Terrain analysis and data mining techniques applied to location of classic gully in a watershed

dc.contributor.author Gonçalves Vendrusculo, Laurimar
dc.contributor.author Kaleita, Amy
dc.contributor.author Kaleita, Amy
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-13T13:47:30.000
dc.date.accessioned 2020-06-29T22:33:28Z
dc.date.available 2020-06-29T22:33:28Z
dc.date.embargo 2013-08-28
dc.date.issued 2013-07-01
dc.description.abstract <p>Gullies are an extreme form of soil erosion that degrade diverse environments trough the siltation of streams and water bodies. Indirectly, gully erosion compromises crop productivity working as a link to watercourse allowing movement of detached topsoil particles from agricultural fields during heavy storm events. Furthermore, studies found reduction of the catchment area when active gullies are present. This complex process involves multiple factors and it demands to be studied consistently in order to locate the areas prone for gully erosion. The determination of gullies areas depends upon topographical, geological, and hydrological characteristics; however its location is mainly controlled by the high capacity of overland flow to cut the channel. We hypothesize that identification of gully in agricultural landscape can be performed from high-resolution elevation data products and unsupervised clustering approaches. In order to examine this hypothesis we have used variables resultant from of LiDAR-based terrain analysis as input of a three clustering techniques.    A k-means, fuzzy k-means, and CLARA clustering algorithms were used to carry out the cluster analysis. The results of the cluster analysis suggested that 8 classes were optimal for group areas in the watershed. Elevation data from one field-scale watershed near Treynor in Pottawattamie County, IA, was used to calibration purpose and terrain analysis using slope, flow accumulation, plan convexity, topographic wetness Index, and stream power index were calculated. The cluster analysis has shown highest concordance with percentage of corrected classified pixels that approach based in medoid (CLARA) has obtained the best agreement of points within gullied area (30.1%). The results of this research might speed up gullies field surveys and also can serve as input in conservation planning framework.</p>
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/325/
dc.identifier.articleid 1338
dc.identifier.contextkey 4520724
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/325
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/345
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/325/2013_GoncalvesVendrusculoL_TerrainAnalysisData.pdf|||Fri Jan 14 23:36:03 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords data mining
dc.subject.keywords soil erosion
dc.subject.keywords Lidar data
dc.subject.keywords feature classification
dc.title Terrain analysis and data mining techniques applied to location of classic gully in a watershed
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 8a405b08-e1c8-4a10-b458-2f5a82fcf148
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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