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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/39346
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/39346 |
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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 |
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1844613628933701632 |
score |
13.070432 |