Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

7-6-2017

Journal or Book Title

Water

Volume

9

Issue

7

First Page

494

Research Focus Area(s)

Land and Water Resources Engineering

DOI

10.3390/w9070494

Abstract

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.

Comments

This article is published as Young, Sierra, Joshua Peschel, Gopal Penny, Sally Thompson, and Veena Srinivasan. "Robot-assisted measurement for hydrologic understanding in data sparse regions." Water 9, no. 7 (2017): 494. DOI: 10.3390/w9070494. Posted with permission.

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Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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