Analyzing fine-scale wetland composition using high resolution imagery and texture features
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2013
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/26962
- Acceso en línea:
- https://doi.org/10.1016/j.jag.2013.01.003
https://repository.urosario.edu.co/handle/10336/26962
- Palabra clave:
- Wetland mapping
High resolution imagery
Image texture
Support Vector Machine
- Rights
- License
- Restringido (Acceso a grupos específicos)
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oai:repository.urosario.edu.co:10336/26962 |
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EDOCUR2 |
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Repositorio EdocUR - U. Rosario |
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b8420d3d-ae5e-4153-9ef6-a87a2a6e2bc9-19ae99ce8-fc5b-408a-9ab0-eab12cb36c9e-182d396e1-afc0-4022-a6a9-50d6e8030bf6-1c0a2c37a-ed2c-442b-957b-a93788108155-1dd96ba5b-574e-45f3-a9e7-47e4c4a4385d-12020-08-19T14:40:37Z2020-08-19T14:40:37Z2013-01-01In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).application/pdfhttps://doi.org/10.1016/j.jag.2013.01.003ISSN: 0303-2434https://repository.urosario.edu.co/handle/10336/26962engElsevier212204International Journal of Applied Earth Observation and GeoinformationVol. 23International Journal of Applied Earth Observation and Geoinformation, ISSN: 0303-2434, Vol.23 (August, 2013); pp. 204-212https://www.sciencedirect.com/science/article/abs/pii/S0303243413000135Restringido (Acceso a grupos específicos)http://purl.org/coar/access_right/c_16ecInternational Journal of Applied Earth Observation and Geoinformationinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURWetland mappingHigh resolution imageryImage textureSupport Vector MachineAnalyzing fine-scale wetland composition using high resolution imagery and texture featuresAnálisis de la composición de humedales a escala fina utilizando imágenes de alta resolución y características de texturaarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Szantoi, ZoltanEscobedo, FranciscoAbd-Elrahman, AmrSmith, ScotPearlstine, Leonard10336/26962oai:repository.urosario.edu.co:10336/269622021-06-03 00:50:03.163https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
dc.title.spa.fl_str_mv |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
dc.title.TranslatedTitle.spa.fl_str_mv |
Análisis de la composición de humedales a escala fina utilizando imágenes de alta resolución y características de textura |
title |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
spellingShingle |
Analyzing fine-scale wetland composition using high resolution imagery and texture features Wetland mapping High resolution imagery Image texture Support Vector Machine |
title_short |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
title_full |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
title_fullStr |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
title_full_unstemmed |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
title_sort |
Analyzing fine-scale wetland composition using high resolution imagery and texture features |
dc.subject.keyword.spa.fl_str_mv |
Wetland mapping High resolution imagery Image texture Support Vector Machine |
topic |
Wetland mapping High resolution imagery Image texture Support Vector Machine |
description |
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features). |
publishDate |
2013 |
dc.date.created.spa.fl_str_mv |
2013-01-01 |
dc.date.accessioned.none.fl_str_mv |
2020-08-19T14:40:37Z |
dc.date.available.none.fl_str_mv |
2020-08-19T14:40:37Z |
dc.type.eng.fl_str_mv |
article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.spa.spa.fl_str_mv |
Artículo |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1016/j.jag.2013.01.003 |
dc.identifier.issn.none.fl_str_mv |
ISSN: 0303-2434 |
dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/26962 |
url |
https://doi.org/10.1016/j.jag.2013.01.003 https://repository.urosario.edu.co/handle/10336/26962 |
identifier_str_mv |
ISSN: 0303-2434 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationEndPage.none.fl_str_mv |
212 |
dc.relation.citationStartPage.none.fl_str_mv |
204 |
dc.relation.citationTitle.none.fl_str_mv |
International Journal of Applied Earth Observation and Geoinformation |
dc.relation.citationVolume.none.fl_str_mv |
Vol. 23 |
dc.relation.ispartof.spa.fl_str_mv |
International Journal of Applied Earth Observation and Geoinformation, ISSN: 0303-2434, Vol.23 (August, 2013); pp. 204-212 |
dc.relation.uri.spa.fl_str_mv |
https://www.sciencedirect.com/science/article/abs/pii/S0303243413000135 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.acceso.spa.fl_str_mv |
Restringido (Acceso a grupos específicos) |
rights_invalid_str_mv |
Restringido (Acceso a grupos específicos) http://purl.org/coar/access_right/c_16ec |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Elsevier |
dc.source.spa.fl_str_mv |
International Journal of Applied Earth Observation and Geoinformation |
institution |
Universidad del Rosario |
dc.source.instname.none.fl_str_mv |
instname:Universidad del Rosario |
dc.source.reponame.none.fl_str_mv |
reponame:Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
Repositorio institucional EdocUR |
repository.mail.fl_str_mv |
edocur@urosario.edu.co |
_version_ |
1828160632189353984 |