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
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/22624
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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)2025-10-23T11:19:34Zoai: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:l2025-10-23 11:19:35.252INTA 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 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier |
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Elsevier |
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Watershed ecology and the Environment 7 : 144-158 (2025) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
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INTA Digital (INTA) |
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INTA Digital (INTA) |
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Instituto Nacional de Tecnología Agropecuaria |
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INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
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tripaldi.nicolas@inta.gob.ar |
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1846787605526478848 |
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12.982451 |