Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring

Autores
Navarro, María Fabiana; Calamari, Noelia Cecilia; Navarro, Carlos Saúl; Enriquez, Andrea Soledad; Mosciaro, Maria Jesus; Saucedo, Griselda Isabel; Barrios, Raúl Ariel; Curcio, Matías Hernán; Dieta, Victorio; Garcia Martinez, Guillermo Carlos; Iturralde Elortegui, Maria Del Rosario Ma; Michard, Nicole Jacqueline; Paredes, Paula Natalia; Umaña, Fernando; Alday Poblete, Silvina Esther; Pezzola, Nestor Alejandro; Vidal, Claudia; Winschel, Cristina Ines; Albarracin Franco, Silvia; Behr, Santiago Javier; Cianfagna, Francisco A.; Cremona, Maria Victoria; Alvarenga, Fernando Agustin; Perucca, Alba Ruth; Lopez, Astor Emilio; Miranda, Federico Waldemar; Kurtz, Ditmar Bernardo
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wetlands, covering 7 % of Earth’s surface, are crucial for providing ecosystem services and regulating climate change. Despite their importance, global fluctuations in wetland distribution highlight the need for accurate and comprehensive mapping to address current and future challenges. In Argentina, a lack of detailed knowledge about wetland distribution, extent, and dynamics impedes effective conservation and management efforts. This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. Our model achieved an accuracy of 89.3 %, effectively identifying wetland areas and delineating “elasticity” zones that reveal temporal wetland behavior. Approximately 9.5 % of Argentina is classified as wetlands, with the Chaco-Mesopotamia region accounting for 43 % of these areas. The performance of the 42 assessed variables varied across macro-regions, highlighting the necessity for regionspecific classification methods. In the Andean region, variables such as the Digital Elevation Model (DEM) and Topographic Wetness Index (TWI) were key predictors, while in the plains, spectral properties including vegetation and water content indices were more significant. Despite challenges in classifying irrigated areas, the model demonstrated considerable robustness. This study not only enhances our understanding of wetland dy­ namics but also provides insights into how different regions respond to various environmental factors, offering a more nuanced perspective on wetland behavior. These findings pave the way for refined conservation strategies and further research into the impacts of climate change and land use on wetland ecosystems. The precision, scalability, and representation of wetland elasticity emphasize its importance for decision-making and provide a crucial baseline for future research amid ongoing environmental changes.
Instituto de Suelos
Fil: Navarro Rau, María Fabiana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina
Fil: Navarro, Carlos S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.
Fil: Mosciaro, Maria Jesus. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina.
Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Barrios, Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agroforestal Esquel; Argentina
Fil: Dieta, Victorio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Delta del Paraná. Agencia De Extensión Rural Delta Frontal; Argentina
Fil: Garcia Martinez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Iturralde Elortegui, María del Rosario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Olavarría; Argentina.
Fil: Michard, Nicole Jacqueline. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Paredes, Paula Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Umaña, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Laboratorio de Teledetección; Argentina
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Vidal, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Albarracin Franco, Silvia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina
Fil: Behr, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Cianfagna, Francisco A. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cremona, Maria Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Grupo Suelos, Agua y Ambiente; Argentina
Fil: Alvarenga, F.A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina
Fil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Lopez, Astor Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Sáenz Peña; Argentina
Fil: Miranda, Federico Waldemar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria El Colorado. Agencia de Extensión Rural Formosa; Argentina
Fil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.
Fil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Alday, Silvina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.
