使用Landsat图像进行阿根树林分类的比较方法方法
El Houcine El Moussaoui1, Aicha Moumni2, Saïd Khabba1,3
1LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco.
Environmental monitoring and assessment
|January 30, 2025
概括
这项研究利用1985年和2019年的遥感数据绘制了摩洛哥阿根树林的地图. 将数字海拔模型 (DEM) 与重新采样的正常化差异植被指数 (NDVI) 集成,实现了土地覆盖地图的最高准确性.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 摩洛哥的阿根树林面临着严重的环境和人为压力,需要有效的监测策略.
- 遥感技术为评估植被健康状况和土地覆盖面随时间变化的动态提供了宝贵的工具.
- 了解土地覆盖面的变化对于保护生态和经济重要阿根山景观至关重要.
研究的目的:
- 评估监督 (支持向量机,最大概率,最小距离) 和无监督 (Isodata) 的分类方法,用于树林的绘制地图.
- 评估重新采样技术和数字高度模型 (DEM) 集成对分类准确性的影响.
- 绘制1985年至2019年期间,埃萨乌伊拉省斯米莫地区阿根森林的土地覆盖面变化图.
主要方法:
- 使用了1985年和2019年的Landsat-5和Landsat-8卫星图像,用于Smimou地区.
- 对比监督分类算法 (SVM,ML,MD) 和不受监督的Isodata分类.
- 研究了重新采样方法对规范差异植被指数 (NDVI) 产品和集成的DEM数据的影响.
主要成果:
- 最大概率分类产生了高的整体准确度 (OA): 89.62% (1985) 和87.58% (2019).
- 重新采样的NDVI产品改善了OA,达到91.60% (1985) 和88.85% (2019).
- 将DEM与重新采样的NDVI集成实现了最高的OA:92.27% (1985) 和92.37% (2019),证明了组合数据的好处.
结论:
- 最大可能性分类器是有效的阿根树林绘制地图.
- 重新采样技术和DEM集成显著提高了监测树林动态的分类准确性.
- 这项研究提供了一种可靠的方法,用于使用遥感来跟踪摩洛哥重要的阿根山景观的变化.
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