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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Understory trees in airborne LiDAR data - Selective mapping due to transmission losses and echo-triggering mechanisms
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Understory trees in airborne LiDAR data - Selective mapping due to transmission losses and echo-triggering mechanisms

机译：机载LiDAR数据中的地下树-由于传输损耗和回波触发机制而导致的选择性映射

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Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echo-triggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L.) stands (62°N, 24°E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10-15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete-return and waveform LiDAR data.
机译：在森林资源清查中，经常忽略多层林中的林下树木。有关它们的信息将有利于造林，木材采购和生物多样性管理。需要经济高效的库存方法，机载LiDAR是野外作业的有希望的补充。但是，由于传输损耗会影响回波触发的概率和强度（峰值幅度）的观测值，因此，上层建筑使用LiDAR阻碍了下层建筑的墙到墙采样。通过仔细的实验​​，我们研究了LiDAR在绘制松树（Pinus sylvestris L.）林分（62°N，24°E）的林下树木的地图中的潜力。我们为传输损失制定了一个概念模型，并说明了损失归一化的病态严重，特别是对于植被。损失使产生后续回波的目标群体倾斜。即使未触发过多的回声，也可能会发生高达10-15％的损失。在LiDAR传感器中，量化强度值从二进制零开始，但实际上应包括一个偏移量，即噪声水平。我们凭经验估计了这些。受限于低损耗脉冲和地面数据，我们基于雷达方程估算了补偿模型的参数，并采用了脉冲的几何形状以及过高的强度观测值作为预测因子。第二次返回数据的强度变化有所减少，但强度数据被认为在物种识别中价值较低。我们的结果突显了近地面回波触发和高度数据传感器之间的差异。来自林下层的基于区域的LiDAR高度度量与林下树木的密度和平均高度具有合理的相关性，而即使传输损失得到补偿，树木种类似乎也无法达到。我们得出结论，对于离散回波和波形LiDAR数据中的多回波脉冲进行辐射分析，传输损耗是一个普遍的障碍。

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