Fuente
Watershed ecology and the Environment 7 : 144-158 (2025)
Materia
Tierras Húmedas
Ecosistema
Sostenibilidad
Teledetección
Argentina
Wetlands
Ecosystems
Sustainability
Remote Sensing
Humedales
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
oai:localhost:20.500.12123/22624

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spelling Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoringNavarro, María FabianaCalamari, Noelia CeciliaNavarro, Carlos SaúlEnriquez, Andrea SoledadMosciaro, Maria JesusSaucedo, Griselda IsabelBarrios, Raúl ArielCurcio, Matías HernánDieta, VictorioGarcia Martinez, Guillermo CarlosIturralde Elortegui, Maria Del Rosario MaMichard, Nicole JacquelineParedes, Paula NataliaUmaña, FernandoAlday Poblete, Silvina EstherPezzola, Nestor AlejandroVidal, ClaudiaWinschel, Cristina InesAlbarracin Franco, SilviaBehr, Santiago JavierCianfagna, Francisco A.Cremona, Maria VictoriaAlvarenga, Fernando AgustinPerucca, Alba RuthLopez, Astor EmilioMiranda, Federico WaldemarKurtz, Ditmar BernardoTierras HúmedasEcosistemaSostenibilidadTeledetecciónArgentinaWetlandsEcosystemsSustainabilityRemote SensingHumedalesWetlands, covering 7 % of Earth’s surface, are crucial for providing ecosystem services and regulating climate change. Despite their importance, global fluctuations in wetland distribution highlight the need for accurate and comprehensive mapping to address current and future challenges. In Argentina, a lack of detailed knowledge about wetland distribution, extent, and dynamics impedes effective conservation and management efforts. This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. Our model achieved an accuracy of 89.3 %, effectively identifying wetland areas and delineating “elasticity” zones that reveal temporal wetland behavior. Approximately 9.5 % of Argentina is classified as wetlands, with the Chaco-Mesopotamia region accounting for 43 % of these areas. The performance of the 42 assessed variables varied across macro-regions, highlighting the necessity for regionspecific classification methods. In the Andean region, variables such as the Digital Elevation Model (DEM) and Topographic Wetness Index (TWI) were key predictors, while in the plains, spectral properties including vegetation and water content indices were more significant. Despite challenges in classifying irrigated areas, the model demonstrated considerable robustness. This study not only enhances our understanding of wetland dy­ namics but also provides insights into how different regions respond to various environmental factors, offering a more nuanced perspective on wetland behavior. These findings pave the way for refined conservation strategies and further research into the impacts of climate change and land use on wetland ecosystems. The precision, scalability, and representation of wetland elasticity emphasize its importance for decision-making and provide a crucial baseline for future research amid ongoing environmental changes.Instituto de SuelosFil: Navarro Rau, María Fabiana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Navarro, Carlos S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.Fil: Mosciaro, Maria Jesus. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina.Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Barrios, Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agroforestal Esquel; ArgentinaFil: Dieta, Victorio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Delta del Paraná. Agencia De Extensión Rural Delta Frontal; ArgentinaFil: Garcia Martinez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; ArgentinaFil: Iturralde Elortegui, María del Rosario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Olavarría; Argentina.Fil: Michard, Nicole Jacqueline. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Paredes, Paula Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Umaña, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Laboratorio de Teledetección; ArgentinaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Vidal, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Albarracin Franco, Silvia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Behr, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; ArgentinaFil: Cianfagna, Francisco A. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cremona, Maria Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Grupo Suelos, Agua y Ambiente; ArgentinaFil: Alvarenga, F.A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Lopez, Astor Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Sáenz Peña; ArgentinaFil: Miranda, Federico Waldemar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria El Colorado. Agencia de Extensión Rural Formosa; ArgentinaFil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Alday, Silvina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Elsevier2025-06-11T10:41:53Z2025-06-11T10:41:53Z2025-04-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/22624https://www.sciencedirect.com/science/article/pii/S25894714250001302598-4714https://doi.org/10.1016/j.wsee.2025.04.001Watershed ecology and the Environment 7 : 144-158 (2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2019-PD-E2-I506-002, Humedales de la República Argentina: distribución, usos y recomendaciones coparticipativas para una producción sustentableinfo: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)2025-09-29T13:47:21Zoai:localhost:20.500.12123/22624instacron: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 13:47:21.422INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
title Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
spellingShingle Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
Navarro, María Fabiana
Tierras Húmedas
Ecosistema
Sostenibilidad
Teledetección
Argentina
Wetlands
Ecosystems
Sustainability
Remote Sensing
Humedales
title_short Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
title_full Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
title_fullStr Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
title_full_unstemmed Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
title_sort Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
dc.creator.none.fl_str_mv Navarro, María Fabiana
Calamari, Noelia Cecilia
Navarro, Carlos Saúl
Enriquez, Andrea Soledad
Mosciaro, Maria Jesus
Saucedo, Griselda Isabel
Barrios, Raúl Ariel
Curcio, Matías Hernán
Dieta, Victorio
Garcia Martinez, Guillermo Carlos
Iturralde Elortegui, Maria Del Rosario Ma
Michard, Nicole Jacqueline
Paredes, Paula Natalia
Umaña, Fernando
Alday Poblete, Silvina Esther
Pezzola, Nestor Alejandro
Vidal, Claudia
Winschel, Cristina Ines
Albarracin Franco, Silvia
Behr, Santiago Javier
Cianfagna, Francisco A.
