Drones for River Habitat Assessment? A Report from the International Symposium on Ecohydraulics

Every two years, the International Symposium on Ecohydraulics (ISE) provides an opportunity for the exchange of knowledge, sharing of practical experiences and collaboration between academics and practitioners working across the many disciplines of ecohydraulics, including aquatic ecology, water engineering, hydraulics, fluvial geomorphology and biogeochemistry. The interconnectedness of these disciplines within ecohydraulics is fundamental in addressing many of the present challenges we face in terms of river and water resource management

An assessment of the use of airborne LiDAR for estimating growth of Sitka spruce (Picea sitchensis) plantation forestry at Kielder Forest, UK

A growing need exists for the collection of accurate and up-to-date information on forest growth rates for management purposes. Recent studies indicate that airborne laser scanning (ALS) offers a quicker and more cost-effective approach than the traditional methods of forest inventorying and may have the potential not only to revolutionise forest management but also provide key data concerning world carbon stocks. This study aims to assess the potential of ALS to estimate forest growth rates of the temperate Sitka spruce plantation forests using canopy height distribution models at Kielder Forest, Northumberland. ALS data from 2003 and 2006 provides an excellent, unique opportunity to contribute to existing work which has so far been limited in focus, looking primarily at individual tree growth m the less densely stocked, slow-growing, cold climate forests of Scandinavia. ALS point cloud data from first and last pulse returns are filtered and classified. Ground returns are used to create digital elevation models (DEM), and first returns used to create digital canopy height models (DCHM). Key ALS variables are then extracted and summarised. Processed ALS data from both years are compared to estimate forest growth. The results are compared with ground truth data. Height correlations are strong and positive. Growth is detected at all plot locations but correlations with ground truth data are weak and mostly negative. Potential explanations for the lack of correlation are presented and discussed, including; data misalignment, inherent error within the ground truth data and the set-up of the LiDAR systems. Further study is necessary to quantify and eliminate systematic and random error within both the LiDAR and ground truth data before ALS may be used routinely for forest management purposes

Subaerial Gravel Size Measurement Using Topographic Data Derived From a UAV-SfM Approach

Fluvial grain size plays a fundamental role in determining the condition and availability of aquatic habitats. Remote sensing provides rapid and objective methods of quantifying fluvial grain size, and typically provide coarse grain size outputs (c. 1m) at the catchment scale (up to 80km channel lengths) or fine resolution outputs (c. 1mm) at the patch scale (c. 1m2). Recently, drone based approaches have started to fill the gap between these scales, providing hyperspatial resolution data (<10cm) over reaches up to a few hundred metres in length. This ‘mesoscale’ is of importance to habitat assessments and is aligned with the ideals of the ‘Riverscape’ concept. Most drone based grain size measurement approaches use textural variables computed from drone orthoimagery. To date however, no published works provide quantitative evidence of the success of this approach, despite significant differences in platform stability and the image quality obtained by manned aircraft versus drones. With interest in drone surveys growing rapidly, such error quantification is essential for making reliable, evidence-based recommendations about the suitability of drones for routine management of fluvial environments. Here we provide an initial assessment of the accuracy and precision of grain size estimates produced using two different drone-based methods; (1) the image textural variable ‘negative entropy’, and; (2) the roughness of point clouds derived from drone imagery processed using structure from motion photogrammetry. Data is collected from a small gravel-bed river in Cumbria, UK. Results from jack knife analyses show that the point cloud roughness method gives more accurate and precise measures of grain size at this site, as indicated by the mean (0.0002m) and standard deviation (0.0184m) of residual errors. However, both methods struggle to provide grain size measures with sub-centimetre precision. We suggest that blur within the drone imagery prevents better precision, resulting from an inadequate camera gimbal

Quantifying Fluvial Topography Using UAS Imagery and SfM-Photogrammetry.

The measurement and monitoring of fluvial topography at high spatial and temporal resolutions is in increasing demand for a range of river science and management applications, including change detection, hydraulic models, habitat assessments, river restorations and sediment budgets. Existing approaches are yet to provide a single technique for rapidly quantifying fluvial topography in both exposed and submerged areas, with high spatial resolution, reach-scale continuous coverage, high accuracy and reasonable cost. In this paper, we explore the potential of using imagery acquired from a small unmanned aerial system (UAS) and processed using Structure-from-Motion (SfM) photogrammetry for filling this gap. We use a rotary winged hexacopter known as the Draganflyer X6, a consumer grade digital camera (Panasonic Lumix DMC-LX3) and the commercially available PhotoScan Pro SfM software (Agisoft LLC). We test the approach on three contrasting river systems; a shallow margin of the San Pedro River in the Valdivia region of south-central Chile, the lowland River Arrow in Warwickshire, UK, and the upland Coledale Beck in Cumbria, UK. Digital elevation models (DEMs) and orthophotos of hyperspatial resolution (0.01-0.02m) are produced. Mean elevation errors are found to vary somewhat between sites, dependent on vegetation coverage and the spatial arrangement of ground control points (GCPs) used to georeference the data. Mean errors are in the range 4-44mm for exposed areas and 17-89mm for submerged areas. Errors in submerged areas can be improved to 4-56mm with the application of a simple refraction correction procedure. Multiple surveys of the River Arrow site show consistently high quality results, indicating the repeatability of the approach. This work therefore demonstrates the potential of a UAS-SfM approach for quantifying fluvial topography

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

Drones and digital photogrammetry: from classifications to continuums for monitoring river habitat and hydromorphology

Recently, we have gained the opportunity to obtain very high-resolution imagery and topographic data of rivers using drones and novel digital photogrammetric processing techniques. The high-resolution outputs from this method are unprecedented, and provide the opportunity to move beyond river habitat classification systems, and work directly with spatially explicit continuums of data. Traditionally, classification systems have formed the backbone of physical river habitat monitoring for their ease of use, rapidity, cost efficiency, and direct comparability. Yet such classifications fail to characterize the detailed heterogeneity of habitat, especially those features which are small or marginal. Drones and digital photogrammetry now provide an alternative approach for monitoring river habitat and hydromorphology, which we review here using two case studies. First, we demonstrate the classification of river habitat using drone imagery acquired in 2012 of a 120 m section of the San Pedro River in Chile, which was at the technological limits of what could be achieved at that time. Second, we review how continuums of data can be acquired, using drone imagery acquired in 2016 from the River Teme in Herefordshire, England. We investigate the precision and accuracy of these data continuums, highlight key current challenges, and review current best practices of data collection, processing, and management. We encourage further quantitative testing and field applications. If current difficulties can be overcome, these continuums of geomorphic and hydraulic information hold great potential for providing new opportunities for understanding river systems to the benefit of both river science and management

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process oriented investigations of flow hydraulics, sediment dynamics and in-stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through-water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. Whilst the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM-photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary-winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02m) for two different river systems over channel lengths of 50- 100m. Errors in submerged areas range from 0.016m to 0.089m, which can be reduced to between 0.008m and 0.053m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM-photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10m to a few hundred metres)