森林相对密度的地理空间估计为整个美国大陆的碳管理决策支持
Emmerson Chivhenge1,2, Aaron R Weiskittel3, Christopher W Woodall4
1University of Maine, School of Forest Resources, Orono, ME, USA. emmerson.chivhenge@maine.edu.
Scientific data
|November 1, 2025
概括
为美国大陆创建了一个新的森林树大小密度指标的空间数据集,包括树立密度指数 (SDI),最大SDI (SDI_MAX) 和相对密度 (RD). 该数据集通过提供数百万个森林像素的中等分辨率估计来增强森林管理和政策.
科学领域:
- 林业科学 林业科学
- 生态建模 生态建模
- 地理空间分析是什么?
背景情况:
- 美国森林服务局 (USFS) 的森林库存和分析 (FIA) 计划提供了关键的森林数据.
- 树木大小密度指标对于为森林管理政策和区域规划提供信息至关重要.
- 现有的数据可能缺乏详细分析所需的空间分辨率和一致性.
研究的目的:
- 在美国大陆 (CONUS) 开发一套关键森林树木大小密度指标的墙壁对墙空间数据集.
- 为了提高站立密度指数 (SDI),最大SDI (SDI_MAX) 和相对密度 (RD) 估计的分布和实用性.
- 为生态和管理应用提供全国一致的中分辨率数据集.
主要方法:
- 利用了54,925个FIA图片来为30m x 30m的形数据集的开发提供信息.
- 采用TREEMAP2016子作为在CONUS.中生成估计的基础.
- 计算了大约26.7亿个森林像素的立体密度指数 (SDI),最大SDI (SDI_MAX) 和相对密度 (RD).
主要成果:
- 在美国大陆地区为SDI,SDI_MAX和RD生成了全面的空间数据集.
- 确定了FIA基于图片的估计和新的TREEMAP光谱估计之间的关键差异.
- 将差异归因于空间分辨率的变化,方法学假设和时空因素.
结论:
- 新的中分辨率森林大小密度光谱提供了全国一致的数据.
- 这一数据集为量化树木竞争和生态系统脆弱性提供了独特的机会.
- 这些数据支持对碳捕获潜力的评估,并在各种规模上保持动态.
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