基于时空机器学习的全球草原类别和范围 (2000-2022) 的每年30万张地图
Leandro Parente1, Lindsey Sloat2, Vinicius Mesquita3
1OpenGeoHub Foundation, Doorwerth, The Netherlands. leandro.parente@opengeohub.org.
Scientific data
|December 11, 2024
概括
本研究介绍了2000-2022年全球草原的年度地图,分辨率为30米. 该数据集区分了耕地和自然草原,有助于土地利用变化分析.
科学领域:
- 地球观测 地球观测
- 遥感 遥感 遥感 遥感
- 地理空间科学 地理空间科学
背景情况:
- 准确的全球草原测绘对于了解土地使用动态和生态变化至关重要.
- 现有的数据集往往缺乏详细分析所需的空间和时间分辨率.
研究的目的:
- 为2000-2022年期间制作和评估一个高分辨率 (30米) 的全球草原面积数据集.
- 提供一个时空分布图,区分耕种和自然/半自然草原.
主要方法:
- 使用了GLAD Landsat ARD-2图像档案,其中包含气候,地形和近距离的共变量.
- 使用的时空机器学习 (随机森林) 拥有超过230万个参考样本.
- 使用自定义的概率值和空间交叉验证来导出主导类地图.
主要成果:
- 在2000-2022年以30米的空间分辨率生成年度全球草原地图.
- 在耕草地获得F1评分为0.64,在自然/半自然草地获得0.75.
- 该数据集可作为云优化的GeoTIFF和谷歌地球引擎资产.
结论:
- 开发的草原数据集为全球土地覆盖面的监测提供了宝贵的资源.
- 促进与其他土地覆盖产品的整合,并跟踪草原转型动态.
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