频谱和地形特征的融合用于土地利用地图,使用机器学习框架进行区域规模的应用
J K S Sankalpa1, A M R W S D Rathnayaka2, P G N Ishani2
1Rubber Research Institute of Sri Lanka, Dartonfield, Agalawatta, 12200, Sri Lanka. ssankalpa2@gmail.com.
Environmental monitoring and assessment
|October 8, 2024
概括
这项研究比较了斯里兰卡的土地利用和土地覆盖地图的机器学习模型,发现随机森林是农业发展规划中最准确的.
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:
- 土地使用和土地覆盖 (LULC) 数据对于农业发展政策至关重要,但在斯里兰卡Kegalle地区等地区,时间数据集往往无法获得.
- 准确的LULC测绘对于农业规划和土地管理中的知情决策至关重要.
研究的目的:
- 模拟,绘制和评估斯里兰卡Kegalle地区的LULC动态,使用遥感数据和机器学习.
- 为了比较LULC分类的随机森林 (RF),支向量机 (SVM) 和分类和回归树 (CART) 的准确性.
- 分析2001-2013年和2013-2022年之间的LULC变化.
主要方法:
- 利用谷歌地球引擎 (GEE) 与远程传感数据集来映射LULC类别.
- 实现并比较了三个机器学习分类器:随机森林 (RF),支持矢量机器 (SVM) 和分类和回归树 (CART).
- 使用网格搜索交叉验证 (CV) 优化模型,并研究了各种输入特征组合.
主要成果:
- 随机森林 (RF) 分类器在LULC分类方面始终表现出比SVM和CART更高的准确性.
- 观察到土地覆盖面的重大变化,包括从2001年到2022年期间,和子种植园转化为建成和荒的土地.
- 该研究成功生成了适用于发展中国家土地管理的时间LULC数据集.
结论:
- 随机森林是LULC映射的可靠分类器,在研究区域中表现优于SVM和CART.
- 建议采用高空间分辨率的卫星数据和高级功能选择,以提高LULC分类的准确性.
- 衍生出来的时间LULC数据集为农业发展和土地管理策略提供了实际应用.
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