基于多时的Landsat数据和机器学习算法,分析了Alar修复区 (1990-2019) 的耕地变化和驱动因素
1School of Environment and Resource, Xichang University, Xichang, 615013, China.
Scientific reports
|December 6, 2025
概括
在中国的耕地.
科学领域:
- 遥感和地理空间分析
- 农业科学 农业科学
- 环境科学 环境科学
背景情况:
- 了解耕地动态对于粮食安全和土地管理至关重要.
- 干旱的绿洲地区为土地利用研究带来了独特的挑战.
研究的目的:
- 分析耕地面积的变化,并确定中国阿拉尔修复区的关键驱动因素.
- 为了比较六个机器学习算法的土地使用分类性能.
主要方法:
- 利用了Landsat卫星图像从1990年到2019年.
- 对比了六个机器学习算法,包括支持矢量机器条件随机场 (SVM-CRF).
- 使用的路径分析与社会经济和气象数据.
主要成果:
- SVM-CRF算法显示了最高的分类准确度 (整体准确度=0.95,卡帕=0.94).
- 1990年至2019年间,耕地扩大了729.97平方公里.
- 人口,GDP,投资,农业产量和棉花价格是重要的驱动因素,GDP显示负面影响.
结论:
- 在干旱地区,SVM-CRF模型是土地利用分类的最佳选择.
- 土地利用变化受到社会经济因素和农业政策的复杂相互作用的影响.
- 结合遥感和社会经济数据的综合方法对于分析土地利用机制是有效的.
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