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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Landslides and gullies in the mountaintop removal mines of eastern Kentucky and southwestern West Virginia

Authors: Reed, Miles;

Landslides and gullies in the mountaintop removal mines of eastern Kentucky and southwestern West Virginia

Abstract

The extent datasets are high-res, hand-drawn outlines of mountaintop removal extents from large lidar scenes (county-scale). Could be useful in the creation of automated delineation schemes. The fill slides are point datasets of landslides or deformations consistent with incipient landsliding within valley fills created by mountaintop removal. The perimeter slides dataset contains outlines of landslides occurring along the edge of reclaimed mines. The gullies datasets are point locations of gullies associated with reclaimed mountaintop removal mines. Each point is classified by location of the gully. Outside of several gullies in Holden (where I did field work), these datasets have not been assessed for accuracy as most mines are private property. High-res lidar is often considered to be a source of "ground truth" data, though. I would encourage attempts at creating landslide or gully detection schemes using these datasets for training/calibration. Kentucky lidar is available from http://kyfromabove.ky.gov/ as point clouds or rasters. West Virginia lidar is available from http://www.wvgis.wvu.edu/lidar/.

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Keywords

Anthropocene, mountaintop removal mining, Appalachia, geomorphology

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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