Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina

Autores
Alaggia, Francisco Guillermo; Innangi, Michele; Cavallero, Laura; Lopez, Dardo Ruben; Pontieri, Federica; Marzialetti, Flavio; Riera-Tatche, Ramon; Gamba, Paolo; Carranza, María Laura
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Anthropogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN’s 15th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancaní Provincial Reserve and surrounding areas of theWest Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWIGao (Normalized Difference Water Index), and MCARISent (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.
EEA Manfredi
Fil: Alaggia, Francisco G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.
Fil: Innangi. Michele. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Cavallero, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; Argentina
Fil: López, Dardo Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; Argentina
Fil: Pontieri, Federica. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Marzialetti, Flavio. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; Italia
Fil: Riera-Tatche, Ramon. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; Italia
Fil: Gamba, Paolo. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; Italia
Fil: Carranza, María Laura. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Carranza, María Laura. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; Italia
Fuente
Remote Sensing 17 (5) : 748 (February 2025)
Materia
Bosque Tropical
Bosque Seco
Clorofila
Tropical Forests
Dry Forests
Chlorophylls
Remote Sensing
Teledetección
Región Gran Chaco, Argentina
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
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oai_identifier_str oai:localhost:20.500.12123/21492
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spelling Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central ArgentinaAlaggia, Francisco GuillermoInnangi, MicheleCavallero, LauraLopez, Dardo RubenPontieri, FedericaMarzialetti, FlavioRiera-Tatche, RamonGamba, PaoloCarranza, María LauraBosque TropicalBosque SecoClorofilaTropical ForestsDry ForestsChlorophyllsRemote SensingTeledetecciónRegión Gran Chaco, ArgentinaAnthropogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN’s 15th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancaní Provincial Reserve and surrounding areas of theWest Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWIGao (Normalized Difference Water Index), and MCARISent (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.EEA ManfrediFil: Alaggia, Francisco G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Innangi. Michele. University of Molise. Department of Biosciences and Territory. EnviXLab; ItaliaFil: Cavallero, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; ArgentinaFil: López, Dardo Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; ArgentinaFil: Pontieri, Federica. University of Molise. Department of Biosciences and Territory. EnviXLab; ItaliaFil: Marzialetti, Flavio. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; ItaliaFil: Riera-Tatche, Ramon. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; ItaliaFil: Gamba, Paolo. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; ItaliaFil: Carranza, María Laura. University of Molise. Department of Biosciences and Territory. EnviXLab; ItaliaFil: Carranza, María Laura. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; ItaliaMDPI2025-02-27T12:47:50Z2025-02-27T12:47:50Z2025-02-21info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21492https://www.mdpi.com/2072-4292/17/5/7482072-4292https://doi.org/10.3390/rs17050748Remote Sensing 17 (5) : 748 (February 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2023-PD-L02-I091, Adaptación a la variabilidad y al cambio global: herramientas para la gestión de riesgos, la reducción de impactos y el aumento de la resiliencia de socioecosistemasinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:47:10Zoai:localhost:20.500.12123/21492instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:47:10.449INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
title Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
spellingShingle Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
Alaggia, Francisco Guillermo
Bosque Tropical
Bosque Seco
Clorofila
Tropical Forests
Dry Forests
Chlorophylls
Remote Sensing
Teledetección
Región Gran Chaco, Argentina
title_short Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
title_full Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
title_fullStr Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
title_full_unstemmed Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
title_sort Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
dc.creator.none.fl_str_mv Alaggia, Francisco Guillermo
Innangi, Michele
Cavallero, Laura
Lopez, Dardo Ruben
Pontieri, Federica
Marzialetti, Flavio
Riera-Tatche, Ramon
Gamba, Paolo
Carranza, María Laura
author Alaggia, Francisco Guillermo
author_facet Alaggia, Francisco Guillermo
Innangi, Michele
Cavallero, Laura
Lopez, Dardo Ruben
Pontieri, Federica
Marzialetti, Flavio
Riera-Tatche, Ramon
Gamba, Paolo
Carranza, María Laura
author_role author
author2 Innangi, Michele
Cavallero, Laura
Lopez, Dardo Ruben
Pontieri, Federica
Marzialetti, Flavio
Riera-Tatche, Ramon
Gamba, Paolo
Carranza, María Laura
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Bosque Tropical
Bosque Seco
Clorofila
Tropical Forests
Dry Forests
Chlorophylls
Remote Sensing
Teledetección
Región Gran Chaco, Argentina
topic Bosque Tropical
Bosque Seco
Clorofila
Tropical Forests
Dry Forests
Chlorophylls
Remote Sensing
Teledetección
Región Gran Chaco, Argentina
dc.description.none.fl_txt_mv Anthropogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN’s 15th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancaní Provincial Reserve and surrounding areas of theWest Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWIGao (Normalized Difference Water Index), and MCARISent (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.
EEA Manfredi
Fil: Alaggia, Francisco G. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.
Fil: Innangi. Michele. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Cavallero, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; Argentina
Fil: López, Dardo Rubén. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Villa Dolores; Argentina
Fil: Pontieri, Federica. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Marzialetti, Flavio. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; Italia
Fil: Riera-Tatche, Ramon. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; Italia
Fil: Gamba, Paolo. University of Pavia. Department of Electrical, Biomedical and Computer Engineering; Italia
Fil: Carranza, María Laura. University of Molise. Department of Biosciences and Territory. EnviXLab; Italia
Fil: Carranza, María Laura. University of Sassari. National Biodiversity Future Center. Department of Agricultural Sciences; Italia
description Anthropogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN’s 15th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancaní Provincial Reserve and surrounding areas of theWest Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWIGao (Normalized Difference Water Index), and MCARISent (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.
publishDate 2025
dc.date.none.fl_str_mv 2025-02-27T12:47:50Z
2025-02-27T12:47:50Z
2025-02-21
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/20.500.12123/21492
https://www.mdpi.com/2072-4292/17/5/748
2072-4292
https://doi.org/10.3390/rs17050748
url http://hdl.handle.net/20.500.12123/21492
https://www.mdpi.com/2072-4292/17/5/748
https://doi.org/10.3390/rs17050748
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PD-L02-I091, Adaptación a la variabilidad y al cambio global: herramientas para la gestión de riesgos, la reducción de impactos y el aumento de la resiliencia de socioecosistemas
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 17 (5) : 748 (February 2025)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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