MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION
This work describes the generation of a database of algal species richness at two spatial scales – regional (Gulf of Mexico and the Caribbean) and global (coastal zones). As a first approach to the definition of the temporal variability, and to produce the corresponding maps, a previously published...
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-7fdb19dcbe3e403bb5ff9d09d73e3abc2020-11-24T21:53:38ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-04-01XL-7/W31005100810.5194/isprsarchives-XL-7-W3-1005-2015MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATIONF. O. Tapia-Silva0O. E. Hernández-Cervantes1M. I. Vilchis-Alfaro2A. Sentíes3K. M. Dreckmann4Departamento de Hidrobiología, Universidad Autónoma Metropolitana-Iztapalapa. A.P. 55-535, México, D.F. 09340, MéxicoMaestría en Biología. Universidad Autónoma Metropolitana-Iztapalapa. A.P. 55-535, México, D.F. 09340, MéxicoMaestría en Biología. Universidad Autónoma Metropolitana-Iztapalapa. A.P. 55-535, México, D.F. 09340, MéxicoDepartamento de Hidrobiología, Universidad Autónoma Metropolitana-Iztapalapa. A.P. 55-535, México, D.F. 09340, MéxicoDepartamento de Hidrobiología, Universidad Autónoma Metropolitana-Iztapalapa. A.P. 55-535, México, D.F. 09340, MéxicoThis work describes the generation of a database of algal species richness at two spatial scales – regional (Gulf of Mexico and the Caribbean) and global (coastal zones). As a first approach to the definition of the temporal variability, and to produce the corresponding maps, a previously published decision tree is used in order to select the best spatial interpolation technique according to the characteristics of the spatial data. The methods presented are ordinary Kriging (since no relationship exists with any environmental variable that could function as an external variable) and inverse distance squared, for comparative purposes. The methods to generate the spatial layers are evaluated using the leave-one-out cross validation technique. Although the evaluation did not find a large correspondence (in terms of linear regression) between the interpolated and measured values, it was possible to capture the spatial variability of the process and produce the cartography of this variable, with which future ecological analyses can be performed.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1005/2015/isprsarchives-XL-7-W3-1005-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
F. O. Tapia-Silva O. E. Hernández-Cervantes M. I. Vilchis-Alfaro A. Sentíes K. M. Dreckmann |
spellingShingle |
F. O. Tapia-Silva O. E. Hernández-Cervantes M. I. Vilchis-Alfaro A. Sentíes K. M. Dreckmann MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
F. O. Tapia-Silva O. E. Hernández-Cervantes M. I. Vilchis-Alfaro A. Sentíes K. M. Dreckmann |
author_sort |
F. O. Tapia-Silva |
title |
MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION |
title_short |
MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION |
title_full |
MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION |
title_fullStr |
MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION |
title_full_unstemmed |
MAPPING OF ALGAE RICHNESS USING SPATIAL DATA INTERPOLATION |
title_sort |
mapping of algae richness using spatial data interpolation |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2015-04-01 |
description |
This work describes the generation of a database of algal species richness at two spatial scales – regional (Gulf of Mexico and the
Caribbean) and global (coastal zones). As a first approach to the definition of the temporal variability, and to produce the corresponding
maps, a previously published decision tree is used in order to select the best spatial interpolation technique according to the characteristics
of the spatial data. The methods presented are ordinary Kriging (since no relationship exists with any environmental variable that could
function as an external variable) and inverse distance squared, for comparative purposes. The methods to generate the spatial layers are
evaluated using the leave-one-out cross validation technique. Although the evaluation did not find a large correspondence (in terms of
linear regression) between the interpolated and measured values, it was possible to capture the spatial variability of the process and
produce the cartography of this variable, with which future ecological analyses can be performed. |
url |
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/1005/2015/isprsarchives-XL-7-W3-1005-2015.pdf |
work_keys_str_mv |
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1725870929415766016 |