Palm Oil Polygons for Ucayali Province, Peru (2019-2020)
| cg.authorship.types | CGIAR single centre | en |
| cg.creator.identifier | Geoffrey Fricker: 0000-0001-6310-3427 | en |
| cg.creator.identifier | Kylee Nielsen: 0000-0002-7654-4632 | en |
| cg.creator.identifier | Isabella Clark: 0000-0003-3561-0848 | en |
| cg.creator.identifier | Naiara Pinto: 0000-0003-2081-0660 | en |
| cg.identifier.doi | https://doi.org/10.7910/dvn/bsc9ei | en |
| cg.reviewStatus | Internal Review | en |
| dc.contributor.author | Fricker, Geoffrey | en |
| dc.contributor.author | Nielsen, Kylee | en |
| dc.contributor.author | Clark, Isabella | en |
| dc.contributor.author | Davis, Jaxson | en |
| dc.contributor.author | Bates, Sarah | en |
| dc.contributor.author | Davis, Isabella | en |
| dc.contributor.author | Pinto, Naira | en |
| dc.date.accessioned | 2023-06-20T13:12:12Z | en |
| dc.date.available | 2023-06-20T13:12:12Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/130765 | |
| dc.title | Palm Oil Polygons for Ucayali Province, Peru (2019-2020) | en |
| dcterms.abstract | A small team of faculty and student researchers hand digitized polygons delineating palm oil plantations in Ucayali, Peru in support of SERVIR Amazonia goals. GIS experts used high-resolution (< 1 m) optical observations to identify areas of oil palm presence across different conditions (young vs. mature, industrial vs. small-scale). This hand-digitized oil palm presence map will serve as a calibration / validation dataset for an automated classification model using remote sensing observations. This task presented numerous challenges, namely the availability of cloud-free, high resolution imagery. Polygons were digitized from numerous imagery datasets including mosaiced basemap imagery from Maxar and Planet Scope. Whenever the high resolution Maxar imagery was available, it was used. In some cases, we were unable to procure imagery in the time frame. We provide a training document describing our methodology and process in QGIS, an open source geospatial software package so other researchers could repeat our methods at later times or different geographic extents. The major variables in our study were the spatial extents of the palm oil plantations, whether they were open or closed canopy, and the imagery data source (2020-01-01) | en |
| dcterms.accessRights | Open Access | |
| dcterms.bibliographicCitation | Fricker, Geoffrey;Nielsen, Kylee;Clark, Isabella;Davis, Jaxson;Bates, Sarah;Davis, Isabella;Pinto, Naira, 2022, "Palm Oil Polygons for Ucayali Province, Peru (2019-2020)", 10.7910/DVN/BSC9EI, Harvard Dataverse, V1, | en |
| dcterms.issued | 2022-10 | en |
| dcterms.language | en | |
| dcterms.license | CC-BY-4.0 | |
| dcterms.subject | perennial crops | en |
| dcterms.subject | oil palms | en |
| dcterms.subject | agriculture | en |
| dcterms.subject | geographical information systems | en |
| dcterms.subject | gis | en |
| dcterms.subject | remote sensing | en |
| dcterms.subject | peru | en |
| dcterms.subject | amazonia | en |
| dcterms.type | Dataset |
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