Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions

dc.contributor.author Young, Sierra
dc.contributor.author Peschel, Joshua
dc.contributor.author Peschel, Joshua
dc.contributor.author Penny, Gopal
dc.contributor.author Thompson, Sally
dc.contributor.author Srinivasan, Veena
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-05-20T20:43:36.000
dc.date.accessioned 2020-06-29T22:43:50Z
dc.date.available 2020-06-29T22:43:50Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.issued 2017-07-06
dc.description.abstract <p>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.</p>
dc.description.comments <p>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." <em>Water</em> 9, no. 7 (2017): 494. DOI: <a href="http://dx.doi.org/10.3390/w9070494" target="_blank">10.3390/w9070494</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/936/
dc.identifier.articleid 2221
dc.identifier.contextkey 12122740
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/936
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1753
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/936/2017_Peschel_RobotAssisted.pdf|||Sat Jan 15 02:31:58 UTC 2022
dc.source.uri 10.3390/w9070494
dc.subject.disciplines Agriculture
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Environmental Indicators and Impact Assessment
dc.subject.disciplines Hydrology
dc.subject.keywords unmanned aerial vehicle
dc.subject.keywords unmanned surface vehicle
dc.subject.keywords remote sensing
dc.subject.keywords agricultural water management
dc.title Robot-Assisted Measurement for Hydrologic Understanding in Data Sparse Regions
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 3ab64f1f-e7f6-4daa-9a3a-3dbf28e8be78
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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