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Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in aWestern Amazonian Forest Using Airborne LiDAR

机译:使用机载LiDAR的亚马逊西部森林中一般地上碳密度估算模型的空间显式测试

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摘要

Mapping aboveground carbon density in tropical forests can support CO2 emissionmonitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accuracy and generality of calibrations of LiDAR-based aboveground carbon density (ACD) predictions with those obtained from field inventory techniques should be intensified in order to advance tropical forest carbon mapping. Here we present results from the application of a general ACD estimation model applied with small-footprint LiDAR data and field-based estimates of a 50-ha forest plot in Ecuador?s Yasuní National Park. Subplots used for calibration and validation of the general LiDAR equation were selected based on analysis of topographic position and spatial distribution of aboveground carbon stocks. The results showed that stratification of plot locations based on topography can improve the calibration and application of ACD estimation using airborne LiDAR (R2 = 0.94, RMSE = 5.81 Mg?C? ha?1, BIAS = 0.59). These results strongly suggest that a general LiDAR-based approach can be used for mapping aboveground carbon stocks in western lowland Amazonian forests.
机译:在热带森林中绘制地上碳密度的图谱可支持监测CO2排放,并为国家资源管理提供好处。尽管已显示LiDAR技术可用于评估碳密度模式,但应加强基于LiDAR的地上碳密度(ACD)预测与从田间清查技术获得的预测值之间的校准的准确性和通用性,以推进热带森林碳测绘。在这里,我们介绍了从通用ACD估计模型的应用结果得出的结果,该模型适用于小面积LiDAR数据和厄瓜多尔Yasuní国家公园50公顷森林地的实地估计。基于地形位置和地上碳储量的空间分布分析,选择了用于校准和验证一般LiDAR方程的子图。结果表明,基于地形对地块位置进行分层可以改善机载LiDAR的ACD估计的校准和应用(R2 = 0.94,RMSE = 5.81 Mg?C3 ha?1,BIAS = 0.59)。这些结果强烈表明,可以使用基于LiDAR的通用方法来绘制西部低地亚马逊森林中的地上碳储量。

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