Cremona, Maria Victoria
Alvarenga, Fernando Agustin
Perucca, Alba Ruth
Lopez, Astor Emilio
Miranda, Federico Waldemar
Kurtz, Ditmar Bernardo
author Navarro, María Fabiana
author_facet Navarro, María Fabiana
Calamari, Noelia Cecilia
Navarro, Carlos Saúl
Enriquez, Andrea Soledad
Mosciaro, Maria Jesus
Saucedo, Griselda Isabel
Barrios, Raúl Ariel
Curcio, Matías Hernán
Dieta, Victorio
Garcia Martinez, Guillermo Carlos
Iturralde Elortegui, Maria Del Rosario Ma
Michard, Nicole Jacqueline
Paredes, Paula Natalia
Umaña, Fernando
Alday Poblete, Silvina Esther
Pezzola, Nestor Alejandro
Vidal, Claudia
Winschel, Cristina Ines
Albarracin Franco, Silvia
Behr, Santiago Javier
Cianfagna, Francisco A.
Cremona, Maria Victoria
Alvarenga, Fernando Agustin
Perucca, Alba Ruth
Lopez, Astor Emilio
Miranda, Federico Waldemar
Kurtz, Ditmar Bernardo
author_role author
author2 Calamari, Noelia Cecilia
Navarro, Carlos Saúl
Enriquez, Andrea Soledad
Mosciaro, Maria Jesus
Saucedo, Griselda Isabel
Barrios, Raúl Ariel
Curcio, Matías Hernán
Dieta, Victorio
Garcia Martinez, Guillermo Carlos
Iturralde Elortegui, Maria Del Rosario Ma
Michard, Nicole Jacqueline
Paredes, Paula Natalia
Umaña, Fernando
Alday Poblete, Silvina Esther
Pezzola, Nestor Alejandro
Vidal, Claudia
Winschel, Cristina Ines
Albarracin Franco, Silvia
Behr, Santiago Javier
Cianfagna, Francisco A.
Cremona, Maria Victoria
Alvarenga, Fernando Agustin
Perucca, Alba Ruth
Lopez, Astor Emilio
Miranda, Federico Waldemar
Kurtz, Ditmar Bernardo
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Tierras Húmedas
Ecosistema
Sostenibilidad
Teledetección
Argentina
Wetlands
Ecosystems
Sustainability
Remote Sensing
Humedales
topic Tierras Húmedas
Ecosistema
Sostenibilidad
Teledetección
Argentina
Wetlands
Ecosystems
Sustainability
Remote Sensing
Humedales
dc.description.none.fl_txt_mv Wetlands, covering 7 % of Earth’s surface, are crucial for providing ecosystem services and regulating climate change. Despite their importance, global fluctuations in wetland distribution highlight the need for accurate and comprehensive mapping to address current and future challenges. In Argentina, a lack of detailed knowledge about wetland distribution, extent, and dynamics impedes effective conservation and management efforts. This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. Our model achieved an accuracy of 89.3 %, effectively identifying wetland areas and delineating “elasticity” zones that reveal temporal wetland behavior. Approximately 9.5 % of Argentina is classified as wetlands, with the Chaco-Mesopotamia region accounting for 43 % of these areas. The performance of the 42 assessed variables varied across macro-regions, highlighting the necessity for regionspecific classification methods. In the Andean region, variables such as the Digital Elevation Model (DEM) and Topographic Wetness Index (TWI) were key predictors, while in the plains, spectral properties including vegetation and water content indices were more significant. Despite challenges in classifying irrigated areas, the model demonstrated considerable robustness. This study not only enhances our understanding of wetland dy­ namics but also provides insights into how different regions respond to various environmental factors, offering a more nuanced perspective on wetland behavior. These findings pave the way for refined conservation strategies and further research into the impacts of climate change and land use on wetland ecosystems. The precision, scalability, and representation of wetland elasticity emphasize its importance for decision-making and provide a crucial baseline for future research amid ongoing environmental changes.
