Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina
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...
| Autores principales: | , , , , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
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MDPI
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/21492 https://www.mdpi.com/2072-4292/17/5/748 https://doi.org/10.3390/rs17050748 |
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| author | Alaggia, Francisco Guillermo Innangi, Michele Cavallero, Laura Lopez, Dardo Ruben Pontieri, Federica Marzialetti, Flavio Riera-Tatche, Ramon Gamba, Paolo Carranza, María Laura |
| author_browse | Alaggia, Francisco Guillermo Carranza, María Laura Cavallero, Laura Gamba, Paolo Innangi, Michele Lopez, Dardo Ruben Marzialetti, Flavio Pontieri, Federica Riera-Tatche, Ramon |
| 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_sort | Alaggia, Francisco Guillermo |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA21492 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA214922025-02-27T13:01:09Z Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina Alaggia, Francisco Guillermo Innangi, Michele Cavallero, Laura Lopez, Dardo Ruben Pontieri, Federica Marzialetti, Flavio Riera-Tatche, Ramon Gamba, Paolo Carranza, María Laura Bosque Tropical Bosque Seco Clorofila Tropical Forests Dry Forests Chlorophylls Remote Sensing Teledetección Región Gran Chaco, Argentina 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 2025-02-27T12:47:50Z 2025-02-27T12:47:50Z 2025-02-21 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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 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) application/pdf MDPI Remote Sensing 17 (5) : 748 (February 2025) |
| spellingShingle | Bosque Tropical Bosque Seco Clorofila Tropical Forests Dry Forests Chlorophylls Remote Sensing Teledetección Región Gran Chaco, Argentina Alaggia, Francisco Guillermo Innangi, Michele Cavallero, Laura Lopez, Dardo Ruben Pontieri, Federica Marzialetti, Flavio Riera-Tatche, Ramon Gamba, Paolo Carranza, María Laura 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 |
| 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_short | 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 |
| topic | Bosque Tropical Bosque Seco Clorofila Tropical Forests Dry Forests Chlorophylls Remote Sensing Teledetección Región Gran Chaco, Argentina |
| url | http://hdl.handle.net/20.500.12123/21492 https://www.mdpi.com/2072-4292/17/5/748 https://doi.org/10.3390/rs17050748 |
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