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首页> 外文期刊>Forests >Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA
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Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA

机译：通过美国俄勒冈州Panther Creek流域的多时相激光雷达采样和重新测量的田间库存数据预测产量和生物量变化

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摘要

Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass ( ? AGB ) and cubic volume including top and stump ( ? CVTS ) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate ? CVTS were not as accurate as the models employed to estimate ? AGB . Final models had cross-validation root mean squared errors as low as 40.90% for ? AGB and 54.36% for ? CVTS .
机译：使用激光雷达进行大规模森林管理可以改善运营和管理决策。使用多时相激光雷达采样和从美国俄勒冈州Panther Creek流域的78个样地收集的重新测量的现场清单数据，我们评估了不同固定模型和混合模型在估算地上生物量（？AGB）和立方体积（包括顶部）变化方面的性能。和五年内的树桩（？CVTS）。 CVTS和AGB的实际值是使用新拟合的体积和生物量方程式或森林清单和分析程序的西北太平洋单位使用的方程式获得的。基于固定和混合效应线性模型的变化估算比基于LIDAR估算的差异估算变化更准确。这可能是由于在两个时间段内基于LIDAR的估算中误差的混合造成的。但是，用于预测给定时间的体积和生物量的模型比用于预测变化的模型更为精确。用于估计的模型？ CVTS不如用来估算的模型那么准确？ AGB。最终模型的交叉验证均方根误差低至40.90％。 AGB和54.36％？ CVTS。

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