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dc.contributor.advisorBartlett, Olivia
dc.contributor.authorKingston, Hannah
dc.date.accessioned2020-12-03T14:46:41Z
dc.date.available2020-12-03T14:46:41Z
dc.date.issued5/3/2018
dc.identifier.urihttps://summit.plymouth.edu/handle/20.500.12774/129
dc.descriptionRemote sensing is used to assist in understanding historical land use by identifying unique physical land characteristics that are representative of historical features. Digital elevation models (DEM), representing Earth's bare surface, can be created from light detection and ranging (LiDAR) data. In New England, historical features, including stonewalls, can be digitized using LiDAR-derived DEM products. Common DEM-derived products, such as hillshade, slope, and aspect, are used to manually digitize the areal extent and location of archeological features. These historical features are considered significant sites that require documentation for various land management purposes; particularly within the White Mountain National Forest (WMNF). In 2016, we started a field assessment within the WMNF using line-transect sampling to assess the accuracy of digitized stonewalls. Although we found that common DEM-derived products led to successful identification of stonewalls, methods of digitizing stonewalls that parallel roads were not always accurate and consistent among digitizers. The objective of this project investigates alternative DEM-derived visual renderings by evaluating surface topography to further delineate stonewalls. We found methods of calculating topographic ruggedness more clearly distinguished stonewalls from roads than initial visual products. These results are essential for informing and standardizing digitizing practices for future statewide and regional efforts.
dc.description.abstractStudent Showcase of Research & Engagement 2018
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherPlymouth State University
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectremote sensing
dc.subjectGIS
dc.subjectLiDAR
dc.subjectarchaeology
dc.subjectstone walls
dc.subjecttopography
dc.subjectStudent Showcase of Research & Engagement 2018
dc.titleUsing LiDAR to identify historical features in the White Mountain National Forest
dc.typetext
dc.typeimages
dc.typeposter
dc.identifier.legacyhttps://digitalcommons.plymouth.edu/showcase/2018/posters/3


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