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Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions

Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Ashoka Trust for Research in Ecology and the Environment, Royal Enclave Sriramapura, Jakkur Post,Bangalore, Karnataka, India
Author to whom correspondence should be addressed.
Water 2017, 9(7), 494;
Received: 28 February 2017 / Revised: 8 June 2017 / Accepted: 2 July 2017 / Published: 6 July 2017
(This article belongs to the Special Issue New Developments in Methods for Hydrological Process Understanding)
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This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as ``tanks'') at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed. View Full-Text
Keywords: unmanned aerial vehicle; unmanned surface vehicle; remote sensing; agricultural water management unmanned aerial vehicle; unmanned surface vehicle; remote sensing; agricultural water management

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Young, S.; Peschel, J.; Penny, G.; Thompson, S.; Srinivasan, V. Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions. Water 2017, 9, 494.

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