Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Autores
Hardtke, Leonardo Andrés; Blanco, Paula Daniela; del Valle, Hector Francisco; Metternicht, Graciela; Sione, Walter Fabian
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land  (e.g. ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer  (MODIS) and the Satellite Pour l?Observationde la Terre (SPOT) are available to this end.  However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the Normalized Burned Ratio Index products  derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas) , and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-areareference data was used for validation purposes. Additionally, the performance of the
adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area  ( MCD45A1) , the active fire algorithm  ( MOD14) ; and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R² =0.96) . Our findings confirm prior research calling for caution when using the global fire products locally or regionally.
Fil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Metternicht, Graciela. University of New South Wales; Australia
Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina. Universidad Nacional de Luján; Argentina
Materia
Bushfires
Burned Area
Time Series
Image Segmentation
Modis
Normalized Burn Ratio
Rangelands
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/39346

id CONICETDig_0832f510c93ec1ecd7ebeb764b5a70b6
oai_identifier_str oai:ri.conicet.gov.ar:11336/39346
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imageryHardtke, Leonardo AndrésBlanco, Paula Danieladel Valle, Hector FranciscoMetternicht, GracielaSione, Walter FabianBushfiresBurned AreaTime SeriesImage SegmentationModisNormalized Burn RatioRangelandshttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land  (e.g. ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer  (MODIS) and the Satellite Pour l?Observationde la Terre (SPOT) are available to this end.  However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the Normalized Burned Ratio Index products  derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas) , and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-areareference data was used for validation purposes. Additionally, the performance of the<br />adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area  ( MCD45A1) , the active fire algorithm  ( MOD14) ; and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R² =0.96) . Our findings confirm prior research calling for caution when using the global fire products locally or regionally.Fil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Metternicht, Graciela. University of New South Wales; AustraliaFil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina. Universidad Nacional de Luján; ArgentinaElsevier Science2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/39346Hardtke, Leonardo Andrés; Blanco, Paula Daniela; del Valle, Hector Francisco; Metternicht, Graciela; Sione, Walter Fabian; Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery; Elsevier Science; Itc Journal; 38; 12-2015; 25-350303-2434CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jag.2014.11.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S030324341400261Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:53:15Zoai:ri.conicet.gov.ar:11336/39346instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:53:15.996CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
title Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
spellingShingle Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
Hardtke, Leonardo Andrés
Bushfires
Burned Area
Time Series
Image Segmentation
Modis
Normalized Burn Ratio
Rangelands
title_short Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
title_full Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
title_fullStr Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
title_full_unstemmed Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
title_sort Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery
dc.creator.none.fl_str_mv Hardtke, Leonardo Andrés
Blanco, Paula Daniela
del Valle, Hector Francisco
Metternicht, Graciela
Sione, Walter Fabian
author Hardtke, Leonardo Andrés
author_facet Hardtke, Leonardo Andrés
Blanco, Paula Daniela
del Valle, Hector Francisco
Metternicht, Graciela
Sione, Walter Fabian
author_role author
author2 Blanco, Paula Daniela
del Valle, Hector Francisco
Metternicht, Graciela
Sione, Walter Fabian
author2_role author
author
author
author
dc.subject.none.fl_str_mv Bushfires
Burned Area
Time Series
Image Segmentation
Modis
Normalized Burn Ratio
Rangelands
topic Bushfires
Burned Area
Time Series
Image Segmentation
Modis
Normalized Burn Ratio
Rangelands
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land  (e.g. ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer  (MODIS) and the Satellite Pour l?Observationde la Terre (SPOT) are available to this end.  However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the Normalized Burned Ratio Index products  derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas) , and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-areareference data was used for validation purposes. Additionally, the performance of the<br />adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area  ( MCD45A1) , the active fire algorithm  ( MOD14) ; and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R² =0.96) . Our findings confirm prior research calling for caution when using the global fire products locally or regionally.
Fil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Metternicht, Graciela. University of New South Wales; Australia
Fil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos; Argentina. Universidad Nacional de Luján; Argentina
description Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land  (e.g. ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer  (MODIS) and the Satellite Pour l?Observationde la Terre (SPOT) are available to this end.  However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km² in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the Normalized Burned Ratio Index products  derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas) , and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-areareference data was used for validation purposes. Additionally, the performance of the<br />adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area  ( MCD45A1) , the active fire algorithm  ( MOD14) ; and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R² = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R² =0.96) . Our findings confirm prior research calling for caution when using the global fire products locally or regionally.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/39346
Hardtke, Leonardo Andrés; Blanco, Paula Daniela; del Valle, Hector Francisco; Metternicht, Graciela; Sione, Walter Fabian; Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery; Elsevier Science; Itc Journal; 38; 12-2015; 25-35
0303-2434
CONICET Digital
CONICET
url http://hdl.handle.net/11336/39346
identifier_str_mv Hardtke, Leonardo Andrés; Blanco, Paula Daniela; del Valle, Hector Francisco; Metternicht, Graciela; Sione, Walter Fabian; Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery; Elsevier Science; Itc Journal; 38; 12-2015; 25-35
0303-2434
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jag.2014.11.011
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S030324341400261X
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613628933701632
score 13.070432