Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIR_struc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIR_struc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

中文翻译：

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利用多光谱机载激光扫描数据中的结构和强度指标对森林清单和多样性属性建模

调谐至近红外（NIR; 1064 nm）波长的机载激光扫描（ALS）系统已成为表征植物结构的最佳可用数据源。使用在两个其他波长（通常为532 nm；绿色和1550 nm；短波红外（SWIR））上调谐的激光对多光谱ALS（M-ALS）数据进行扩散，引起了人们对增加波长用于森林调查的兴趣造型。在这项研究中，基于结构和强度的M-ALS度量标准是独立于波长得出的，并结合使用它们来评估其对森林资源属性（Lorey的身高（HL），总体积（V）和基础面积（BA））和过高层建模的价值。加拿大安大略省一个混交林中的物种多样性（香农指数（H），辛普森指数（D）和物种丰富度（R））。使用了基于面积的森林属性建模方法（ABA），其中计算了基于结构和强度的度量，并将其用作随机森林模型的输入。来自SWIR渠道的结构指标（SWIR_STRUC）被认为是最准确的为H和R（％RMSE = 14.3和14.9），和NIR _STRUC对V最精确（％RMSE = 20.4）。强度度量的添加略微提高了SWIR和组合通道的HL模型的准确性（％RMSE = 7.5）。此外，还执行了多分辨率（0.5、1、2 m）体素分析，其中强度数据用于计算一组光谱指数。单独来自每个体素分辨率的光谱指数的图级摘要，以及与来自NIR波长的结构指标相结合，均被用作随机森林预测因子。对于具有HL，BA和V的所有模型，从NIR波段中添加结构度量降低了％RMSE，实现了最大的改进。在D和H的1 m和2 m体素模型中，强度度量是重要变量。总体而言，结果表明结构度量最合适。然而，包括强度指标，并继续测试其对多样性指数建模的潜力，这是有必要的，因为在进行了较小的改进之后。继续使用M-ALS强度指标和基于体素的指数进行分析将有助于更好地理解这些数据的价值及其在森林管理中的未来作用。