machine learning approach has potential for routine application inspatially variable error estimation within the RPAS–SfM workflow.

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study

The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, because cloudy weather prevented the use of OLI images alone. This provided the information needed to perform a time series analysis of the lagoon. We also analyzed the processes that occur in the salt lagoon, characterizing the different spatio-temporal patterns of biophysical parameters. Special attention was given to the role of turbidity and chlorophyll-a levels in the Mar Menor ecosystem with regard to the programs of integral management of this natural space that receives maximum environmental protection. The objective of the work has been fulfilled by answering the questions of the managers: when did the water quality in the Mar Menor begin to change? What is happening in the lagoon? Is remote sensing useful for monitoring the water quality in the Mar Menor? The answers to these questions have allowed the generation of a methodology and monitoring system to track the water quality in the Mar Menor in real-time and space. The tracking system using satellite images is open to the incorporation of images provided by new multispectral sensors.

Can drones be used to conduct water sampling in aquatic environments? A review

Advancements in drone technology have seen the development of drone-assisted water sampling payloads resulting in the ability of drones to retrieve water samples and physico-chemical data from aquatic ecosystems. The application of drones for water sampling provides the potential to fulfil many aspects of the biological and physico-chemical sampling required to meet large-scale water sampling programmes. This paper reviews the achievements made in the development of drone platforms; advances in specially designed water sampling payloads; advances in incorporating off-the-shelf probes and the ability of drone-assisted water sampling payloads to capture water and physico-chemical data from freshwater environments. However, drone-assisted water sampling is still in its infancy and several key limitations include the small volume of water captured via drones to date, the low rate of successful sample capture and the legislative restrictions limiting the distance drones can be flown from the operator. Of critical importance, however, are the clear inconsistencies observed between water chemical parameters obtained using drone-assisted and traditional water sampling methods. Consequently, water samples and physico-chemical data obtained using drones may not provide the level of reliability and accuracy needed to meet the needs of large-scale water sampling programmes. Solutions aimed at addressing these limitations and developing the potential of drones to conduct water samples include: modifying larger drones with greater payload capacity, facilitating the capture of greater volumes of water; technological developments to increase success rates of water capture; planning fieldwork for operation beyond visual line of sight (BVLOS); employing real-time physico-chemical probes; and integrating robust statistical experimental designs. In addition, detailed cost benefit analyses are required to investigate if drones would result in a meaningful financial saving to water sampling programmes. However, it is envisaged that drone-assisted water sampling will act as a pivotal supporting tool if such current limitations can be addressed by future research.

UAV Monitoring of Stream Restorations

This study examines the potential and limits of the unmanned aerial vehicles (UAVs) applicability for the monitoring of stream restoration in an urban environment. UAV imaging was used for long-term post-restoration monitoring of an urban stream. The monitoring was aimed to track the stream changes significant for the assessment of the restoration success, such as the compliance of the restoration to the plan, stability and evolution of the stream channel, or changes in stream and riparian habitats. The recurrent imaging campaigns in the restored segment of Hostavicky brook in Prague, The Czech Republic, were undertaken for three years since the restoration using the DJI Inspire 1 Pro platform. The UAV monitoring revealed that the new stream pattern substantially differs from the proposed restoration plan. Despite this, the new channel has proved stability, supported by intense grassing of the floodplain, resulting in only marginal evolution of the restored channel. The new channel proved the ability to mitigate the course of a significant flood event without significant flood spills outside the riparian zone. The UAV monitoring also revealed intense eutrophication in newly created shallow ponds with insufficient drainage. The research proved that UAV imaging is a unique source of spatial data, providing reliable information for quantitative and qualitative assessment of the stream restoration progress and success.

Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration.

Use of a Wearable Mobile Laser System in Seamless Indoor 3D Mapping of a Complex Historical Site

This paper presents an efficient solution, based on a wearable mobile laser system (WMLS), for the digitalization and modelling of a complex cultural heritage building. A procedural pipeline is formalized for the data acquisition, processing and generation of cartographic products over a XV century palace located in Segovia, Spain. The complexity, represented by an intricate interior space and by the presence of important structural problems, prevents the use of standard protocols such as those based on terrestrial photogrammetry or terrestrial laser scanning, making the WMLS the most suitable and powerful solution for the design of restoration actions. The results obtained corroborate with the robustness and accuracy of the digitalization strategy, allowing for the generation of 3D models and 2D cartographic products with the required level of quality and time needed to digitalize the area by a terrestrial laser scanner.