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

María F. Navarro Rau , Noelia C. Calamari , Carlos S. Navarro , Andrea Enriquez , María J. Mosciaro , Griselda Saucedo , Raul Barrios , Matías Curcio , Victorio Dieta , Guillermo García Martínez , María del R. Iturralde Elortegui , Nicole J. Michard , Paula Paredes , Fernando Umaña , Silvina Alday , Alejandro Pezzola , Claudia Vidal , Cristina Winschel , Silvia Albarracin Franco , Santiago Behr , Ditmar B. Kurtz
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Abstract

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.
推进阿根廷湿地制图:一种整合遥感、机器学习和云计算的概率方法,以实现可持续生态系统监测
湿地占地球表面的7%,对提供生态系统服务和调节气候变化至关重要。尽管它们很重要,但全球湿地分布的波动突出表明,需要准确和全面地绘制地图,以应对当前和未来的挑战。在阿根廷,缺乏关于湿地分布、范围和动态的详细知识阻碍了有效的保护和管理工作。本研究通过展示阿根廷的概率湿地分布图,将20年的卫星图像与机器学习和云计算技术相结合,解决了这些挑战。我们的方法引入了一套全面的生物物理指标,能够识别湿地的关键特征:1)永久或暂时的地表水存在;2)水适应植被物候;3)有利于水分积聚的地貌。我们的模型达到了89.3%的准确率,有效地识别了湿地区域,并描绘了揭示湿地时间行为的“弹性”带。阿根廷大约9.5%的土地被划分为湿地,其中查科-美索不达米亚地区占湿地面积的43%。42个评估变量的表现因宏观区域而异,突出了采用特定区域分类方法的必要性。在安第斯地区,数字高程模型(DEM)和地形湿度指数(TWI)等变量是关键的预测因子,而在平原地区,包括植被和含水量指数在内的光谱特性更为重要。尽管在灌区分类方面存在挑战,但该模型显示出相当强的稳健性。这项研究不仅增强了我们对湿地动态的理解,而且提供了不同区域如何响应各种环境因素的见解,为湿地行为提供了更细致入微的视角。这些发现为完善保护策略和进一步研究气候变化和土地利用对湿地生态系统的影响铺平了道路。湿地弹性的精度、可扩展性和代表性强调了其对决策的重要性,并为持续变化的环境下的未来研究提供了重要的基线。
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