FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2605688001> ?p ?o ?g. }

• W2605688001 endingPage "61" @default.
• W2605688001 startingPage "49" @default.
• W2605688001 abstract "The increased volume, spatial resolution, and areal coverage of high-resolution images of Mars over the past 15 years have led to an increased quantity and variety of small-scale landform identifications. Though many such landforms are too small to represent individually on regional-scale maps, determining their presence or absence across large areas helps form the observational basis for developing hypotheses on the geological nature and environmental history of a study area. The combination of improved spatial resolution and near-continuous coverage significantly increases the time required to analyse the data. This becomes problematic when attempting regional or global-scale studies of metre and decametre-scale landforms. Here, we describe an approach for mapping small features (from decimetre to kilometre scale) across large areas, formulated for a project to study the northern plains of Mars, and provide context on how this method was developed and how it can be implemented. Rather than “mapping” with points and polygons, grid-based mapping uses a “tick box” approach to efficiently record the locations of specific landforms (we use an example suite of glacial landforms; including viscous flow features, the latitude dependant mantle and polygonised ground). A grid of squares (e.g. 20 km by 20 km) is created over the mapping area. Then the basemap data are systematically examined, grid-square by grid-square at full resolution, in order to identify the landforms while recording the presence or absence of selected landforms in each grid-square to determine spatial distributions. The result is a series of grids recording the distribution of all the mapped landforms across the study area. In some ways, these are equivalent to raster images, as they show a continuous distribution-field of the various landforms across a defined (rectangular, in most cases) area. When overlain on context maps, these form a coarse, digital landform map. We find that grid-based mapping provides an efficient solution to the problems of mapping small landforms over large areas, by providing a consistent and standardised approach to spatial data collection. The simplicity of the grid-based mapping approach makes it extremely scalable and workable for group efforts, requiring minimal user experience and producing consistent and repeatable results. The discrete nature of the datasets, simplicity of approach, and divisibility of tasks, open up the possibility for citizen science in which crowdsourcing large grid-based mapping areas could be applied." @default.
• W2605688001 created "2017-04-28" @default.
• W2605688001 creator A5002919397 @default.
• W2605688001 creator A5003209106 @default.
• W2605688001 creator A5005080181 @default.
• W2605688001 creator A5008201742 @default.
• W2605688001 creator A5012361043 @default.
• W2605688001 creator A5013262672 @default.
• W2605688001 creator A5017209360 @default.
• W2605688001 creator A5020191614 @default.
• W2605688001 creator A5020511117 @default.
• W2605688001 creator A5027933909 @default.
• W2605688001 creator A5035481362 @default.
• W2605688001 creator A5037742381 @default.
• W2605688001 creator A5073789981 @default.
• W2605688001 creator A5075466858 @default.
• W2605688001 creator A5075906794 @default.
• W2605688001 creator A5087864276 @default.
• W2605688001 creator A5088069568 @default.
• W2605688001 date "2017-06-01" @default.
• W2605688001 modified "2023-10-17" @default.
• W2605688001 title "Grid-based mapping: A method for rapidly determining the spatial distributions of small features over very large areas" @default.
• W2605688001 cites W1575296942 @default.
• W2605688001 cites W1968173756 @default.
• W2605688001 cites W1971906828 @default.
• W2605688001 cites W1973617505 @default.
• W2605688001 cites W1980469982 @default.
• W2605688001 cites W1980942086 @default.
• W2605688001 cites W1982776499 @default.
• W2605688001 cites W1984031617 @default.
• W2605688001 cites W1992145662 @default.
• W2605688001 cites W1994200797 @default.
• W2605688001 cites W1995451982 @default.
• W2605688001 cites W2006107328 @default.
• W2605688001 cites W2013508505 @default.
• W2605688001 cites W2020615976 @default.
• W2605688001 cites W2022594017 @default.
• W2605688001 cites W2024347184 @default.
• W2605688001 cites W2028134116 @default.
• W2605688001 cites W2030642252 @default.
• W2605688001 cites W2032452384 @default.
• W2605688001 cites W2037372977 @default.
• W2605688001 cites W2038992212 @default.
• W2605688001 cites W2043701783 @default.
• W2605688001 cites W2056615600 @default.
• W2605688001 cites W2057087866 @default.
• W2605688001 cites W2058886389 @default.
• W2605688001 cites W2064107017 @default.
• W2605688001 cites W2067800317 @default.
• W2605688001 cites W2072186658 @default.
• W2605688001 cites W2074151385 @default.
• W2605688001 cites W2076509405 @default.
• W2605688001 cites W2078375547 @default.
• W2605688001 cites W2085819344 @default.
• W2605688001 cites W2088895318 @default.
• W2605688001 cites W2103201781 @default.
• W2605688001 cites W2139407666 @default.
• W2605688001 cites W2147935060 @default.
• W2605688001 cites W2151346430 @default.
• W2605688001 cites W2158400413 @default.
• W2605688001 cites W2162590785 @default.
• W2605688001 cites W2588612296 @default.
• W2605688001 cites W4243063327 @default.
• W2605688001 cites W4362213396 @default.
• W2605688001 doi "https://doi.org/10.1016/j.pss.2017.04.002" @default.
• W2605688001 hasPublicationYear "2017" @default.
• W2605688001 type Work @default.
• W2605688001 sameAs 2605688001 @default.
• W2605688001 citedByCount "23" @default.
• W2605688001 countsByYear W26056880012017 @default.
• W2605688001 countsByYear W26056880012018 @default.
• W2605688001 countsByYear W26056880012019 @default.
• W2605688001 countsByYear W26056880012020 @default.
• W2605688001 countsByYear W26056880012021 @default.
• W2605688001 countsByYear W26056880012022 @default.
• W2605688001 countsByYear W26056880012023 @default.
• W2605688001 crossrefType "journal-article" @default.
• W2605688001 hasAuthorship W2605688001A5002919397 @default.
• W2605688001 hasAuthorship W2605688001A5003209106 @default.
• W2605688001 hasAuthorship W2605688001A5005080181 @default.
• W2605688001 hasAuthorship W2605688001A5008201742 @default.
• W2605688001 hasAuthorship W2605688001A5012361043 @default.
• W2605688001 hasAuthorship W2605688001A5013262672 @default.
• W2605688001 hasAuthorship W2605688001A5017209360 @default.
• W2605688001 hasAuthorship W2605688001A5020191614 @default.
• W2605688001 hasAuthorship W2605688001A5020511117 @default.
• W2605688001 hasAuthorship W2605688001A5027933909 @default.
• W2605688001 hasAuthorship W2605688001A5035481362 @default.
• W2605688001 hasAuthorship W2605688001A5037742381 @default.
• W2605688001 hasAuthorship W2605688001A5073789981 @default.
• W2605688001 hasAuthorship W2605688001A5075466858 @default.
• W2605688001 hasAuthorship W2605688001A5075906794 @default.
• W2605688001 hasAuthorship W2605688001A5087864276 @default.
• W2605688001 hasAuthorship W2605688001A5088069568 @default.
• W2605688001 hasBestOaLocation W26056880012 @default.
• W2605688001 hasConcept C108497213 @default.
• W2605688001 hasConcept C114793014 @default.
• W2605688001 hasConcept C121332964 @default.

• next