Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests
Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests
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DOI：
10.3390/rs6109576
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发表时间：
2014-10-01
期刊：
影响因子：
5
通讯作者：
Valentini, Riccardo
中科院分区：
文献类型：
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作者：
Pirotti, Francesco;Laurin, Gaia Vaglio;Valentini, Riccardo
我们在热带潮湿森林中测试了全波形 (FW) LiDAR（光探测和测距）指标作为森林断面积 (BA) 和地上生物量 (AGB) 的预测因子。测试了三个级别的指标：（i）峰值级别，基于每个返回回波； (ii) 脉冲电平，基于每个发射脉冲的整个返回信号； (iii) 绘图级，模拟大型 LiDAR 数据集。通过逐步回归发现，几个测试指标与两个预测变量具有显着相关性，特别是：离地高度的中值分布 (nZ(median)) 和总脉冲返回强度的第五个百分位数 (i_tot(5th))。前者包含最多的信息，解释了 AGB 和 BA 值的 58% 和 62% 的方差；逐步回归给我们留下了两个和四个预测变量，分别解释了 65% 和 79% 的方差。对于 BA，预测因子是总返回脉冲强度的标准差、中值和第五个百分位数（i_tot(stdDev)、i_tot(中值) 和 i_tot(5th)）以及 nZ(中值)，而对于 AGB，仅使用最后两个。基于绘图的指标显示，回波计数中值高度 (HOMTC) 表现最佳，结果与 nZ(中值) 非常相似，正如预期的那样。交叉验证允许分析残差和模型稳健性。我们讨论了我们的结果，考虑了复杂森林结构的具体情况，在生物量方面具有高度的可变性。
We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZ(median)) and fifth percentile of total pulse return intensity (i_tot(5th)). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_tot(stdDev), i_tot(median) and i_tot(5th)) and nZ(median), whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZ(median), as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass.