An analysis of multispectral unmanned aerial systems for salt marsh foreshore land cover classification and digital elevation model generation

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dc.contributor.advisor Van Proosdij, Danika, 1969-
dc.creator Horrocks, Logan D.
dc.date.accessioned 2018-06-04T14:24:15Z
dc.date.available 2018-06-04T14:24:15Z
dc.date.issued 2018
dc.identifier.uri http://library2.smu.ca/handle/01/27535
dc.description 1 online resource (viii, 84 p.) : illustrations (chiefly colored)
dc.description Includes abstract in English and French.
dc.description Includes appendix.
dc.description Includes bibliographical references (p. 60-66).
dc.description.abstract Recent advances in Unmanned Aerial Systems (UAS), and increased affordability, have proliferated their use in the scientific community. Despite these innovations, UAS attempts to map a site’s true elevation using Structure from Motion Multi-View Stereo (SFM-MVS) software are obstructed by vegetative canopies, resulting in the production of a Digital Surface Model (DSM), rather than the desired Digital Elevation Model (DEM). This project seeks to account for the varying heights of vegetation communities within the Masstown East saltmarsh, producing DEMs for mudflat/saltmarsh landscapes with an accuracy comparable to that the DSM. DEM generation has been completed in two separate stages. The first stage consists of land cover classifications using UAS derived, radiometrically corrected data. Respective land cover classifications are assessed using confusion matrices. Secondly, surveyed canopy heights and function derived heights are subtracted from their respective classes, generating the DEMs. DEM validation has been performed by comparing topographic survey point values to those modeled, using the Root Square Mean Error (RMSE) measure. The project then compares the various parameters implemented for land cover classifications, and DEM accuracy. DEM generation methods were then coupled to produce a final DEM with a RMSE of 6cm. The results suggest consumer grade Multispectral UAS can produce DEMs with accuracies comparable to the initial DSMs generated, and thus merit further studies investigating their scientific capacities. en_CA
dc.description.provenance Submitted by Greg Hilliard (greg.hilliard@smu.ca) on 2018-06-04T14:24:15Z No. of bitstreams: 1 Horrocks_Logan_Honours_2018.pdf: 4962599 bytes, checksum: e702903d42bbbacf4c78d899e5a59886 (MD5) en
dc.description.provenance Made available in DSpace on 2018-06-04T14:24:15Z (GMT). No. of bitstreams: 1 Horrocks_Logan_Honours_2018.pdf: 4962599 bytes, checksum: e702903d42bbbacf4c78d899e5a59886 (MD5) Previous issue date: 2018-04-11 en
dc.language.iso en en_CA
dc.publisher Halifax, N.S. : Saint Mary's University
dc.title An analysis of multispectral unmanned aerial systems for salt marsh foreshore land cover classification and digital elevation model generation en_CA
dc.title.alternative An analysis of multispectral unmanned aerial systems for saltmarsh foreshore land cover classification and digital elevation model generation
dc.type Text en_CA
thesis.degree.name Bachelor of Science (Honours Geography)
thesis.degree.level Undergraduate
thesis.degree.discipline Geography and Environmental Studies
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)
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