基于空间LiDAR和地缘统计的自然森林树冠覆盖的空间效应分析
Jinge Yu1, Li Xu1, Qingtai Shu1
1College of Forestry, Southwest Forestry University, Kunming, China.
Frontiers in plant science
|July 22, 2024
概括
太空LiDAR数据和机器学习准确地预测了自然森林树冠覆盖 (NFCC),揭示了其空间模式和地形对森林结构的重大影响. 这种方法为区域分析提供了昂贵的手工调查提供了可靠的替代方案.
科学领域:
- 遥感 遥感 遥感 遥感
- 林业林业 林业 林业 林业
- 地理空间分析的研究.
背景情况:
- 手动森林调查是昂贵的,并且对区域范围的分析具有挑战性.
- 太空LiDAR提供了用于森林结构评估的全球采样能力.
- 对自然森林树冠覆盖 (NFCC) 的准确空间数据对于理解森林动态至关重要.
研究的目的:
- 开发一种使用空间LiDAR数据和机器学习来预测NFCC的方法.
- 分析NFCC的空间效应和分布模式.
- 调查地形因素对NFCC的影响.
主要方法:
- 使用了来自ICESat-2/ATLAS太空LiDAR数据的ATL08产品.
- 采用机器学习模型,包括随机森林 (RF),以使用80个基准真相图片预测LiDAR足迹位置的NFCC.
- 分析了使用莫兰的I和半变量图的空间相关性和变化,并评估了地形因素 (高度,斜率,方面) 的影响.
主要成果:
- 随机森林模型实现了高预测精度 (R2=0.75,RMSE=0.09).
- NFCC显示出正空间自相关性 (莫兰的I=0.36),表明集群分布.
- 地形因素显著影响了NFCC,高度是最有影响的,其次是斜率和角度.
结论:
- 太空LiDAR数据与机器学习相结合,为分析NFCC空间效应提供了可靠的数据源.
- 开发的射频模型有效地预测了NFCC,从而实现了大规模的空间分析.
- 了解对NFCC的空间模式和地形影响对于有效的森林管理和保护至关重要.
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