Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
- Autores
- Navarro Rau, María F.; Calamari, Noelia Cecilia; Navarro, Carlos Saúl; Enriquez, Andrea Soledad; Mosciaro, María Jesús; Saucedo, Griselda Isabel; Barrios, Raul; Curcio, Matías Hernán; Dieta, Victorio José; García Martínez, Guillermo Carlos; Iturralde Elortegui, María del Rosario Margarita; Michard, Nicole J.; Paredes, Paula; Umaña, Fernando Javier; Alday Poblete, Silvina Esther; Pezzola, Nestor Alejandro; Vidal, Claudia; Winschel, Cristina Ines; Albarracin Franco, Silvia del Milagro; Behr, Santiago; Cianfagna, Francisco Andrés; Cremona, Maria V.; Alvarenga, Fernando; Perucca, Ruth; Lopez, Astor; Miranda, Federico; 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, integrating 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) geomorphology 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 region-specific 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 dynamics 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.
Fil: Navarro Rau, María F.. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Navarro, Carlos Saúl. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Reconquista.; Argentina
Fil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mosciaro, María Jesús. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta. Estación Experimental Agropecuaria Salta; Argentina
Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Fil: Barrios, Raul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina
Fil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Sur. Estación Experimental Agropecuaria Esquel; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dieta, Victorio José. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: García Martínez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina
Fil: Iturralde Elortegui, María del Rosario Margarita. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Michard, Nicole J.. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina
Fil: Paredes, Paula. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; Argentina
Fil: Umaña, Fernando Javier. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina
Fil: Alday Poblete, Silvina Esther. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Fil: Pezzola, Nestor Alejandro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Vidal, Claudia. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Garabato.; Argentina
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Albarracin Franco, Silvia del Milagro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Misiones. Estación Experimental Agropecuaria Cerro Azul; Argentina
Fil: Behr, Santiago. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; Argentina
Fil: Cianfagna, Francisco Andrés. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cremona, Maria V.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina
Fil: Alvarenga, Fernando. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Misiones. Estacion Experimental Agropecuaria Cerro Azul. Agencia de Extension Rural Apostoles; Argentina
Fil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina
Fil: Lopez, Astor. Instituto Nacional de Tecnología Agropecuaria; Argentina. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Chaco-formosa. Estacion Experimental Agropecuaria Saenz Peña. Agencia de Extension Rural Saenz Peña.; Argentina
Fil: Miranda, Federico. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Chaco-formosa; Argentina
Fil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina - Materia
-
Google earth engine
Random forest
Remote sensing
Wetland dynamic
Biophysical indices
Probabilistic modeling
Argentina - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/284610
Ver los metadatos del registro completo
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Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoringNavarro Rau, María F.Calamari, Noelia CeciliaNavarro, Carlos SaúlEnriquez, Andrea SoledadMosciaro, María JesúsSaucedo, Griselda IsabelBarrios, RaulCurcio, Matías HernánDieta, Victorio JoséGarcía Martínez, Guillermo CarlosIturralde Elortegui, María del Rosario MargaritaMichard, Nicole J.Paredes, PaulaUmaña, Fernando JavierAlday Poblete, Silvina EstherPezzola, Nestor AlejandroVidal, ClaudiaWinschel, Cristina InesAlbarracin Franco, Silvia del MilagroBehr, SantiagoCianfagna, Francisco AndrésCremona, Maria V.Alvarenga, FernandoPerucca, RuthLopez, AstorMiranda, FedericoKurtz, Ditmar BernardoGoogle earth engineRandom forestRemote sensingWetland dynamicBiophysical indicesProbabilistic modelingArgentinahttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Wetlands, 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, integrating 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) geomorphology 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 region-specific 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 dynamics 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.Fil: Navarro Rau, María F.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Navarro, Carlos Saúl. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Reconquista.; ArgentinaFil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mosciaro, María Jesús. