This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Hydromorphological Classification Using Synchronous Pressure and Inertial Sensing Asko Ristolainen , Kaia Kalev, Jeffrey Andrew Tuhtan, Alar Kuusik, and Maarja Kruusmaa Abstract— Classification of river morphology is often based on hydromorphological units (HMUs) identified from field mea- surements. Established survey methods rely on expert judg- ment or collection of field point measurements. When used for HMU classification, these methods can suffer from high errors due to the variations in the sampling environment, causing low repeatability. In order to expedite field data collection and increase HMU classification accuracy, we propose a multisensory device, the hydromast. Each hydromast provides a new source of data to classify HMUs. The modules are inexpensive and highly portable, consisting of a synchronous array of commodity pressure and inertial sensors. Rapid, local changes in the flow field are recorded with absolute and differential pressure sensors. At the same time, slower depth-integrated flow signals are obtained from a small damped cylindrical mast, driven by vortex- induced vibrations. In contrast to existing passive flow measure- ment technologies, the hydromast uses fluid–body interactions to provide flow measurements. This allows for minimal signal processing and simple feature extraction. An array of three hydromasts was used to collect ten samples in three river HMUs with shallow depths and highly turbulent flows with smooth and rough beds. We investigated classification accuracy using single, dual, and triple hydromast arrays with pressure, inertial, and combined features using linear regression, a genetic algorithm, and a neural network. Although limited in scope, the set of spot measurements covering three HMUs showed that a single multimodal sensor could deliver an overall classification accuracy of 89% of the HMUs, and an increase of up to 99% was achieved using a multimodal triple hydromast array. These preliminary results show promise in using hydromasts for rapid and robust HMU classification, providing a new way to collect and assess river survey data. Index Terms— Fluid flow measurements, hydrologic measure- ments, water. I. I NTRODUCTION H YDROMORPHOLOGICAL units (HMUs) are river sections broken down into a series of multiscale elements, and should ideally include both field and remote Manuscript received April 10, 2017; revised August 21, 2017 and November 21, 2017; accepted January 2, 2018. This work was supported by the European Union’s Horizon 2020 Research and Innovation Program under Grant 635568 through the frame of Lakhsmi Project. The work of J. A. Tuhtan was supported in part by the Estonian Base Financing under Grant B53, in part by Octavo and PUT under Grant 1690, and in part by Bioinspired flow sensing. (Corresponding author: Asko Ristolainen.) A. Ristolainen, K. Kalev, J. A. Tuhtan, M. Kruusmaa are with the Centre for Biorobotics, Tallinn University of Technology, 12618 Tallinn, Estonia (e-mail: asko.ristolainen@ttu.ee). A. Kuusik is with the Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, 12618 Tallinn, Estonia. This paper has supplementary downloadable material available at http://ieeexplore.ieee.org, provided by the author. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2018.2795641 sensing data [1]. The reach scale divides the river longitu- dinally into a series of subunits, and is the most commonly investigated type of HMU as it contains salient features of the ecological–geomorphological interface [2]. Remote sensing can be used to efficiently collect spatial data and quan- tify temporal effects [3]. However, HMU classification still requires in situ measurements to supply ground truth data [4]. Once HMUs are classified, they provide essential geospatial information for end users including regional planning, flood control, and biodiversity assessment [5]–[7]. Current river morphological survey methodologies rely heavily on expert opinion [8]. This sets limits on the automated classification of HMUs, primarily due to a lack of repeatable field data collection and assessment methods [9]. In general, the local variables such as depth, velocity, cover type, and bed surface condition are used as the primary features to first classify HMUs, which can then be related to aquatic habitats [10], [11]. More recently, local variables such as river surface flow speed have been measured remotely with different technologies [12]–[16]. Hauer et al. [17] found functional linkages between the flow velocity, depth, and bottom shear stress, and developed relations to ecological mesohabitat units using LiDAR bathym- etry and 2-D hydrodynamic models. A hydromorphological index of diversity using the coefficient of variation of the velocity and water depth has also been applied using extensive field data and hydrodynamic models to evaluate reach-scale heterogeneity in alpine gravel-bed streams [18]. In addition, a time-series study of reach-scale units has shown that the classification boundaries can merge or shift depending on the river flow rate [19]. Due to the spatial and temporal changes occurring in rivers, it is, therefore, key that objective and repeatable field measurement of the local variables serve as inputs to calibrate and validate HMU classification methods. This requires the repeated collection of field data correspond- ing to different flow rates which are caused by seasonal changes in the runoff. At the reach scale, data collection of the local variables is expensive and time consuming. This is because a plu- rality of separate point measurements (depth, time-averaged velocity, and sediment samples) must be collected separately and processed before classification can be performed. The hydromast, a simple, timesaving, field survey device that collects local flow information using collocated synchronous pressure and inertial data, provides a methodology to collect an ensemble of relevant hydrodynamic data at each measurement location. Thus, the time reduced is at least 1/3, and in the case 0196-2892 © 2018 IEEE. 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