lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved]
Metrics describing the shape and size of lakes, known as lake morphometry metrics, are important for any limnological study. In cases where a lake has long been the subject of study these data are often already collected and are openly available. Many other lakes have these data collected, but acces...
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2017-09-01
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| Series: | F1000Research |
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| Online Access: | https://f1000research.com/articles/6-1718/v1 |
| _version_ | 1818112471582375936 |
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| author | Jeffrey Hollister Joseph Stachelek |
| author_facet | Jeffrey Hollister Joseph Stachelek |
| author_sort | Jeffrey Hollister |
| collection | DOAJ |
| description | Metrics describing the shape and size of lakes, known as lake morphometry metrics, are important for any limnological study. In cases where a lake has long been the subject of study these data are often already collected and are openly available. Many other lakes have these data collected, but access is challenging as it is often stored on individual computers (or worse, in filing cabinets) and is available only to the primary investigators. The vast majority of lakes fall into a third category in which the data are not available. This makes broad scale modelling of lake ecology a challenge as some of the key information about in-lake processes are unavailable. While this valuable in situ information may be difficult to obtain, several national datasets exist that may be used to model and estimate lake morphometry. In particular, digital elevation models and hydrography have been shown to be predictive of several lake morphometry metrics. The R package lakemorpho has been developed to utilize these data and estimate the following morphometry metrics: surface area, shoreline length, major axis length, minor axis length, major and minor axis length ratio, shoreline development, maximum depth, mean depth, volume, maximum lake length, mean lake width, maximum lake width, and fetch. In this software tool article we describe the motivation behind developing lakemorpho, discuss the implementation in R, and describe the use of lakemorpho with an example of a typical use case. |
| first_indexed | 2024-12-11T03:19:28Z |
| format | Article |
| id | doaj.art-a5f38f05605e45b9a7ba8a64fc8931c5 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-11T03:19:28Z |
| publishDate | 2017-09-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-a5f38f05605e45b9a7ba8a64fc8931c52022-12-22T01:22:40ZengF1000 Research LtdF1000Research2046-14022017-09-01610.12688/f1000research.12512.113548lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved]Jeffrey Hollister0Joseph Stachelek1US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI, USAMichigan State University, Department of Fisheries and Wildlife, Natural Resources Building, East Lansing, MI, USAMetrics describing the shape and size of lakes, known as lake morphometry metrics, are important for any limnological study. In cases where a lake has long been the subject of study these data are often already collected and are openly available. Many other lakes have these data collected, but access is challenging as it is often stored on individual computers (or worse, in filing cabinets) and is available only to the primary investigators. The vast majority of lakes fall into a third category in which the data are not available. This makes broad scale modelling of lake ecology a challenge as some of the key information about in-lake processes are unavailable. While this valuable in situ information may be difficult to obtain, several national datasets exist that may be used to model and estimate lake morphometry. In particular, digital elevation models and hydrography have been shown to be predictive of several lake morphometry metrics. The R package lakemorpho has been developed to utilize these data and estimate the following morphometry metrics: surface area, shoreline length, major axis length, minor axis length, major and minor axis length ratio, shoreline development, maximum depth, mean depth, volume, maximum lake length, mean lake width, maximum lake width, and fetch. In this software tool article we describe the motivation behind developing lakemorpho, discuss the implementation in R, and describe the use of lakemorpho with an example of a typical use case.https://f1000research.com/articles/6-1718/v1BioinformaticsMarine & Freshwater EcologySpatial & Landscape Ecology |
| spellingShingle | Jeffrey Hollister Joseph Stachelek lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] F1000Research Bioinformatics Marine & Freshwater Ecology Spatial & Landscape Ecology |
| title | lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] |
| title_full | lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] |
| title_fullStr | lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] |
| title_full_unstemmed | lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] |
| title_short | lakemorpho: Calculating lake morphometry metrics in R [version 1; referees: 2 approved] |
| title_sort | lakemorpho calculating lake morphometry metrics in r version 1 referees 2 approved |
| topic | Bioinformatics Marine & Freshwater Ecology Spatial & Landscape Ecology |
| url | https://f1000research.com/articles/6-1718/v1 |
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