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta. Estación Experimental Agropecuaria Salta; ArgentinaFil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Barrios, Raul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; ArgentinaFil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Sur. Estación Experimental Agropecuaria Esquel; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dieta, Victorio José. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: García Martínez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; ArgentinaFil: Iturralde Elortegui, María del Rosario Margarita. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Michard, Nicole J.. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Paredes, Paula. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; ArgentinaFil: Umaña, Fernando Javier. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Alday Poblete, Silvina Esther. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; ArgentinaFil: Pezzola, Nestor Alejandro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Vidal, Claudia. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Garabato.; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Albarracin Franco, Silvia del Milagro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Misiones. Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Behr, Santiago. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; ArgentinaFil: Cianfagna, Francisco Andrés. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cremona, Maria V.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Alvarenga, Fernando. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Misiones. Estacion Experimental Agropecuaria Cerro Azul. Agencia de Extension Rural Apostoles; ArgentinaFil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; ArgentinaFil: Lopez, Astor. Instituto Nacional de Tecnología Agropecuaria; Argentina. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Chaco-formosa. Estacion Experimental Agropecuaria Saenz Peña. Agencia de Extension Rural Saenz Peña.; ArgentinaFil: Miranda, Federico. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Chaco-formosa; ArgentinaFil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; ArgentinaKeAi Communications Co.2025-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/284610Navarro Rau, María F.; Calamari, Noelia Cecilia; Navarro, Carlos Saúl; Enriquez, Andrea Soledad; Mosciaro, María Jesús; et al.; Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring; KeAi Communications Co.; Watershed Ecology and the Environment; 7; 4-2025; 144-1582589-4714CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2589471425000130info:eu-repo/semantics/altIdentifier/doi/10.1016/j.wsee.2025.04.001info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:32:31Zoai:ri.conicet.gov.ar:11336/284610instacron: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:34982026-08-25 14:32:31.6CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| title |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| spellingShingle |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring Navarro Rau, María F. Google earth engine Random forest Remote sensing Wetland dynamic Biophysical indices Probabilistic modeling Argentina |
| title_short |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| title_full |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| title_fullStr |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| title_full_unstemmed |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| title_sort |
Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring |
| dc.creator.none.fl_str_mv |
Navarro Rau, María F. Calamari, Noelia Cecilia Navarro, Carlos Saúl Enriquez, Andrea Soledad Mosciaro, María Jesús Saucedo, Griselda Isabel Barrios, Raul Curcio, Matías Hernán Dieta, Victorio José García Martínez, Guillermo Carlos Iturralde Elortegui, María del Rosario Margarita Michard, Nicole J. Paredes, Paula Umaña, Fernando Javier Alday Poblete, Silvina Esther Pezzola, Nestor Alejandro Vidal, Claudia Winschel, Cristina Ines Albarracin Franco, Silvia del Milagro Behr, Santiago Cianfagna, Francisco Andrés Cremona, Maria V. Alvarenga, Fernando Perucca, Ruth Lopez, Astor Miranda, Federico Kurtz, Ditmar Bernardo |
| author |
Navarro Rau, María F. |
| author_facet |
Navarro Rau, María F. Calamari, Noelia Cecilia Navarro, Carlos Saúl Enriquez, Andrea Soledad Mosciaro, María Jesús Saucedo, Griselda Isabel Barrios, Raul Curcio, Matías Hernán Dieta, Victorio José García Martínez, Guillermo Carlos Iturralde Elortegui, María del Rosario Margarita Michard, Nicole J. Paredes, Paula Umaña, Fernando Javier Alday Poblete, Silvina Esther Pezzola, Nestor Alejandro Vidal, Claudia Winschel, Cristina Ines Albarracin Franco, Silvia del Milagro Behr, Santiago Cianfagna, Francisco Andrés Cremona, Maria V. Alvarenga, Fernando Perucca, Ruth Lopez, Astor Miranda, Federico Kurtz, Ditmar Bernardo |
| author_role |
author |
| author2 |
