用K-means对植被指数进行集群,用于绘制使用摩洛哥穆罕默德六世卫星高分辨率图像绘制的种植区的地图
Abdellatif Moussaid1, Mohamed Bayad2, Yousra Gamoussi3
1Center for Sustainable Soil Sciences (C3S), College of Agriculture and Environmental Sciences (CAES), University Mohammed VI Polytechnic (UM6P), 660 Lot, Ben Guerir, 43150, Morocco. abdellatif.moussaid-ext@um6p.ma.
Scientific reports
|February 25, 2026
概括
这项研究引入了使用卫星图像和K-means集群绘制耕地地图的无监督分类方法. 该方法准确地识别了作物面积,优于精准农业的其他方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 农业科学 农业科学
- 地理空间分析是什么
背景情况:
- 准确地绘制耕地地图对于农业管理和粮食安全至关重要.
- 高分辨率卫星图像为详细的现场规模土地覆盖面分析提供了潜力.
- 无监督的分类方法可以减少对广泛的基准真相数据的需求.
研究的目的:
- 开发和评估基于像素的无监督分类方法,用于绘制耕地地图.
- 将植被指数 (NDVI,MNDWI) 和近红外 (NIR) 频段的光谱特征与K-means集群集成.
- 评估方法的性能与传统分类技术相比.
主要方法:
- 使用基于像素的K-means集群算法.
- 纳入规范差异植被指数 (NDVI),修改规范差异水指数 (MNDWI) 和近红外 (NIR) 频段的光谱特征.
- 分类图像到背景,赤裸的土壤和农作物占主导地位的地区.
主要成果:
- 在绘制摩洛哥北部耕地地图时,实现了1.41%的低相对误差.
- 显著优于基于值 (7.2%),基于NDVI (6.95%) 和标准K-平均值 (5.47%) 的NIR分类.
- 证明了将植被指数与未经监督的集群用于农业绘图的有效性.
结论:
- 拟议的方法有效地利用高分辨率卫星数据和无监督集群绘制耕地地图.
- 这种方法支持精确灌,可持续土地管理和现场农业监测.
- 高分辨率卫星图像是详细农业应用的宝贵工具.
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