第一代区域规模的1米森林树冠覆盖数据集使用机器学习和谷歌地球引擎云计算平台:美国阿肯色州的一个案例研究
1University of Arkansas Division of Agriculture, Arkansas Forest Resources Center, University of Arkansas System, 110 University Court, Monticello, AR 71656, USA.
Data in brief
|January 31, 2024
概括
这项研究使用机器学习和谷歌地球引擎为阿肯色州创建了一个高分辨率的森林树冠覆盖数据集. 该数据集准确地绘制了森林覆盖面,并且与现有产品有很好的相关性,有助于森林管理.
科学领域:
- 林业和远程传感 林业和远程传感
- 生态系统监测 生态系统监测
- 地理空间分析的研究.
背景情况:
- 森林树冠覆盖 (FCC) 对生态系统服务和森林管理至关重要.
- 准确和高效的FCC绘图对于可靠的森林评估至关重要.
- 评估FCC提取的新技术是必要的.
研究的目的:
- 开发一个大规模的,1米分辨率的森林树冠覆盖 (FCC) 数据集.
- 评估阿肯色州FCC的区域空间分布.
- 为了比较新的FCC数据集与现有产品 (Hansen等人,2013年和USFS) 在县和城市层面.
主要方法:
- 利用高分辨率的空中图像和机器学习算法.
- 使用谷歌地球引擎云计算平台处理数据以实现可扩展性.
- 使用全球森林变化数据和NAIP图像验证了准确性.
主要成果:
- 产生了高精度的1米分辨率FCC数据集 (83.31%-94.35%每个县).
- 在新数据集和汉森等人,2013/USFS产品之间显示出强烈的正相关性 (R2 0.94-0.98).
- 证明了机器学习和云计算对大规模FCC映射的有效性.
结论:
- 开发的FCC数据集对阿肯色州的森林资源监测和管理有价值.
- 基于云的方法提供了效率,成本效益和可扩展性,用于生成高分辨率的森林覆盖数据.
- 这种方法可以应用于全球其他地区,以改进森林评估.
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