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Abstract：
Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth\'s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).
摘要：
准确绘制和监测热带森林地上生物量(AGB)对于设计有效的碳减排策略和提高我们对地球碳循环的认识至关重要。然而,通过结合地球观测（EO）和森林清单数据生成的现有热带森林AGB大比例图显示出明显不同的估计，即使在考虑了报告的不确定性之后。为了解决这个问题，需要高质量的参考数据网络来校准和验证映射算法。本研究旨在使用中部非洲八个地点和南亚五个地点的现场库存图和机载LiDAR数据生成参考AGB数据集，在全球参考AGB数据集中，两个区域的代表性严重不足。这项研究提供了对这些参考AGB地图的访问，包括不确定性地图，在100m和40m的空间分辨率下，LiDAR总占地面积为1,11,650公顷[在现场级别为150至40,000公顷]。这些地图用作校准/验证数据集，以提高当前和即将进行的EO任务的AGB映射的准确性和可靠性（即,GEDI,BIOMASS,和NISAR）。
