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...
- Autores:
- 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
- Rights
- License
- Abierto (Texto Completo)
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45081160085e7b75f-ff76-404b-a8ef-8a7adc3f89b3-1009cd4d4-a518-4659-8960-15674b53dba4-12020-08-19T14:43:28Z2020-08-19T14:43:28Z2012-06-01The 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.application/pdfhttps://doi.org/10.3390/rs4061781ISSN: 2072-4292https://repository.urosario.edu.co/handle/10336/27710engJapan Society of Photogrammetry and Remote Sensing1803No. 61782Remote SensingVol. 4Remote Sensing, ISSN: 2072-4292, Vol.4, No.6 (2012); pp. 1782-1803https://www.mdpi.com/2072-4292/4/6/1781Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Remote Sensinginstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURPhenologyNDVIRandom forestsMODISForest vegetationExploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categoriesExplorar el uso de indicadores fenológicos basados ??en MODIS NDVI para clasificar las categorías generales de hábitats forestalesarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Clerici, NicolaWeissteiner, Christof JGerard, FranceORIGINALremotesensing-04-01781.pdfapplication/pdf1366696https://repository.urosario.edu.co/bitstreams/c1d9bd16-8b93-4e3a-895c-50fbe3408f28/downloadc6b7eb1650209778bc1dbe762a172a13MD51TEXTremotesensing-04-01781.pdf.txtremotesensing-04-01781.pdf.txtExtracted texttext/plain57776https://repository.urosario.edu.co/bitstreams/8bc7f938-c7ae-4096-aae3-620631976ee1/downloade80b0b1d40aebfc7e4381aa5790e7fdeMD52THUMBNAILremotesensing-04-01781.pdf.jpgremotesensing-04-01781.pdf.jpgGenerated Thumbnailimage/jpeg4948https://repository.urosario.edu.co/bitstreams/a5ef998f-74bb-48c2-ba23-7fc102516883/downloadbdcfe6876b292201431f5aa8c4ad53c7MD5310336/27710oai:repository.urosario.edu.co:10336/277102022-05-02 07:37:21.922862https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co |
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 |
dc.date.available.none.fl_str_mv |
2020-08-19T14:43:28Z |
dc.type.eng.fl_str_mv |
article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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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/10.3390/rs4061781 |
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 |
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eng |
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1803 |
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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http://purl.org/coar/access_right/c_abf2 |
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Abierto (Texto Completo) |
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Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2 |
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Japan Society of Photogrammetry and Remote Sensing |
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Remote Sensing |
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Universidad del Rosario |
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