在2000-2021年期间从重新调整的MODIS季节性数据生成的全球年度部分树木覆盖数据集
Yang Liu1, Ronggao Liu2, Jilong Chen1,3
1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Scientific data
|August 1, 2024
概括
本研究提出了一个新的全球数据集 (GLOBMAP FTC) 关于2000-2021年每年分数树木覆盖的250米分辨率. 这一数据集通过减少树木覆盖率估计的不确定性来改善森林覆盖率变化分析.
科学领域:
- 地球观测 地球观测
- 林业科学 林业科学
- 地理空间分析是什么
背景情况:
- 从卫星数据中估计部分树木覆盖面是具有挑战性的,这导致了森林覆盖面变化分析的不确定性.
- 准确的森林密度和变化监测对于全球环境评估至关重要.
研究的目的:
- 从2000年到2021年以250米分辨率生成全球年度部分树木覆盖数据集 (GLOBMAP FTC).
- 通过提供可靠的树木覆盖数据集,减少森林覆盖变化分析中的不确定性.
主要方法:
- 利用MODIS年度观测,重新调整到一个共同的现象学.
- 提取了十二个特征来区分树木和草本植物,减少了维度.
- 训练了一个feedforward神经网络,使用高分辨率森林覆盖产品的4.65.88亿个样本点.
主要成果:
- 与验证数据集相比,获得了0.73的R2值,10.55%的MAE和17.98%的RMSE.
- 创建了一个全球年度分数树木覆盖数据集 (GLOBMAP FTC),用于2000-2021年,分辨率为250m.
结论:
- GLOBMAP FTC数据集为评估森林覆盖面变化提供了有价值的工具,包括突然损失和逐渐增长.
- 这一数据集可以显著提高全球森林监测和分析的准确性.
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