Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R2-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R2-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications

Resistance and reconfiguration of natural flexible submerged vegetation in hydrodynamic river modelling

In-stream submerged macrophytes have a complex morphology and several species are not rigid, but are flexible and reconfigure along with the major flow direction to avoid potential damage at high stream velocities. However, in numerical hydrodynamic models, they are often simplified to rigid sticks. In this study hydraulic resistance of vegetation is represented by an adapted bottom friction coefficient and is calculated using an existing two layer formulation for which the input parameters were adjusted to account for (i) the temporary reconfiguration based on an empirical relationship between deflected vegetation height and upstream depth-averaged velocity, and (ii) the complex morphology of natural, flexible, submerged macrophytes. The main advantage of this approach is that it removes the need for calibration of the vegetation resistance coefficient. The calculated hydraulic roughness is an input of the hydrodynamic model Telemac 2D, this model simulates depth-averaged stream velocities in and around individual vegetation patches. Firstly, the model was successfully validated against observed data of a laboratory flume experiment with three macrophyte species at three discharges. Secondly, the effect of reconfiguration was tested by modelling an in situ field flume experiment with, and without, the inclusion of macrophyte reconfiguration. The inclusion of reconfiguration decreased the calculated hydraulic roughness which resulted in smaller spatial variations of simulated stream velocities, as compared to the model scenario without macrophyte reconfiguration. We discuss that including macrophyte reconfiguration in numerical models input, can have significant and extensive effects on the model results of hydrodynamic variables and associated ecological and geomorphological parameters

Numerical simulations of flow around vegetation with Telemac2D: application on laboratory experiments and on the Isère river (France)

Water Qualit

Implementing plant growth of flexible aquatic vegetation into a hydrodynamic model (TELEMAC2D)

Water Qualit

What is a Macrophyte Patch? Patch Identification in Aquatic Ecosystems and Guidelines for Consistent Delineation

Schoelynck J, Creëlle S, Buis K, De Mulder T, Emsens W, Hein T, Meire D, Meire P, Okruszko T, Preiner S, Roldan Gonzalez R, Silinski A, Temmerman S, Troch P, Van Oyen T, Verschoren V, Visser F, Wang C, Wolters J, Folkard A, in press. . Ecohydrology & Hydrobiology. DOI 10.1016/j.ecohyd.2017.10.00

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy

Development of a knowledge driven rule set for classification of submerged aquatic vegetation (sav) in a clear water stream : where do you draw the boundaries...?

A recent attempt at mapping submerged aquatic vegetation (SAV) species composition of a clear water stream in Belgium from ultra-high resolution, multispectral photographs, using object based image analysis (OBIA), resulted in a low, but consistent overall classification accuracy (53-61%). Since the results were obtained with a single rule set they show promise for the development of an automated tool to map SAV despite the challenges of its submerged environment. This extended abstract investigates to what extent difficulties with species delineation in the validation data may have influenced the results. We compare class boundaries, as drawn by experts along image segmentation outlines, with the results from the expert knowledge driven classification rules. A comparison for ‘pure’ objects, where the expert is certain about the assigned object class, resulted in a moderately good overall similarity (68%), while inclusion of ambiguous objects reduces the results to 59%. Under ideal circumstances the rule set seems capable of 74% similarity with expert validation data

Studiedag: Wat als ... elke instromende student een stem en gezicht had?

status: publishe