In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual flight lines and overlapping regions of adjacent flight lines of a multispectral LiDAR dataset. We tested the effectiveness of these corrections in various land/forest cover types in a temperate mixed mature forest in Ontario, Canada. Subsequently, we predicted three plot level forest attributes, i.e., basal area (BA), quadratic mean diameter (QMD), and trees per hectare (TPH), using different combinations of height and intensity metrics derived from the multispectral LiDAR data to determine if LiDAR intensity data (corrected and uncorrected) improved predictions over models that utilize LiDAR height-derived information only. The results show that LSLC can reduce the intensity banding effect by 0.19–23.06% in channel 1 (1550 nm) and 4.79–66.87% in channel 2 (1064 nm) at the close-to-nadir region. The combined effect of LSLC and OIC is notable particularly at the swath edges. After implementing both methods, the intensity homogeneity is improved by 5.51–12% in channel 1, 6.37–42.93% in channel 2, and 6.48–33.77% in channel 3 (532 nm). Our results further demonstrate that BA and QMD predictions in our study area gained little from additional LiDAR intensity metrics. Intensity metrics from multiple LiDAR channels and intensity normalized difference vegetation index (NDVI) metrics did improve TPH predictions up to 7.2% in RMSE and 1.8% in Bias. However, our lowest TPH prediction errors (%RMSE) were still approximately 10% larger than for BA and QMD. We observed only minimal differences in plot level BA, QMD, and TPH predictions between models using original and corrected intensity. We attribute this to: (i) the lower effectiveness of radiometric correction in forest versus grassland, bare soil and road land cover types, and (ii) the effect of spatial resolution on intensity noise.

为了将机载LiDAR强度与高度信息结合使用，以进行森林建模和分类，辐射度校正被视为一项关键的预处理要求。在这项研究中，我们实施了LiDAR扫描线校正（LSLC）和重叠驱动强度校正（OIC），以消除出现在多光谱LiDAR数据集的单个飞行线和相邻飞行线重叠区域中的条纹伪影。我们在加拿大安大略省的一个温带混合成熟林中的各种土地/森林覆盖类型中测试了这些校正的有效性。随后，我们预测了三个样地级森林属性，即基础面积（BA），二次平均直径（QMD）和每公顷树木（TPH），使用从多光谱LiDAR数据得出的高度和强度指标的不同组合来确定LiDAR强度数据（校正和未校正）是否比仅使用LiDAR高度信息的模型改善了预测。结果表明，在接近最低点的区域，LSLC可以在通道1（1550 nm）处降低强度谱带效应，在通道2（1064 nm）处降低强度谱带效应4.79–66.87％。LSLC和OIC的组合效果尤其明显，在条带边缘。实施这两种方法后，通道1的强度均匀性提高了5.51–12％，通道2的提高了6.37–42.93％，通道3的增强了（6.4nm–33.77％）（532 nm）。我们的结果进一步表明，我们的研究领域中的BA和QMD预测没有从其他LiDAR强度指标中获得多少。来自多个LiDAR通道的强度指标和强度归一化差异植被指数（NDVI）指标确实将TPH预测的RMSE和Bias的预测提高了7.2％和1.8％。但是，我们的最低TPH预测误差（％RMSE）仍比BA和QMD大约10％。我们使用原始强度和校正强度仅在模型之间观察到地块水平BA，QMD和TPH预测方面的差异很小。我们将其归因于：（i）森林与草地，裸露的土壤和道路土地覆盖类型之间的辐射校正的有效性较低，以及（ii）空间分辨率对强度噪声的影响。我们使用原始强度和校正强度仅在模型之间观察到地块水平BA，QMD和TPH预测方面的差异很小。我们将其归因于：（i）森林与草地，裸露的土壤和道路土地覆盖类型之间的辐射校正的有效性较低，以及（ii）空间分辨率对强度噪声的影响。我们使用原始强度和校正强度仅在模型之间观察到地块水平BA，QMD和TPH预测方面的差异很小。我们将其归因于：（i）森林与草地，裸露的土壤和道路土地覆盖类型之间的辐射校正的有效性较低，以及（ii）空间分辨率对强度噪声的影响。