Instituto de Suelos
Fil: Navarro Rau, María Fabiana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina
Fil: Navarro, Carlos S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.
Fil: Mosciaro, Maria Jesus. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina.
Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Barrios, Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agroforestal Esquel; Argentina
Fil: Dieta, Victorio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Delta del Paraná. Agencia De Extensión Rural Delta Frontal; Argentina
Fil: Garcia Martinez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Iturralde Elortegui, María del Rosario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Olavarría; Argentina.
Fil: Michard, Nicole Jacqueline. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina
Fil: Paredes, Paula Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.
Fil: Umaña, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Laboratorio de Teledetección; Argentina
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Vidal, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Albarracin Franco, Silvia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina
Fil: Behr, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Cianfagna, Francisco A. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cremona, Maria Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Grupo Suelos, Agua y Ambiente; Argentina
Fil: Alvarenga, F.A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina
Fil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina
Fil: Lopez, Astor Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Sáenz Peña; Argentina
Fil: Miranda, Federico Waldemar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria El Colorado. Agencia de Extensión Rural Formosa; Argentina
Fil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.
Fil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Alday, Silvina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.
description Wetlands, covering 7 % of Earth’s surface, are crucial for providing ecosystem services and regulating climate change. Despite their importance, global fluctuations in wetland distribution highlight the need for accurate and comprehensive mapping to address current and future challenges. In Argentina, a lack of detailed knowledge about wetland distribution, extent, and dynamics impedes effective conservation and management efforts. This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. Our model achieved an accuracy of 89.3 %, effectively identifying wetland areas and delineating “elasticity” zones that reveal temporal wetland behavior. Approximately 9.5 % of Argentina is classified as wetlands, with the Chaco-Mesopotamia region accounting for 43 % of these areas. The performance of the 42 assessed variables varied across macro-regions, highlighting the necessity for regionspecific classification methods. In the Andean region, variables such as the Digital Elevation Model (DEM) and Topographic Wetness Index (TWI) were key predictors, while in the plains, spectral properties including vegetation and water content indices were more significant. Despite challenges in classifying irrigated areas, the model demonstrated considerable robustness. This study not only enhances our understanding of wetland dy­ namics but also provides insights into how different regions respond to various environmental factors, offering a more nuanced perspective on wetland behavior. These findings pave the way for refined conservation strategies and further research into the impacts of climate change and land use on wetland ecosystems. The precision, scalability, and representation of wetland elasticity emphasize its importance for decision-making and provide a crucial baseline for future research amid ongoing environmental changes.
publishDate 2025
dc.date.none.fl_str_mv 2025-06-11T10:41:53Z
2025-06-11T10:41:53Z
2025-04-04
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/20.500.12123/22624
https://www.sciencedirect.com/science/article/pii/S2589471425000130
2598-4714
https://doi.org/10.1016/j.wsee.2025.04.001
url http://hdl.handle.net/20.500.12123/22624
https://www.sciencedirect.com/science/article/pii/S2589471425000130
https://doi.org/10.1016/j.wsee.2025.04.001
identifier_str_mv 2598-4714
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2019-PD-E2-I506-002, Humedales de la República Argentina: distribución, usos y recomendaciones coparticipativas para una producción sustentable
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Watershed ecology and the Environment 7 : 144-158 (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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