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[结合多源远程传感数据和面向对象的信息提取干旱湿地]

Hong-Xia Li¹, Yun Shi¹, Zhong-Jie Ding²

¹School of Geographic Sciences and Planning, Ningxia University, Yinchuan 750021, China.

Huan jing ke xue= Huanjing kexue

|May 20, 2025

概括

这项研究表明,结合 Sentinel-1 雷达, Sentinel-2 红边图像和地形数据,可显著提高干旱地区湿地绘图的准确性. 随机森林模型与RF-Pearson特征选择提供了一种可靠的方法来提取重要的湿地信息.

科学领域:

遥感遥感遥感遥感
环境监测环境监测
地理空间分析是什么

背景情况:

湿地是干旱地区的重要生态系统,对生态稳定和资源管理至关重要.
准确的湿地信息提取对于监测环境变化,生物多样性和防止土地退化至关重要.

研究的目的:

探索红边,雷达和地形特征在干旱环境中的湿地开采的有效性.
为了验证RF-Pearson模型在湿地绘图中进行最佳特征选择.
评估用于湿地分类的随机森林和BP神经网络模型的性能.

主要方法:

使用 Sentinel-1 SAR, Sentinel-2 光学图像和地形数据.
应用面向对象的特征提取和RF-Pearson模型用于特征选择.
采用随机森林和BP神经网络算法来对宁夏阴川大都市区的湿地进行分类.

主要成果:

红边,雷达和地形特征提高了湿地识别的准确性.
在RF-皮尔森模型中,光谱,几何,红边,雷达和地形特征是最重要的.
随机森林模型实现了89.79%的整体准确性,超过了BP神经网络.

结论:

整合多来源数据和先进的算法可以改善干旱地区的湿地开采.
随机森林模型具有优化的特征,为湿地监测提供了可靠的方法.
这些发现支持黄河流域的生态保护和可持续发展.

关键词:

宁夏黄河流域城市聚集地在RF-皮尔森模型中,卫星"卫星"号的"卫星1号"号的图像卫星"卫星2号"的图像来自卫星"卫星2号".随机森林 (RF) 是一个随机的森林.在干旱地区的湿地.

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