{
"DOI": {
"doi": "10.5066/f7736q51",
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"suffix": "f7736q51",
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"identifier": "https://doi.org/10.5066/f7736q51",
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"creators": [
{
"name": "Jones, William R",
"nameType": "Personal",
"givenName": "William R",
"familyName": "Jones",
"affiliation": [],
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"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0002-5493-4138",
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{
"name": "Hartley, Stephen B",
"nameType": "Personal",
"givenName": "Stephen B",
"familyName": "Hartley",
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"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-1380-2769",
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},
{
"name": "Stagg, Camille L",
"nameType": "Personal",
"givenName": "Camille L",
"familyName": "Stagg",
"affiliation": [],
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"schemeUri": "https://orcid.org",
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{
"name": "Osland, Michael J",
"nameType": "Personal",
"givenName": "Michael J",
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],
"titles": [
{
"title": "Land-water classification for selected sites in McFaddin NWR and J.D. Murphree WMA"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2018,
"subjects": [
{
"subject": "Ecology,Environmental Health,Geography,Information Sciences,Remote Sensing"
}
],
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"dates": [
{
"date": "2018",
"dateType": "Issued"
}
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{
"description": "Land-water data was derived from imagery acquired at 350 feet using unmanned aerial systems (UAS) for 6 separate study locations using the Ricoh GR II camera. Three sites are healthy marsh and three sites are degraded marshes. For each study site, ground control markers were established and surveyed in using Real Time Kinematic (RTK) survey equipment. The imagery collected has been processed to produce a land-water classification dataset for scientific research. The land-water data will not only quantify how much marsh is being affected, but the data will also provide a spatial aspect as to where these degrading marsh fragmentations are occurring. The land-water data will be correlated with other data such as salinity, prescribed burns, flooding frequency and flooding duration data to better understand what events may be causing marsh deterioration. With low resolution, vegetation types do not cause any troubling issues with classification but due to the high resolution of the imagery (1.18 inches/0.03 meters) there will be inherent noise that causes speckling throughout the classified image. With the image resolution at such a small Ground Sample Distance (GSD), the smallest of information will be visible. These small pieces of information that we call noise will be introduced into our image classification and will mostly come from vegetation shadows and some water saturation. In this study, we are attempting to identify hollows which are low areas or holes in the vegetation which may suggest a degradation of adjacent marsh. For our study analysis, a hollow is defined as an area that is .25m * .25m = 0.0625m2 (69 pixels) or greater. Any cluster of cells smaller than 69 pixels will be absorbed into the surrounding vegetation type. This method will help reduce noise and maintain confidence in the hollow identification.",
"descriptionType": "Abstract"
}
],
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