Alciaturi Giancarlo, Wdowinski Shimon, García-Rodríguez María Del Pilar, Fernández Virginia
Programa de Doctorado en Geografía, Facultad de Geografía e Historia, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Institute of Environment, Department of Earth and Environment, Florida International University, Miami, FL 33199, USA.
Sensors (Basel). 2025 Jan 3;25(1):228. doi: 10.3390/s25010228.
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country's economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix.
地球观测传感器的最新进展、图像获取的改善以及相应处理工具的开发，极大地增强了研究人员从多源遥感中提取见解的能力。本研究旨在利用这些技术绘制乌拉圭梅里尼亚克恩卡德拉拉古纳的夏季和冬季土地利用/土地覆盖特征图，同时比较随机森林、支持向量机和梯度提升树分类器的性能。材料包括哨兵 - 2、哨兵 - 1 和航天飞机雷达地形测绘任务图像、谷歌地球引擎、训练和验证数据集以及引用的分类器。方法包括创建多源数据库、进行特征重要性分析、开发模型、监督分类和进行精度评估。结果表明，相对于光学特征，微波输入的重要性较低。短波红外波段以及诸如归一化植被指数、陆地表面水指数和增强植被指数等变换显示出最高的重要性。精度评估表明，在绘制各类地图方面性能最佳，特别是对于稻田，稻田在该国经济中起着至关重要的作用，并突出了重大的环境问题。然而，在减少类别之间的混淆方面仍然存在挑战，特别是在区分自然植被特征与季节性淹没植被，以及区分农业后田地/裸地和草本区域方面。与支持向量机相比，随机森林和梯度提升树表现出更优的性能。未来的研究应探索深度学习以及基于像素和基于对象的分类集成等方法，以应对已识别的挑战。这些举措应考虑各种数据组合，包括从灰度共生矩阵导出的额外指数和纹理度量。
