在GRASS GIS中使用梯度增强算法进行图像分析的重新分类方案:东非吉布提的案例
1Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum-Università di Bologna, Via Irnerio 42, Emilia-Romagna, 40126 Bologna, Italy.
Journal of imaging
|August 27, 2025
概括
这项研究使用机器学习和卫星图像分析来绘制吉布提的土地覆盖变化. 结果显示,由于气候影响,荒漠化正在增加,这凸显了土地管理战略的必要性.
科学领域:
- 环境科学
- 遥感技术
- 地理信息系统
背景情况:
- 卫星数据的图像分析对于监测环境动态和土地管理至关重要.
- 了解景观变化是预测生态系统过程和地球系统功能的关键.
研究的目的:
- 通过使用多谱遥感数据和地理资源分析支持系统 (GRASS) 地理信息系统 (GIS) 软件开发和应用重新分类方案.
- 分析巴布-埃尔-曼德布海峡沿海地区的土地覆盖动态,重点关注阿萨尔湖周围的变化.
- 评估气候影响对吉布提的荒漠化过程的影响.
主要方法:
- 利用2015年,2019年,2021年和2023年的Landsat 8-9卫星图像进行时间序列分析.
- 使用机器学习 (ML) 算法和GRASS GIS模块 (例如"r.reclass"",r.learn.predict"",i.cluster") 来进行图像分类和重新分类.
- 集成了各种GRASS GIS模块和脚本技术,用于处理Landsat图像并识别土地覆盖变量.
主要成果:
- 这项研究揭示了阿萨尔湖周围景观的年度波动以及吉布提半干旱和沙漠土地覆盖的变化.
- 观察到半沙漠地区的增加和自然植被的减少,这表明沙漠化.
- 开发的土地覆盖地图为该地区的时空变化提供了宝贵的见解.
结论:
- 使用GRASS GIS提出的基于ML的方法对于分析土地覆盖的动态和变化是有效的.
- 这些发现突显了吉布提因气候影响而逐渐荒漠化.
- 这种方法可以应用于非洲其他干旱地区的土地管理策略.
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