Calamari, Noelia Cecilia Navarro, Carlos Saúl Enriquez, Andrea Soledad Mosciaro, María Jesús Saucedo, Griselda Isabel Barrios, Raul Curcio, Matías Hernán Dieta, Victorio José García Martínez, Guillermo Carlos Iturralde Elortegui, María del Rosario Margarita Michard, Nicole J. Paredes, Paula Umaña, Fernando Javier Alday Poblete, Silvina Esther Pezzola, Nestor Alejandro Vidal, Claudia Winschel, Cristina Ines Albarracin Franco, Silvia del Milagro Behr, Santiago Cianfagna, Francisco Andrés Cremona, Maria V. Alvarenga, Fernando Perucca, Ruth Lopez, Astor Miranda, Federico 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 |
Google earth engine Random forest Remote sensing Wetland dynamic Biophysical indices Probabilistic modeling Argentina |
| topic |
Google earth engine Random forest Remote sensing Wetland dynamic Biophysical indices Probabilistic modeling Argentina |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| 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, integrating 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) geomorphology 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 region-specific 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 dynamics 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. Fil: Navarro Rau, María F.. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Navarro, Carlos Saúl. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Reconquista.; Argentina Fil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Mosciaro, María Jesús. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta. Estación Experimental Agropecuaria Salta; Argentina Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina Fil: Barrios, Raul. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina Fil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Sur. Estación Experimental Agropecuaria Esquel; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Dieta, Victorio José. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: García Martínez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina Fil: Iturralde Elortegui, María del Rosario Margarita. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Michard, Nicole J.. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina Fil: Paredes, Paula. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; Argentina Fil: Umaña, Fernando Javier. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina Fil: Alday Poblete, Silvina Esther. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina Fil: Pezzola, Nestor Alejandro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Vidal, Claudia. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Santa Fe. Estacion Experimental Agropecuaria Reconquista. Agencia de Extension Rural Garabato.; Argentina Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Albarracin Franco, Silvia del Milagro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Misiones. Estación Experimental Agropecuaria Cerro Azul; Argentina Fil: Behr, Santiago. Instituto Nacional de Tecnologia Industrial. Centro Regional Chubut.; Argentina Fil: Cianfagna, Francisco Andrés. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cremona, Maria V.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; Argentina Fil: Alvarenga, Fernando. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Misiones. Estacion Experimental Agropecuaria Cerro Azul. Agencia de Extension Rural Apostoles; Argentina Fil: Perucca, Ruth. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; Argentina Fil: Lopez, Astor. Instituto Nacional de Tecnología Agropecuaria; Argentina. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Chaco-formosa. Estacion Experimental Agropecuaria Saenz Peña. Agencia de Extension Rural Saenz Peña.; Argentina Fil: Miranda, Federico. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Chaco-formosa; Argentina Fil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes; 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, integrating 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) geomorphology 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 region-specific 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 dynamics 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 |
2025-04 |
| 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/11336/284610 Navarro Rau, María F.; Calamari, Noelia Cecilia; Navarro, Carlos Saúl; Enriquez, Andrea Soledad; Mosciaro, María Jesús; et al.; Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring; KeAi Communications Co.; Watershed Ecology and the Environment; 7; 4-2025; 144-158 2589-4714 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/284610 |
| identifier_str_mv |
Navarro Rau, María F.; Calamari, Noelia Cecilia; Navarro, Carlos Saúl; Enriquez, Andrea Soledad; Mosciaro, María Jesús; et al.; Advancing wetland mapping in Argentina: A probabilistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring; KeAi Communications Co.; Watershed Ecology and the Environment; 7; 4-2025; 144-158 2589-4714 CONICET Digital CONICET |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2589471425000130 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.wsee.2025.04.001 |
| 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 |
| dc.publisher.none.fl_str_mv |
KeAi Communications Co. |
| publisher.none.fl_str_mv |
KeAi Communications Co. |
| 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 |
| _version_ |
1874774119057195008 |
| score |
13.24418 |