arXiv:2506.03120stat.APcs.LG2025-06被引 1

用森林普查数据验证商业遥感生物质估测,结果高度一致。

Validating remotely sensed biomass estimates with forest inventory data in the western US

  • 用美国林务局普查数据独立检验商业遥感产品
  • 六边形尺度下相关系数达0.94,均方误差26.68吨/公顷
  • 发现非林地和高生物量区存在系统偏差,适合碳监测应用

在高分辨率下监测地上生物量(AGB)及其密度(AGBD)对碳核算和生态系统管理至关重要。尽管美国宇航局的全球生态系统动态调查(GEDI)激光雷达任务提供了全球分布的参考测量数据,但基于GEDI的多数商业遥感产品仍缺乏严谨且独立的验证。本文基于美国林务局森林普查与分析(FIA)计划的独立参考数据,对terraPulse公司提供的AGBD数据集进行了区域性独立验证。在犹他州、内华达州和华盛顿州的64,000公顷六边形及县级尺度上,terraPulse与FIA估计值表现出极强一致性。在六边形尺度,决定系数R²为0.88,均方根误差(RMSE)为26.68兆克/公顷,相关系数r为0.94;在县级尺度,性能提升至R²=0.90,RMSE=32.62兆克/公顷,斜率=1.07,r=0.95。空间与统计分析表明,terraPulse在非林地估算值偏高,可能因FIA对非林植被采样不足;在高生物量森林中则偏低,可能源于其光学遥感变量的饱和效应。本研究为碳监测提供了可扩展的验证框架,并为全球生物量监测的新商业数据集建立了基准验证。

原文摘要 · Abstract (English)

Monitoring aboveground biomass (AGB) and its density (AGBD) at high resolution is essential for carbon accounting and ecosystem management. While NASA's spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR mission provides globally distributed reference measurements for AGBD estimation, the majority of commercial remote sensing products based on GEDI remain without rigorous or independent validation. Here, we present an independent regional validation of an AGBD dataset offered by terraPulse, Inc., based on independent reference data from the US Forest Service Forest Inventory and Analysis (FIA) program. Aggregated to 64,000-hectare hexagons and US counties across the US states of Utah, Nevada, and Washington, we found very strong agreement between terraPulse and FIA estimates. At the hexagon scale, we report R2 = 0.88, RMSE = 26.68 Mg/ha, and a correlation coefficient (r) of 0.94. At the county scale, agreement improves to R2 = 0.90, RMSE =32.62 Mg/ha, slope = 1.07, and r = 0.95. Spatial and statistical analyses indicated that terraPulse AGBD values tended to exceed FIA estimates in non-forest areas, likely due to FIA's limited sampling of non-forest vegetation. The terraPulse AGBD estimates also exhibited lower values in high-biomass forests, likely due to saturation effects in its optical remote-sensing covariates. This study advances operational carbon monitoring by delivering a scalable framework for comprehensive AGBD validation using independent FIA data, as well as a benchmark validation of a new commercial dataset for global biomass monitoring.

生物量监测遥感验证碳核算森林普查

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