Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring
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 Argentin...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Elsevier
2025
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1855486978292711424 |
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| author | 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_browse | Albarracin Franco, Silvia Alday Poblete, Silvina Esther Alvarenga, Fernando Agustin Barrios, Raúl Ariel Behr, Santiago Javier Calamari, Noelia Cecilia Cianfagna, Francisco A. Cremona, Maria Victoria Curcio, Matías Hernán Dieta, Victorio Enriquez, Andrea Soledad Garcia Martinez, Guillermo Carlos Iturralde Elortegui, Maria Del Rosario Ma Kurtz, Ditmar Bernardo Lopez, Astor Emilio Michard, Nicole Jacqueline Miranda, Federico Waldemar Mosciaro, Maria Jesus Navarro, Carlos Saúl Navarro, María Fabiana Paredes, Paula Natalia Perucca, Alba Ruth Pezzola, Nestor Alejandro Saucedo, Griselda Isabel Umaña, Fernando Vidal, Claudia Winschel, Cristina Ines |
| 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_sort | Navarro, María Fabiana |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA22624 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA226242025-06-11T11:19:05Z Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring 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 Tierras Húmedas Ecosistema Sostenibilidad Teledetección Argentina Wetlands Ecosystems Sustainability Remote Sensing Humedales 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. 2025-06-11T10:41:53Z 2025-06-11T10:41:53Z 2025-04-04 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repograntAgreement/INTA/2019-PD-E2-I506-002, Humedales de la República Argentina: distribución, usos y recomendaciones coparticipativas para una producción sustentable info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Elsevier Watershed ecology and the Environment 7 : 144-158 (2025) |
| spellingShingle | Tierras Húmedas Ecosistema Sostenibilidad Teledetección Argentina Wetlands Ecosystems Sustainability Remote Sensing Humedales 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 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 |
| 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_short | 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 |
| topic | Tierras Húmedas Ecosistema Sostenibilidad Teledetección Argentina Wetlands Ecosystems Sustainability Remote Sensing Humedales |
| 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 |
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