Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories

The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Ob...

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Tipo de recurso:
Fecha de publicación:
2012
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/27710
Acceso en línea:
https://doi.org/10.3390/rs4061781
https://repository.urosario.edu.co/handle/10336/27710
Palabra clave:
Phenology
NDVI
Random forests
MODIS
Forest vegetation
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Abierto (Texto Completo)
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dc.title.spa.fl_str_mv Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
dc.title.TranslatedTitle.spa.fl_str_mv Explorar el uso de indicadores fenológicos basados ??en MODIS NDVI para clasificar las categorías generales de hábitats forestales
title Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
spellingShingle Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
Phenology
NDVI
Random forests
MODIS
Forest vegetation
title_short Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
title_full Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
title_fullStr Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
title_full_unstemmed Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
title_sort Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories
dc.subject.keyword.spa.fl_str_mv Phenology
NDVI
Random forests
MODIS
Forest vegetation
topic Phenology
NDVI
Random forests
MODIS
Forest vegetation
description The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77-82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach.
publishDate 2012
dc.date.created.spa.fl_str_mv 2012-06-01
dc.date.accessioned.none.fl_str_mv 2020-08-19T14:43:28Z
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dc.type.eng.fl_str_mv article
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dc.type.spa.spa.fl_str_mv Artículo
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dc.identifier.issn.none.fl_str_mv ISSN: 2072-4292
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/27710
url https://doi.org/10.3390/rs4061781
https://repository.urosario.edu.co/handle/10336/27710
identifier_str_mv ISSN: 2072-4292
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.relation.citationIssue.none.fl_str_mv No. 6
dc.relation.citationStartPage.none.fl_str_mv 1782
dc.relation.citationTitle.none.fl_str_mv Remote Sensing
dc.relation.citationVolume.none.fl_str_mv Vol. 4
dc.relation.ispartof.spa.fl_str_mv Remote Sensing, ISSN: 2072-4292, Vol.4, No.6 (2012); pp. 1782-1803
dc.relation.uri.spa.fl_str_mv https://www.mdpi.com/2072-4292/4/6/1781
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rights_invalid_str_mv Abierto (Texto Completo)
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dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Japan Society of Photogrammetry and Remote Sensing
dc.source.spa.fl_str_mv Remote Sensing
institution Universidad del Rosario
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dc.source.reponame.none.fl_str_mv reponame:Repositorio Institucional EdocUR
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