使用GEDI和光学数据在热带萨凡纳地区木质物种多样性的空间表征
Franciel Eduardo Rex1, Carlos Alberto Silva2, Eben North Broadbent3
1Department of Forestry Engineering, Federal University of Paraná-UFPR, Curitiba 80050-380, PR, Brazil.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究表明,全球生态系统动力学调查 (GEDI) 数据与卫星图像和气候数据相结合,如何有效地预测热带草原中的树木物种多样性. 这些遥感模型为监测生物多样性和为保护工作提供信息提供了有价值的工具.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 生物多样性监测 生物多样性监测
背景情况:
- 阻止全球生物多样性丧失需要强大的物种多样性监测能力.
- 遥感技术为生态评估提供了一种独特且可扩展的方法.
- 现有的评估热带草原树木物种多样性的方法存在局限性.
研究的目的:
- 评估全球生态系统动态调查 (GEDI) 数据与其他遥感和气候数据集成的潜力,以预测树木物种的alpha多样性.
- 开发和验证热带萨凡纳生态系统中物种丰富性,辛普森指数和香农指数的预测模型.
- 为了生成整个塞拉多地区树木物种多样性的空间显式地图.
主要方法:
- 在四个研究领域中利用了Sentinel-2光学图像,ERA-5气候数据,SRTM-DEM和模拟的GEDI数据.
- 采用随机森林 (RF) 回归模型,从遥感变量估计树木物种多样性指数.
- 集成辅助数据以提高生物多样性指标预测的准确性.
主要成果:
- 随机森林模型证明了从遥感变量来估计树种多样性的适用性,预测性能从R2 = 0.24到0.56不等.
- 在所有多样性模型中,叶片高度多样性 (FHD) 和重新规范差异植被指数 (RDVI) 一致地被选择.
- 香农多样性模型实现了最低的根平均平方误差百分比 (RMSE%),为31.98%,表明预测准确度很好.
结论:
- 开发的遥感模型是评估热带萨凡纳生态系统物种多样性的宝贵工具.
- 将GEDI数据与光学图像和气候数据集成,可以显著改善生物多样性预测.
- 模型的选择应基于具体的研究目标,所需的性能/错误水平以及数据的可用性.
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